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The SoundBite® Crossing System XS Peripheral is indicated to facilitate the intraluminal placement of conventional guidewires or treatment devices beyond peripheral artery chronic total occlusions.

The SoundBite® Crossing System XS Peripheral is not intended for use in the carotid arteries.

The SoundBite® Crossing System XS Peripheral, is a recanalization device designed to help physicians place a conventional guidewire or treatment device in the intraluminal space beyond peripheral chronic total occlusions (CTOs). It is intended for use in a professional healthcare facility, such as a catheterization laboratory, also known as a Cath Lab. The system consists of the AC-powered, reusable, mobile SoundBite® Console XS with a Foot Switch, and a single-use, sterile SoundBite® Active Wire XS 14P packaged together with two accessories, the SoundBite® Curving Tool 14 and the SoundBite® Torquer.

The AC-powered, reusable, mobile SoundBite® Console XS is an iteration of the predicate device, re-engineered for improved usability but with similar design, functional, safety, and performance specifications. It generates controlled, high-amplitude, short-duration mechanical pulses (i.e., shock waves) that are transmitted to the connected SoundBite® Active Wire XS 14P and cause the distal end of the wire to move back and forth (axially), acting like a micro-jackhammer.

The single-use SoundBite® Active Wire XS 14P is a 350 cm long metallic wire with an outer diameter of 0.36 mm (0.014") over its usable length. It is similar in construction to the Active Wire of the predicate and reference devices, but 50 cm longer. The proximal end of the SoundBite® Active Wire XS 14P includes a connector assembly that encapsulates a wire section reducer for easy connection to the SoundBite® Console XS, and a Radio Frequency ldentification (RFID) tag that allows the SoundBite® Console XS to detect and uniquely identify the wire and track its use. A thermoplastic elastomer sleeve was also added to proximal length of the wire during use.

I am sorry, but the provided text focuses on the regulatory clearance of a medical device (SoundBite® Crossing System XS Peripheral) and its substantial equivalence to a predicate device. It describes the device, its intended use, and a comparison of its characteristics with the predicate.

The document includes a "Summary of Non-Clinical Testing" section that lists various tests performed to assess the safety and performance of the device. However, this section does not provide specific acceptance criteria or the reported device performance in a quantitative manner that would allow for the creation of the requested table. It also does not delve into the details of study design, sample sizes for test sets, data provenance, expert qualifications for ground truth, adjudication methods, MRMC studies, standalone performance, or training set details.

Therefore, I cannot fulfill your request for:

1. A table of acceptance criteria and the reported device performance. While tests are listed, the specific criteria and performance values are not provided.
2. Sample size used for the test set and data provenance.
3. Number of experts used to establish the ground truth for the test set and their qualifications.
4. Adjudication method.
5. Whether a MRMC comparative effectiveness study was done, or its effect size.
6. Whether a standalone performance study was done.
7. The type of ground truth used.
8. The sample size for the training set.
9. How the ground truth for the training set was established.

The document confirms that non-clinical testing was conducted, but the specifics required to answer your questions are not present in the provided text.

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The SOMAVAC® 100 Sustained Vacuum System (SOMAVAC® 100) is a portable battery powered vacuum source / waste container intended for the removal of surgical and bodily fluids from a closed wound following plastic surgery and other general surgery forming large flaps for hematoma and seroma prophylaxis. It is intended for use in homecare and healthcare environments.

The SOMAVAC® 100 Sustained Vacuum System (SOMAVAC® 100) is a portable, batterypowered vacuum pump/waste container intended for the removal of surgical and bodily fluids from a closed wound following plastic surgery and other general surgery forming large flaps for hematoma and seroma prophylaxis. It is intended for homecare and healthcare environments. The SOMAVAC® 100 may be used for most instances a surgeon determines a closed suction drain device is applicable. The SOMAVAC® 100 is compatible with drains commonly used after surgeries and is intended to be used by a single patient. The SOMAVAC® 100 is to be installed by trained medical personnel. Post-installation (in patient setting or at home), the SOMAVAC® 100 is intended to be operated by patients or their caregivers.

The provided text is related to the FDA 510(k) clearance for the SOMAVAC® 100 Sustained Vacuum System. This document primarily focuses on demonstrating substantial equivalence to a predicate device rather than presenting a study of the device's clinical performance against specific acceptance criteria for a novel AI or diagnostic system.

Therefore, many of the requested categories in your prompt cannot be fully addressed from the provided text. The document describes changes to a previously cleared device (SOMAVAC® 100, K222856) specifically regarding an additional sterilization method (ethylene oxide) for its drain connector accessories. The performance testing section focuses on validating this new sterilization method and its impact on packaging.

Here's an attempt to answer your questions based on the available information:

1. Table of Acceptance Criteria and Reported Device Performance

The acceptance criteria and reported device performance are not presented in a table format for a clinical outcome or diagnostic accuracy in this document. Instead, the "performance testing" focuses on validating the sterilization method and its effects on packaging integrity, referencing existing standards. The acceptance criteria essentially reflect compliance with these standards.

2. Sample size used for the test set and the data provenance

The document does not specify a "test set" in the context of patient data or algorithm performance. The testing mentioned relates to the physical and chemical properties associated with the new sterilization method and packaging. Therefore, no information on sample size for a "test set" for device performance (e.g., fluid removal efficiency in patients) or data provenance (country, retrospective/prospective) is available.

3. Number of experts used to establish the ground truth for the test set and the qualifications of those experts

Not applicable. The document does not describe a test set requiring expert-established ground truth for clinical or diagnostic performance. The validation of sterilization and packaging integrity would typically involve laboratory testing by qualified personnel, but not "experts" in the sense of clinicians establishing ground truth from patient data.

4. Adjudication method for the test set

Not applicable. There is no mention of an adjudication method as the testing concerns physical and chemical properties of the device components/packaging, not clinical or diagnostic outcomes requiring expert consensus.

5. If a multi reader multi case (MRMC) comparative effectiveness study was done, If so, what was the effect size of how much human readers improve with AI vs without AI assistance

Not applicable. This device is a powered suction pump, not an AI or diagnostic tool that would involve human readers or image interpretation.

6. If a standalone (i.e. algorithm only without human-in-the-loop performance) was done

Not applicable. This device is a physical medical device (suction pump), not an algorithm or AI system.

