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The Eclipse Treatment Planning System (Eclipse TPS) is used to plan radiotherapy treatments for patients with malignant or benign diseases. Eclipse TPS is used to plan external beam irradiation with photon, electron and proton beams, as well as for internal radiation (brachytherapy) treatments.

Eclipse provides software tools for planning the treatment of malignant or benign diseases with radiation. Eclipse is a computer-based software device used by trained medical professionals to design and simulate radiation therapy treatments. It is capable of planning treatments for external beam irradiation with photon, electron, and proton beams, as well as for internal irradiation (brachytherapy) treatments.

Eclipse is used for planning external beam radiation therapy treatments employing photon energies between 1 and 50 MV, electron energies between 1 and 50 MeV, for proton energies between 50 and 300 MeV, and for planning internal radiation (brachytherapy) treatments with any clinically approved radioisotope. The treatment planning system utilizes a patient model or virtual patient derive from medical imaging techniques to simulate, calculate and optimize the radiation dose distribution inside the body during a treatment procedure in order to ensure effective treatment of the tumor but to minimize damage to surrounding tissue.

The provided document is a 510(k) premarket notification letter from the FDA regarding the Eclipse Treatment Planning System (18.1). It primarily addresses the substantial equivalence of the new version to a legally marketed predicate device (Eclipse Treatment Planning System 18.0).

Unfortunately, this document does not contain the detailed information required to describe acceptance criteria and a study proving the device meets those criteria, as requested in your prompt.

Here's why and what information is missing:

• No specific acceptance criteria table or performance metrics: The document states that "Test results demonstrate conformance to applicable requirements and specifications," but it does not list what those requirements or specifications are (the acceptance criteria), nor does it provide a table of reported device performance against those criteria.
• No information on clinical studies or human-in-the-loop performance: The document explicitly states, "No animal studies or clinical tests have been included in this pre-market submission." This immediately tells us that there was no MRMC study, no standalone performance study in a clinical context, and no ground truth established from patient outcomes or expert consensus for such a study.
• Focus on software V&V and equivalence to predicate: The "Summary of Performance Testing (Non-Clinical Testing)" section primarily references software verification and validation (V&V) activities (unit, integration, system testing) and measurement comparison tests using Gamma evaluation criteria and plan comparisons using clinical objectives and workflow testing to show comparability to the predicate. These are engineering and software quality assurance tests, not clinical performance studies with defined acceptance metrics for AI/algorithm performance.
• No mention of AI/algorithm specific performance: The document describes "RapidArc Dynamic" as an "improved optimization algorithm," but it does not treat it as a distinct AI algorithm requiring specific clinical performance validation against a ground truth as one might expect for a diagnostic or prognostic AI tool. The testing mentioned appears to be related to the accuracy and efficiency of the planning output compared to the predicate, rather than the performance of an AI model in a diagnostic or assistive capacity.
• No details on sample size, data provenance, expert ground truth, or adjudication: Because no clinical performance study was conducted or reported, all these details are consequently missing.

In summary, the provided document focuses on regulatory compliance, substantial equivalence to a predicate device, and general software V&V, rather than providing the detailed clinical performance study information you are asking for, which is typical for AI/ML-driven diagnostic or prognostic devices seeking regulatory clearance.

The "Eclipse Treatment Planning System" is a software tool used by trained medical professionals to design and simulate radiation therapy treatments. While it includes "optimization algorithms" (like RapidArc Dynamic), the FDA submission treats these changes as enhancements to an existing system, validated through engineering and software testing for comparability, rather than a novel AI/ML device requiring an independent clinical performance study as outlined in your prompt questions.

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The Eclipse Treatment Planning System (Eclipse TPS) is used to plan radiotherapy treatments with malignant or benign diseases. Eclipse TPS is used to plan irradiation with photon, electron and proton beams, as well as for internal radiation (brachytherapy) treatments.

The Varian Eclipse™ Treatment Planning System (Eclipse TPS) provides software tools for planning the treatment of malignant or benign diseases with radiation. Eclipse TPS is a computer-based software device used by trained medical professionals to design and simulate radiation therapy treatments. Eclipse TPS consists of different applications, each used for specific purposes at a different phase of treatment planning. Eclipse TPS is capable of planning treatments for external beam irradiation with photon, electron, and proton beams, as well as for internal irradiation (brachytherapy) treatments.

The provided FDA 510(k) summary for the Eclipse Treatment Planning System (v18.0) does not include acceptance criteria or the specific details of a study that proves the device meets acceptance criteria in the format requested.

The document states that "Software verification and validation was conducted and documentation was provided," and that "Test results demonstrate conformance to applicable requirements and specifications." However, it does not provide a table of acceptance criteria or reported device performance metrics. It also explicitly states: "No animal studies or clinical tests have been included in this pre-market submission."

Therefore, for the specific questions requested, the direct answer from the provided text is that the information is not available.

Here's a breakdown of the requested information that cannot be sourced from the provided text:

1. A table of acceptance criteria and the reported device performance: Not provided. The document generally states conformance to requirements and specifications but doesn't list specific performance metrics or their acceptance thresholds.
2. Sample size used for the test set and the data provenance: Not provided. The nature of the "test set" for software verification and validation is not detailed, nor is the origin of any data used. Given the statement "No animal studies or clinical tests," it's highly likely this refers to internal software testing data.
3. Number of experts used to establish the ground truth for the test set and the qualifications of those experts: Not provided. There is no mention of expert involvement in establishing ground truth for any test set.
4. Adjudication method (e.g. 2+1, 3+1, none) for the test set: Not provided.
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 provided. The device is a "Treatment Planning System" and the focus is on its software functionality, not on AI assistance for human readers in diagnostic interpretation.
6. If a standalone (i.e. algorithm only without human-in-the loop performance) was done: The document describes "Software Verification and Validation Testing" but does not distinguish between standalone and human-in-the-loop performance, nor does it provide performance metrics. The nature of a "treatment planning system" inherently involves a human user in the loop.
7. The type of ground truth used (expert consensus, pathology, outcomes data, etc): Not provided.
8. The sample size for the training set: Not applicable/Not provided. As a traditional software update for a treatment planning system, it's unlikely to involve a "training set" in the machine learning sense. The changes described are primarily related to dose calculation algorithms, UI support, and brachytherapy controls.
9. How the ground truth for the training set was established: Not applicable/Not provided.

Summary of what is present:

• Device Name