7. The type of ground truth used (expert consensus, pathology, outcomes data, etc)

For the performance testing related to the new sterilization method, the "ground truth" is established by adherence to recognized international standards (ISO 11135, ISO 10993-7, ASTM F88/F88M, ASTM 1886). These standards define acceptable parameters for sterilization efficacy, residuals, and packaging integrity.

8. The sample size for the training set

Not applicable. This device does not involve a "training set" in the context of machine learning or AI.

9. How the ground truth for the training set was established

Not applicable. As above, there is no training set for this device.

Summary of Device Performance (from the document):

The SOMAVAC® 100 Sustained Vacuum System, in this submission (K231063), is essentially the same device as its predicate (K222856). The only change covered by this 510(k) is the addition of Ethylene Oxide (EO) as a sterilization method for the drain connector accessories, alongside the existing gamma radiation method.

The study presented here focuses on validating this new sterilization method and confirming that it does not negatively impact the device's safety or effectiveness. This was achieved by demonstrating compliance with ISO standards for EO sterilization and biological evaluation, and by conducting packaging evaluations (seal strength and integrity) post-sterilization, which all "Passed". The conclusion is that the device remains substantially equivalent to the predicate.

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The SOMAVAC® 100 Sustained Vacuum System (SOMAVAC® 100) is a portable battery powered vacuum source / waste container intended for the removal of surgical and bodily fluids from a closed wound following plastic surgery and other general surgery forming large flaps for hematoma and seroma prophylaxis. It is intended for use in homecare and healthcare environments.

The SOMAVAC® 100 Sustained Vacuum System (SOMAVAC® 100) is a portable battery-powered vacuum pump/waste container intended for the removal of surgical and bodily fluids from a closed wound following plastic surgery and other general surgery forming large flaps for hematoma and seroma prophylaxis. It is intended for homecare and healthcare environments. The SOMAVAC® 100 may be used for most instances a surgeon determines a closed suction drain device is applicable. The SOMAVAC® 100 is compatible with drains commonly used after surgeries and is intended to be used by a single patient. The SOMAVAC® 100 is to be installed by trained medical personnel. Post-installation (in patient setting or at home), the SOMAVAC® 100 is intended to be operated by patients or their caregivers.

This document details a 510(k) premarket notification for the SOMAVAC® 100 Sustained Vacuum System. It establishes substantial equivalence to a predicate device, K180606.

Here’s an breakdown of the acceptance criteria and study information provided:

Acceptance Criteria and Reported Device Performance

The provided document does not explicitly present a table of "acceptance criteria" against which a clinical performance study would be evaluated in terms of sensitivity, specificity, accuracy, or similar metrics. Instead, the document focuses on demonstrating substantial equivalence to a predicate device (SOMAVAC® Device, K180606) through non-clinical performance testing and direct comparison of specifications.

The "performance" described refers to the device's functional characteristics and compliance with standards, rather than diagnostic or treatment efficacy from a clinical study. The table below summarizes the key comparisons made to demonstrate substantial equivalence, which serves as the "acceptance criteria" for this type of regulatory submission in the absence of a new clinical claim.

Study Information

The document describes non-clinical testing rather than a traditional “study” with patient data.

1. Sample size used for the test set and the data provenance: Not applicable. This submission relies on non-clinical performance verification and validation. The "test set" would refer to the physical devices and components tested. The provenance is not explicitly stated in terms of country of origin of "data," but it implicitly comes from the manufacturer's internal testing. The testing is prospective in the sense that the new device was built and then tested.

2. Number of experts used to establish the ground truth for the test set and the qualifications of those experts: Not applicable. For non-clinical performance testing of a physical device like this, "ground truth" is established by engineering specifications, validated measurement equipment, and industry standards (e.g., electrical safety, EMC). Expert review would be part of the design and verification processes but not as a formal "ground truth" for a test set in the clinical sense.

3. Adjudication method (e.g. 2+1, 3+1, none) for the test set: Not applicable. This type of adjudication is typically used in clinical studies for interpretation of imaging or clinical outcomes, not for engineering performance testing.

4. If a multi reader multi case (MRMC) comparative effectiveness study was done, If so, what was the effect size of how much human readers improve with AI vs without AI assistance: Not applicable. This device is a powered suction pump, not an AI-assisted diagnostic or therapeutic tool that would involve human readers or cases in an MRMC study.

5. If a standalone (i.e. algorithm only without human-in-the-loop performance) was done: Not applicable. This device is a hardware product. While it contains software, the performance listed is for the complete system.

6. The type of ground truth used (expert consensus, pathology, outcomes data, etc.): The "ground truth" for the non-clinical testing is based on:

   • Engineering specifications: The defined parameters for vacuum, flow, electrical characteristics, and physical properties.
   • Regulatory standards: Compliance with voluntary standards for electrical safety (e.g., IEC 60601-1), electromagnetic compatibility (e.g., IEC 60601-1-2), and powered suction pumps (e.g., ISO 10079-1).
   • Previous device performance: The performance of the predicate device (K180606) served as a benchmark for equivalence.

7. The sample size for the training set: Not applicable. This device is not an AI/ML algorithm that requires a training set. The software changes were validated in accordance with FDA guidances for software in medical devices, but this is distinct from training an AI model.

8. How the ground truth for the training set was established: Not applicable. (See answer to point 7).

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The monitors are intended to be used for monitoring, storing, and to generate alarms for, multiple physiological parameters of adults and pediatrics. The monitors are intended for use by trained healthcare professionals in hospital environments.

The LM-8 monitor monitors parameters such as ECG (3-lead, 12-lead selectable), Respiration (RESP), Functional arterial oxygen saturation (SpO2), Invasive or noninvasive blood pressure (dual-IBP, NIBP), Temperature (dual-TEMP), Expired CO2 and Quick Temperature (Quick TEMP).

The LM-10 monitor monitors parameters such as ECG (3-lead, 12-lead selectable), Respiration (RESP), Functional arterial oxygen saturation (SpO2), Invasive or noninvasive blood pressure (2/4 channels IBP, NIBP), Cardiac Output (C.O.), Temperature (dual-TEMP) and Expired CO2.

The LM-12 monitor monitors parameters such as ECG (3-lead, 12-lead selectable), Respiration (RESP), Functional arterial oxygen saturation (SpO2), Invasive or noninvasive blood pressure (2/4 chamels IBP, NIBP), Cardiac Output (C.O.), Temperature (dual-TEMP), Expired CO2 and Anesthetic gas (AG).

The LM-15 monitors parameters such as ECG (3-lead, 5-lead, 12-lead selectable), Respiration (RESP), Functional arterial oxygen saturation (SpO2), Invasive or noninvasive blood pressure (2/4 channels IBP, NIBP), Cardiac Output (C.O.), Temperature (dual-TEMP), Expired CO2 and Anesthetic gas (AG).

The arrhythmia detection and ST Segment analysis are intended for adult and pediatric patients.

The monitors are not intended for MRI environments.

LM-8, LM-10, LM-12 and LM-15, patient monitor integrates parameter measuring modules, display and recorder in one device, featuring in compactness, lightweight and portability. Replaceable built-in battery facilitates patient transport. Large high-resolution display provides clear view of 10 waveforms and full monitoring parameters. Patient Monitor can monitor vital signal such as ECG, respiration (RESP), non-invasive blood pressure (NIBP), oxygen saturation of the blood (SpO2), temperature (TEMP), invasive blood pressure (IBP), cardiac output (C.O.), CO2 and anesthetic gas (AG). Those signals are digitized, processed and examined for alarm conditions, after that presents all those information on the color TFT display. The monitor also provides advantageous operating control for the user.

The provided text is an FDA 510(k) summary for a Patient Monitor (models LM-8, LM-10, LM-12, LM-15). It primarily focuses on demonstrating substantial equivalence to predicate devices through technical comparisons and compliance with general performance standards.

However, the document does not contain the detailed information necessary to answer all aspects of your request regarding acceptance criteria and a study proving the device meets those criteria in the context of an AI/algorithm-driven medical device performance study.

Specifically, the document lacks:

• A explicit table of acceptance criteria for algorithm performance (e.g., sensitivity, specificity, F1-score for arrhythmia detection).
• Detailed results of a study demonstrating the device meets specific performance criteria for arrhythmia detection or ST-segment analysis (beyond basic functional checks).
• Information on sample size for test sets directly related to algorithm performance (as opposed to overall device safety/functionality).
• Data provenance, number of experts for ground truth, adjudication methods, or MRMC studies, which are typical for AI/ML device evaluations.
• Training set details for any AI/ML components.

The "Performance data" section refers to "Clinical data" for validation, but these appear to be general functional validation tests on physiological parameters (ECG, RESP, SpO2, NIBP, etc.) to ensure the monitors function as intended, rather than a specific study to validate the performance of the arrhythmia detection and ST Segment analysis algorithm against clinical ground truth. The statement "The clinical data demonstrate that the subject devices perform comparably to the predicate device that is currently marketed for the same intended use" is a high-level conclusion without supporting details beyond the comparative features table.

Based on the provided text, here's what can be extracted and what is missing:

1. A table of acceptance criteria and the reported device performance:

The document doesn't provide a specific table of quantitative acceptance criteria for the arrhythmia detection and ST segment analysis algorithm (e.g., sensitivity, specificity thresholds) and corresponding reported performance metrics. It lists general parameters and their measurement ranges, which are functional specifications, not performance criteria for an arrhythmia detection algorithm.

General device functional specifications (from comparison table, not acceptance criteria for algorithm):

The document notes that "The arrhythmia detection and ST Segment analysis are intended for adult and pediatric patients" and that "Clinical tests were performed on the LM-8, LM-12 and LM-15 monitors to validate their performance in terms of ECG...". However, it does not specify what constituted "validation" for these particular algorithmic features or what the performance metrics were. The "Conclusion" states: "The clinical data demonstrate that the subject devices perform comparably to the predicate device that is currently marketed for the same intended use." This is the reported device performance for these features: "comparable to predicate."

2. Sample sized used for the test set and the data provenance:

• Sample Size for Test Set: Not specified for the "Clinical data" related to ECG/arrhythmia/ST validation. The statement is general: "Clinical tests were performed on the LM-8, LM-12 and LM-15 monitors..."
• Data Provenance: Not specified (e.g., country of origin, retrospective or prospective).

3. Number of experts used to establish the ground truth for the test set and the qualifications of those experts (e.g. radiologist with 10 years of experience):

• Not specified. The document does not describe how ground truth for arrhythmia or ST segment analysis was established for clinical testing.

4. Adjudication method (e.g. 2+1, 3+1, none) for the test set:

• Not specified.

5. If a multi reader multi case (MRMC) comparative effectiveness study was done, If so, what was the effect size of how much human readers improve with AI vs without AI assistance:

• No, an MRMC study is not mentioned. This device is a monitor, not an AI-assisted diagnostic tool for interpretation by a human reader in the typical sense of an MRMC study for imaging. It provides "arrhythmia detection and ST Segment analysis" algorithms directly to the user.

6. If a standalone (i.e. algorithm only without human-in-the-loop performance) was done:

• The "Clinical data" statement indicates the monitors' performance was validated. Given the nature of a patient monitor, the arrhythmia and ST segment analysis would inherently be "standalone" algorithmic functions integrated into the device, providing automated analysis. However, specific performance metrics (like sensitivity/specificity of the algorithm itself) from this standalone evaluation are not presented.

7. The type of ground truth used (expert consensus, pathology, outcomes data, etc):

• Not specified. It is generally implied that such devices are validated against accepted physiological measurement standards and potentially manually confirmed ECG interpretations, but the document does not detail this for the arrhythmia/ST segment analysis.

8. The sample size for the training set:

• Not applicable/Not specified. The document does not indicate that the arrhythmia detection or ST segment analysis algorithms utilize machine learning or require a "training set" in the sense of AI/ML development. It's likely these are based on established rule-based or signal processing algorithms, not learned from data.

9. How the ground truth for the training set was established:

• Not applicable. (See #8)

In summary, this 510(k) submission successfully demonstrates substantial equivalence through technical specifications, comparisons to predicate devices, and compliance with general safety and performance standards (e.g., electrical safety, EMC, biocompatibility, software verification/validation). However, it does not detail a specific performance study for its arrhythmia detection and ST segment analysis algorithms in a way that typically applies to AI/ML clearance, which would include explicit acceptance criteria, detailed test set characteristics, and ground truth methodologies. The "clinical data" section is very high-level and only states comparability to predicate devices.

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The monitors are intended to be used for monitoring, storing, and to generate alarms for, multiple physiological parameters of adults, pediatrics and neonates. The monitors are intended for use by trained healthcare professionals in hospital environments.

The monitored physiological parameters include: ECG, respiration (RESP), temperature (TEMP), oxygen saturation of arterial blood (SpO2), pulse rate (PR), non-invasive blood pressure (NIBP), invasive blood pressure (IBP), carbon dioxide (CO2), cardiac output (C.O.), anesthetic gas (AG), bispectral index (BIS), respiration mechanice cardiography (ICG).

BIS is intended for use on adult and pediatric patients.

ICG monitoring is intended for use on adults only.

The arrhythmia detection and ST Segment analysis are intended for adult patients.

The monitors are additionally intended for use during patient transport inside hospitals.

The monitors are not intended for MRI environments.

LMPLUS series Patient Monitor including LMPLUS-12, LMPLUS-15 and LMPLUS-17 which can perform long-time continuous monitoring of multiple physiological parameters. Also, it is capable of storing, displaying, analyzing and controlling measurements, and it will indicate alarms in case of abnormity so that doctors and nurses can deal with them in time.

The LMPLUS series Patient Monitor realize the monitoring of physiological parameters by configuration with different parameter modules which include SpO2 (pulse oxygen saturation, pulse rate and SpO2 plethysmogram) with EDAN SpO2 module or Nellcor SPO2 module, NIBP (systolic pressure, diastolic pressure and pulse rate), TEMP, ECG, RESP (respiration), CO2, IBP, C.O. and AG (anesthetic gas), RM (respiratory mechanics), BIS (bispectral index) and ICG (impedance cardiography).

The above is the maximum configuration for LMPLUS series Patient Monitor, the user may select different monitoring parameters in according with their requirements.

LMPLUS-12 configures with 12.1-inch color TFT touch screen, LMPLUS-15 and LMPLUS-17 with same screen except different sizes 15-inch and 17-inch separately. Three models are all build-in Lithium-ion battery, support software upgrade online and networking.

The provided document focuses on the 510(k) summary for the CAF Medical Solutions Inc. Patient Monitor (models LMPLUS-12, LMPLUS-15, and LMPLUS-17), demonstrating its substantial equivalence to a predicate device (Edan Instruments, Inc. Patient Monitor, models elite V5, elite V6, and elite V8). The document primarily presents a feature-by-feature comparison and non-clinical performance data, with a brief mention of clinical tests.

Therefore, the information regarding acceptance criteria and the study proving the device meets them will be limited to what is explicitly stated in the document or can be inferred from the provided test types and standards. A full, detailed study proving acceptance criteria for specific performance metrics (like sensitivity, specificity, or inter-reader variability for an AI model) is not present in this type of regulatory submission document, which focuses on substantial equivalence to a predicate rather than a novel AI algorithm.

Based on the provided document, here's what can be extracted and inferred regarding performance and validation:

The document indicates that the device's performance was evaluated against various recognized standards for patient monitors, which inherently define acceptable performance ranges for each physiological parameter. The study primarily aims to show that the new device meets these established standards and performs comparably to the predicate device.

1. Table of Acceptance Criteria and Reported Device Performance

The document does not explicitly present a "table of acceptance criteria" in the format of a pre-defined threshold that the device must meet for a specific study's outcome (e.g., "sensitivity > X%"). Instead, it shows a feature-by-feature comparison to a predicate device, including various performance specifications (e.g., accuracy, measurement range) that are in line with industry standards for patient monitors. The "Acceptance Criteria" are implicitly defined by the parameters and accuracy/range specifications of the predicate device and the relevant IEC/ISO standards the device claims compliance with. The "Reported Device Performance" for the subject device (LMPLUS models) is stated to be "Same" as the predicate device across all listed specifications.

Here's an illustrative table based on the provided comparison, highlighting key physiological parameters:

Note: The table above is a summary of just a few representative parameters from the much larger comparison table (Table 1) in the document. The general "Acceptance Criteria" for all listed parameters are the identical specifications of the predicate device.

2. Sample Size Used for the Test Set and Data Provenance

The document states: "Clinical tests were performed on the LMPLUS 12, LMPLUS 15 and LMPLUS 17 monitors to validate their performance in terms of noninvasive blood pressure (NIBP) and SpO2 accuracy."

However, the specific sample sizes for these clinical tests (number of patients, number of measurements) and the data provenance (e.g., country of origin, retrospective or prospective nature) are not detailed in this 510(k) summary. This level of detail would typically be found in the full test report, which is referenced but not included.

3. Number of Experts Used to Establish Ground Truth and Qualifications

The document mentions "clinical tests" for NIBP and SpO2 accuracy. For these types of physiological measurements, the ground truth is typically established by:

• Reference Devices: Using highly accurate, calibrated reference measurement devices.
• Clinical Protocols: Adhering to established clinical protocols for data collection (e.g., for NIBP, a protocol like ISO 81060-2 which often involves comparisons to invasive arterial measurements or calibrated sphygmomanometers by trained healthcare professionals).

There is no mention of human experts being used to establish "ground truth" in the context of interpretation (e.g., radiologists for imaging, unlike an AI algorithm for image analysis). The device measures physiological parameters, and accuracy is validated against established, objective measurement techniques, not expert consensus on qualitative data. Therefore, the concept of "experts establishing ground truth" as it applies to subjective judgments or interpretations (which is common for AI/ML in imaging) is not directly applicable here.

4. Adjudication Method for the Test Set

Given that the clinical tests mentioned are for quantitative physiological parameter accuracy (NIBP and SpO2), adjudication methods like 2+1 or 3+1 (common in studies involving multiple readers for subjective assessments) are not applicable. Accuracy is determined by comparing device readings to a reference standard, not by expert consensus on interpretations.

5. Multi-Reader Multi-Case (MRMC) Comparative Effectiveness Study

No MRMC study was mentioned or implied.
This device is a patient monitor, not an AI-assisted diagnostic tool that would involve human readers interpreting cases. Therefore, a study to measure how much human readers improve with AI assistance is not relevant to this type of device and was not performed.

6. Standalone Performance (Algorithm Only without Human-in-the-Loop)

The document does not describe the device as having a distinct "algorithm" component for analysis that would be evaluated in isolation. It's a physiological monitoring device. Its accuracy in measuring parameters like NIBP and SpO2 is its "standalone performance." The clinical tests mentioned (for NIBP and SpO2 accuracy) would indeed be an assessment of the device's ability to accurately measure these parameters independently, which is effectively its standalone performance. The results are implied by the statement "the subject devices perform comparably to the predicate device."

7. Type of Ground Truth Used

For the clinical tests (NIBP and SpO2 accuracy), the ground truth would be established through:

• Reference Standard Measurements: Using a highly accurate and validated reference device (e.g., an invasive arterial line for NIBP, or a co-oximeter for SpO2) or an established standardized method as per relevant ISO standards (e.g., ISO 81060-2 for NIBP).
• Physiological Data: Direct physiological measurements, not pathology, outcomes data, or expert consensus on subjective interpretations.

8. Sample Size for the Training Set

This document describes a conventional patient monitor, not a medical device that utilizes AI/ML requiring a distinct "training set" of data to learn from. Therefore, there is no mention of a training set or its sample size. The device's algorithms for processing physiological signals are based on established engineering principles and signal processing, not machine learning from a large training dataset.

9. How the Ground Truth for the Training Set Was Established

As there is no training set for this type of device, this question is not applicable.

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The intended use of LE-12CH electrocardiograph is to acquire ECG signals from adult and pediatric patients (beginning at birth through 21 vears of age) through body surface FCG electrocardiograph is only intended to be used in hospitals or healthcare facilities by doctors and trained healtheare professionals. The cardiogram recorded by the electrocardiograph can help users to analyze and diagnose heart disease. However, the interpreted ECG with measurements and interpretive statements is offered to clinicians on an advisory basis only.

CAF Medical's LE-12CH electrocardiograph device is a twelve-channel electrocardiograph, which features a user-friendly design, a high-resolution 8.4-inch (800*600) TFT LCD touch screen, an alphanumeric keypad and an optimized workflow.

The LE-12CH electrocardiograph can acquire 12-channel waveforms simultaneously, and can also print the 12-channel electrocardiograph waveforms simultaneously, using a 216 mm wide thermal printer.

An advanced digital filtering technique has been used in the LE-12CH device, including a baseline anti-drift filter, AC noise filter (50/60 Hz), EMG filter and low-pass filter, which can help the user record the ECG more clearly.

The LE-12CH electrocardiograph works by sampling the ECG signal from the patient's skin surface using special electrodes. This signal is then amplified and sampled in A/D format by the ECG board. The ECG board transfers the data to the system's MCU via UART. The MCU filters the data, sends it to the LCD, prints the waveforms on paper and stores the data in memory. This log can be stored in flash memory or sent to the PC via Ethernet, WIFI or RS232. During the examination, no substances are given to or removed from the patient. The LE-12CH electrocardiograph supports cardiac stress testing.

Here's an analysis of the provided text regarding the acceptance criteria and study for the LE-12CH device:

Based on the provided FDA 510(k) summary for the LE-12CH Electrocardiograph, the submission primarily relies on demonstrating substantial equivalence to a predicate device (K171942. Electrocardiograph with models SE-12, SE-12 Express, SE-1200 and SE-1200 Express. Edan Instruments, Inc.). This means the acceptance criteria are largely aligned with proving that the LE-12CH performs as safely and effectively as the predicate device based on common industry standards and performance specifications.

The document does not describe a specific clinical study (like an MRMC study or standalone algorithm performance study with a test set and ground truth) in the way one might expect for a novel AI/ML-driven diagnostic device. Instead, it details non-clinical testing to ensure compliance with relevant performance and safety standards, and then performs a comparison to the predicate device's specifications.

Therefore, many of the requested fields regarding detailed study methodology (sample size, data provenance, expert consensus, adjudication, effect size) are not applicable in the context of this 510(k) submission as it focuses on demonstrating equivalence through non-clinical means and direct comparison of specifications.

Here's the breakdown of the information that can be extracted or inferred:

Acceptance Criteria and Reported Device Performance

The acceptance criteria are implicitly defined by the safety and performance standards the device was tested against and by the specifications matching the predicate device. The "reported device performance" is essentially that it meets these standards and matches the predicate.

Study Details (as per request, with N/A for non-applicable fields)

1. Sample size used for the test set and the data provenance:

   • Not Applicable. The submission does not describe a clinical "test set" in the context of an algorithm's performance evaluation with patient data. It relies on non-clinical testing against standards and direct comparison of technical specifications to a predicate device.

2. Number of experts used to establish the ground truth for the test set and the qualifications of those experts:

   • Not Applicable. As no clinical test set requiring expert ground truth was documented, this information is not provided. The device includes "interpreted ECG with measurements and interpretive statements... Offered to clinicians on an advisory basis only," indicating that the device's interpretation is assistive and not designed to replace expert diagnosis directly without human review.

3. Adjudication method (e.g. 2+1, 3+1, none) for the test set:

   • Not Applicable. No clinical test set with human adjudication was described.

4. If a multi-reader multi-case (MRMC) comparative effectiveness study was done, If so, what was the effect size of how much human readers improve with AI vs without AI assistance:

   • No. The document explicitly states: "Clinical testing was not required to demonstrate the substantial equivalence of the subject device to the predicate device." Therefore, no MRMC study was conducted or reported.

5. If a standalone (i.e. algorithm only without human-in-the-loop performance) was done:

   • No, not in the sense of a clinical performance study. The device itself is an electrocardiograph that acquires and processes ECG signals, providing interpreted ECG with measurements and interpretive statements. This processing is inherent to the device's function, but its "standalone performance" isn't evaluated as a diagnostic algorithm through a dedicated clinical study with ground truth. Its performance is demonstrated by meeting IEC standards and matching the predicate's technical specifications. The interpretations are explicitly "advisory only."

6. The type of ground truth used (expert consensus, pathology, outcomes data, etc.):

   • Not Applicable. For the purpose of this 510(k), ground truth for specific disease diagnoses from patient data was not established or used to evaluate the device's interpretive statements in a clinical performance study. The "ground truth" here is compliance with established engineering and safety standards (e.g., accuracy of heart rate detection against known input signals, filter performance) and the technical specifications of a legally marketed predicate device.

7. The sample size for the training set:

   • Not Applicable. The document provides no information on a training set, as it does not describe a machine learning algorithm that requires training data. The device's "advanced digital filtering technique" is a more traditional signal processing methodology, not explicitly stated as an AI/ML algorithm requiring a training set for its interpretive functionality.

8. How the ground truth for the training set was established:

   • Not Applicable. As there is no described training set, this information is not provided.

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The SoundBite® Crossing System - Peripheral (14P) is indicated to facilitate the intraluminal placement of conventional guidewires or treatment devices beyond peripheral artery chronic total occlusions.

The SoundBite® Crossing System - Peripheral (14P) is not intended for use in the carotid arteries.

The SoundBite® Crossing System - Peripheral (14P) is a recanalization tool, designed to help physician's placement of conventional guidewires or treatment devices in the intraluminal space beyond chronic total occlusions in the peripheral vasculature. The SoundBite® Crossing System - Peripheral (14P) consists of the reusable mobile SoundBite® Console, a single-use sterile SoundBite® Active Wire 14P, and their respective accessories.

The SoundBite® Console generates controlled mechanical pulses (i.e., shock waves) which are transmitted to the SoundBite® Active Wire 14P and cause the distal tip of the wire to accelerate axially in a reciprocating motion, acting like a micro-jackhammer.

The SoundBite® Active Wire 14P is similar in construction to the 0.018" CTO crossing wire provided with the predicate device, with a friction reducing PTFE coating (except for the distal tip), a radiopaque marker near the tip, and enhanced flexibility at the distal end. It has an outer diameter of 0.36 mm (0.014") and it is 300 cm long, with a working length of 145 cm. At the proximal end of the SoundBite® Active Wire 14P, a section reducer allows the wire to be connected to the SoundBite® Console. The single-use SoundBite® Active Wire 14P is supplied sterile with a shelf life of 24 months.

This document is a 510(k) summary for the SoundBite® Crossing System - Peripheral (14P), a medical device. It focuses on demonstrating substantial equivalence to a predicate device, not on proving clinical efficacy through a specific study with acceptance criteria for device performance as would be the case for an AI/ML-driven device.

Therefore, the requested information elements related to acceptance criteria, device performance, sample sizes for test sets, expert-driven ground truth, adjudication methods, MRMC studies, standalone performance, and training set details are not applicable to this document. This submission relies on non-clinical testing and comparison to a predicate device.

However, I can provide the available information from the document as it relates to the device and its testing:

1. A table of acceptance criteria and the reported device performance

This document does not specify quantitative acceptance criteria or reported device performance in the manner typically seen for AI/ML device evaluations (e.g., sensitivity, specificity, accuracy thresholds). Instead, it states that "The results from bench testing indicate that the performance characteristics of the SoundBite® Crossing System - Peripheral (14P) are substantially equivalent to the predicate device and do not raise new questions of safety or performance." The performance is evaluated through various non-clinical tests to show substantial equivalence.

Non-Clinical Testing Areas:

• Visual and dimensional inspection
• Simulated use
• Coating integrity
• Tip pull
• Flexibility resistance
• Fracture resistance
• Torque strength
• Tensile strength
• Corrosion resistance
• Torqueability
• Distal temperature
• Tip flexibility
• Catheter qualification
• Lubricity assessment
• Particulate testing
• Shelf-life testing
• Console Output Stability
• Life-Cycle Testing
• Console and Shipping Container Labels Verification
• Electronics and Software Verification
• ANSI AAMI ES60601-1:2005/(R) 2012 and A1:2012
• IEC 60601-1-2 Edition 4.0 2014-02
• Biocompatibility (full panel performed under GLP)
• Usability (summative evaluation)

2. Sample sized used for the test set and the data provenance (e.g. country of origin of the data, retrospective or prospective)

Not applicable. The submission is based on non-clinical bench testing, animal studies, and usability evaluations, not clinical data sets from human subjects with specified sample sizes for testing or training AI/ML alongside provenance.

3. Number of experts used to establish the ground truth for the test set and the qualifications of those experts (e.g. radiologist with 10 years of experience)

Not applicable. The ground truth for the non-clinical tests would be established by standard engineering and testing procedures, not by human experts interpreting clinical data.

4. Adjudication method (e.g. 2+1, 3+1, none) for the test set

Not applicable. This concept pertains to expert review of clinical data, which is not the basis of this submission.

5. If a multi reader multi case (MRMC) comparative effectiveness study was done, If so, what was the effect size of how much human readers improve with AI vs without AI assistance

Not applicable. This device is a physical medical device (a catheter system), not an AI/ML-driven diagnostic or assistive tool for human readers.

6. If a standalone (i.e. algorithm only without human-in-the-loop performance) was done

Not applicable. This is not an algorithm-only device. It is a physical medical device used by a physician.

7. The type of ground truth used (expert concensus, pathology, outcomes data, etc)

The "ground truth" for this device's evaluation comes from established engineering standards, material science, biocompatibility testing protocols, and usability guidelines to ensure safety and performance equivalence to the predicate device. For example, "biocompatibility" is assessed against established biological response criteria, and "tensile strength" against material specifications.

8. The sample size for the training set

Not applicable. This is not an AI/ML device that requires a training set.

9. How the ground truth for the training set was established

Not applicable. This is not an AI/ML device that requires a training set.

Summary of the Study (as described in the document):

The submission for the SoundBite® Crossing System - Peripheral (14P) is a 510(k) premarket notification seeking to demonstrate substantial equivalence to an existing predicate device (SoundBite® Crossing System - Peripheral, K192211). The study primarily consists of non-clinical bench testing, animal studies, and usability evaluations.

• Objective: To demonstrate that the SoundBite® Crossing System - Peripheral (14P) is substantially equivalent to its predicate device in intended use, indications for use, fundamental technologies, principles of operation, and labeling.
• Methodology:
  • Non-clinical bench testing: A wide range of physical and mechanical tests were performed on the device components and the system (as listed in point 1 above) to assess its design, performance characteristics, and compliance with recognized standards (e.g., ISO 14971, IEC 62304, ANSI AAMI ES60601-1, IEC 60601-1-2).
  • Biocompatibility testing: A full panel of tests was performed under GLP to ensure materials met biocompatibility requirements.
  • Usability evaluation: Representative users were included in a summative evaluation to confirm safe and effective use.
  • Animal studies: Performance data from animal studies were submitted to support the substantial equivalence claim, particularly concerning the differences in device characteristics (e.g., smaller diameter wire, different pulse repetition rate).
• Conclusion: "The data submitted with this Special 510(k) premarket notification demonstrate that the SoundBite® Crossing System - Peripheral (14P) is substantially equivalent to the predicate device." The differences between the new device and the predicate device (e.g., wire diameter, working length, radiopaque marker position, console setting for pulse repetition rate) were addressed by the non-clinical and animal testing to show they do not raise new questions of safety or performance.

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CloudVue is a software application that displays medical image data to aid in diagnosis for healthcare professionals. It performs operations relating to the transfer, storage, display, and measurement of image data.

CloudVue allows users to perform image manipulations, including window/level, rotation, measurement and markup.

CloudVue provides 2D display, Multi-Planar Reformatting and 3D visualization of medical image data, and mobile access to images.

CloudVue displays both lossless and lossy compressed images. For lossy images, the medical professional user must determine if the level of loss is acceptable for their purposes. Display monitors or mobile devices used for reading medical images for diagnostic purposes must comply with applicable regulatory approvals and with quality control requirements for their use and maintenance. For mobile diagnostic usage only when a full workstation is not available.

Usage for mammography is for reference and referral only.

CloudVue is a software application that allows for the display of medical image data within a web browser without installing client software. Conceptually, the application comprises two components: the web viewer and the web server. The server is installed on any computer that meets the minimum system requirements and is configured to communicate with a DICOM archive. The server is capable of DICOM and DICOMweb communication, which allows the connected DICOM archive to exist on a physical server or in the cloud, giving institutions the flexibility to choose a system infrastructure that is best suited to their needs.

CloudVue provides fast and secure access to full-fidelity CR, CT, DX, HC, IVUS, MR, NM, OP, OPT, OT, PT, SC, US, and XA images using Chrome, Safari, Firefox, or Edge web browsers. With interactive features, such as: multi-study viewing, multi-monitor support, customized screen and tool layouts, annotation saving and loading, measurement drawing and calibration, reference lines, key images, multi-planar reformatting (MPR), MIP, VR rendering, and image link sharing, CloudVue is a feature-rich solution that informs diagnostic decision-making by healthcare professionals.

CloudVue also supports these same features across the following devices: Windows 10 and higher, macOS X and higher, iPad Pro 200 generation and higher, and higher. This level of portability allows healthcare professionals to access their medical image data anytime, anywhere.

The display is not part of the subject device, CloudVue, however, is an essential component of a fullyfunctional imaging system. The end user will view the medical images on the display monitor of their choice.

The provided text is a 510(k) summary for the CloudVue device. It states that no clinical testing was required to support the safety and effectiveness of the subject device. Therefore, there is no study provided in this document that proves the device meets specific acceptance criteria based on clinical performance.

The document focuses on demonstrating substantial equivalence to a predicate device (eUnity) based on technological characteristics and intended use.

Here's an analysis of the information provided within the scope of the request, acknowledging the absence of a clinical study:

1. Table of Acceptance Criteria and Reported Device Performance:

Since no clinical study was conducted for establishing acceptance criteria related to a specific diagnostic performance outcome, there isn't a table in the traditional sense for acceptance criteria. The "performance" described pertains to functional and technical equivalence.

2. Sample size used for the test set and the data provenance:

• Sample Size: Not applicable. No clinical test set was used for a performance study. "Verification and validation activities" were conducted, but specific sample sizes for these internal tests are not detailed as they were not clinical performance studies.
• Data Provenance: Not applicable. No patient data or clinical data provenance is mentioned as part of a performance study.

3. Number of experts used to establish the ground truth for the test set and the qualifications of those experts:

• Not applicable. As no clinical performance study was conducted, there was no independent test set requiring ground truth establishment by experts.

4. Adjudication method for the test set:

• Not applicable. No clinical test set was used for a performance study.

5. If a multi reader multi case (MRMC) comparative effectiveness study was done, If so, what was the effect size of how much human readers improve with AI vs without AI assistance:

• No. The document explicitly states "No clinical testing was required to support safety and effectiveness of the subject device." The CloudVue device is a "medical image processing software" that aids in, but does not provide, a diagnosis. It does not incorporate AI for diagnostic assistance, therefore, an MRMC comparative effectiveness study comparing human readers with and without AI assistance was not performed and is not relevant to this device's stated function.

6. If a standalone (i.e. algorithm only without human-in-the-loop performance) was done:

• No. This device is an image display and manipulation software, not an AI algorithm that provides a standalone diagnostic output. Its function is to provide tools for healthcare professionals to aid in diagnosis.

7. The type of ground truth used (expert consensus, pathology, outcomes data, etc.):

• Not applicable. For the verification and validation activities mentioned, the "ground truth" would have been defined by engineering requirements and expected functional outputs, not by clinical expert consensus, pathology, or outcomes data, as it was not a diagnostic accuracy study.

8. The sample size for the training set:

• Not applicable. The CloudVue device is described as medical image processing software for display and manipulation, not a machine learning or AI model that requires a training set of data.

9. How the ground truth for the training set was established:

• Not applicable. As there is no training set for an AI/ML model, there's no ground truth to establish for it.

In summary: The provided document is a 510(k) summary for a Picture Archiving and Communications System (PACS) component (medical image display software). It demonstrates substantial equivalence to a predicate device based on technical and functional characteristics, and explicitly states that no clinical performance testing was required. Therefore, the detailed information requested regarding clinical studies, experts, ground truth, and AI performance metrics is not present in this submission.

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The syngo.CT Lung CAD VC30 device is a Computer-Aided Detection (CAD) tool designed to assist radiologists in the detection of solid pulmonary nodules during review of multi-detector computed tomography (MDCT) examinations of the chest. The software is an adjunctive tool to alert the radiologist to regions of interest that may have been initially overlooked. The syngo.CT Lung CAD device is intended to be used as a second reader after the radiologist has completed his/her initial read.

Siemens Healthcare GmbH intends to market the syngo.CT Lung CAD which is a medical device that is designed to perform CAD processing in thoracic CT examinations for the detection of solid pulmonary nodules ≥ 3.0 mm in size. The device processes images acquired with Siemens multi-detector CT scanners with 4 or more detector rows.

The syngo.CT Lung CAD device supports the full range of nodule locations (central, peripheral) and contours (round, irregular). The detection performance of the syngo.CT Lung CAD device is optimized for nodules between 3.0 mm and 20.0 mm in size.

The syngo.CT Lung CAD sends a list of nodule candidate locations to a visualization application, such as syngo MM Oncology, or a visualization rendering component, which generates output images series with the CAD marks superimposed on the input thoracic CT images for use in a second reader mode. syngo MM Oncology (FDA clearance K191309) is implemented on the syngo.via platform (FDA clearance K191040), which provides a common framework for various other applications implementing specific clinical workflows (but are not part of this clearance) to display the CAD marks. The syngo.CT Lung CAD device is intended to be used as a second reader only after the initial read is completed.

The subject device and the predicate device has the same basic technical characteristics as the predicate; however, the fundamental technology has been replaced by deep learning technology. Specifically, the predicate VC20 uses feature-based and Machine Learning whereas the current VC30 uses algorithms based on Convolutional Neural Networks. This does not introduce new types of safety or effectiveness concerns. In particular, as demonstrated by the statistical analysis and results of the standalone benchmark evaluations:

i. The standalone accuracy has been shown not only to be non-inferior but actually superior to that of the device and
ii. The marks generated by the two devices have been shown to be reasonably consistent.

This device description holds true for the subject device, syngo.CT Lung CAD, software version VC30, as well as the predicate device, syngo.CT Lung CAD, software version VC20.

Here's a breakdown of the acceptance criteria and the study proving the device meets them, based on the provided FDA 510(k) summary for syngo.CT Lung CAD (VC30):

Device Name: syngo.CT Lung CAD (VC30)
Intended Use: A Computer-Aided Detection (CAD) tool to assist radiologists in the detection of solid pulmonary nodules (≥ 3.0 mm) during review of multi-detector computed tomography (MDCT) examinations of the chest. It's an adjunctive tool to alert radiologists to initially overlooked regions of interest, used as a second reader after the radiologist's initial read.

1. Table of Acceptance Criteria and Reported Device Performance

The document primarily focuses on demonstrating non-inferiority and superiority to the predicate device rather than explicitly stating acceptance criteria with numerical targets for metrics like sensitivity or specificity. However, based on the conclusions regarding "standalone accuracy" and "false positive rate," we can infer the implicit criteria and the reported performance as comparative to the predicate.

Note: The document describes the study as a "standalone benchmark evaluation" focused on comparing VC30's performance to VC20's. Specific numerical metrics for sensitivity, specificity, or FPs are not provided in this summary, but the conclusions about superiority and reduction in FPs serve as the performance statement.

2. Sample Size Used for the Test Set and Data Provenance

• Sample Size for Test Set: The document states that "The endpoints to establish meaningful and statistically relevant performance and equivalence of the device and sample size were considered and defined as part of the test protocols." However, the specific number of cases or nodules in the test set is not provided in this summary.
• Data Provenance: Not explicitly stated regarding country of origin. The document mentions "Non-clinical performance testing was performed at various levels for verification and validation of the device intended use and to ensure safety and effectiveness." It does not specify if the data was retrospective or prospective.

3. Number of Experts Used to Establish Ground Truth and Qualifications

• Number of Experts: Not specified in the provided text.
• Qualifications of Experts: Not specified in the provided text.

4. Adjudication Method for the Test Set

• Adjudication Method: Not specified in the provided text. The document refers to "ground truth" but does not detail the method by which it was established.

5. Multi-Reader Multi-Case (MRMC) Comparative Effectiveness Study

• MRMC Study: The document does not mention a multi-reader multi-case (MRMC) comparative effectiveness study comparing human readers with AI vs. without AI assistance. The study described is a "standalone benchmark evaluation" comparing the performance of the new AI algorithm (VC30) to the previous algorithm (VC20).
• Effect Size of Human Improvement: Not applicable, as no MRMC study is detailed here.

6. Standalone (Algorithm Only) Performance Study

• Standalone Study: Yes, a standalone study was done. The document explicitly states: "The standalone performance test proved that the standalone sensitivity of syngo.CT Lung CAD VC30 is superior to that of syngo.CT Lung CAD VC20 (predicate) and the false positive rate improved (reduced)."

7. Type of Ground Truth Used

• Type of Ground Truth: The document refers to "ground truth" for the test set, stating that it was established to define "meaningful and statistically relevant performance." However, the specific method (e.g., expert consensus, pathology, follow-up outcomes) for establishing this ground truth is not detailed in the provided summary.

8. Sample Size for the Training Set

• Sample Size for Training Set: The document does not provide the sample size used for the training set. It only mentions that the "fundamental technology has been replaced by deep learning technology," indicating a training process was involved.

9. How the Ground Truth for the Training Set Was Established

• How Ground Truth for Training Set Was Established: The document does not provide details on how the ground truth for the training set was established. It only describes the functional components of the new syngo.CT Lung CAD VC30 as using Convolutional Neural Networks (CNN) for lung segmentation, candidate generation, feature calculation, and candidate classification, which inherently require labeled training data.

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MOBILETT Elara Max is a mobile device intended to visualize anatomical structures by converting an X-ray pattern into a visible image. MOBILETT Elara Max enables radio-graphic exposures of the whole body and may be used on pediatric. adult and bariatric patients. It can also be used for emergency applications. MOBILETT Elara Max is not indicated for mammographic imaging.

The MOBILETT Elara Max is a mobile X-ray system with a solid state x-ray imager (SSXI). The system is designed to provide X-ray imaging by healthcare professionals. Rechargeable batteries support cable less operation and motor-driven movements to the point of care. The mobile generator is positioned at bedside and the X-ray system is directed to the anatomical area to be imaged. The image detector is placed perpendicular to the central beam behind this anatomical area. The system features a collimator with a light field that mimics the x-ray field to limit the field of exposure to the area to be imaged. Exposure may be released via remote control. The image data acquired by the detector are send wireless to the mobile unit.

The purpose of this submission is a new software version VF10, some minor hardware changes and the new device name MOBILETT Elara Max. The new software VF10 will introduce the following new features:

• The operating system will be MS Windows 10
• New cybersecurity features
• Additional pediatric programs
• Implementing a "Virtual Machine" that supports hospital IT
• The SSXIs have been updated
• The EMC (Electromagnetic Compatibility was tested according to the IEC 4th edition)
• The image processing algorithms (Diamond View MAX) will be used for exposures without grid.
• The Mobilett Elara will be coated with an anti-microbial paint

The provided text is a 510(k) summary for the Siemens MOBILETT Elara Max, a mobile X-ray system. The document focuses on demonstrating substantial equivalence to a predicate device (MOBILETT MIRA), primarily through non-clinical testing and comparison of technical characteristics, particularly regarding a new software version (VF10) and minor hardware changes.

*Crucially, this document does not contain any information about an AI-powered medical device or a clinical study that establishes acceptance criteria and then proves the device meets those criteria using expert consensus, MRMC studies, or specific performance metrics like sensitivity/specificity.

The device, MOBILETT Elara Max, is a standard X-ray imaging system. The updates described (new software, cybersecurity features, updated SSXIs, antimicrobial paint) are enhancements to the existing X-ray system, not features that introduce AI for diagnostic interpretation or require a clinical study with expert readers to establish diagnostic performance.

Therefore, I cannot fulfill your request to describe acceptance criteria and a study proving an AI device meets them based on the provided text. The document is primarily a regulatory submission demonstrating substantial equivalence for a conventional medical imaging device.

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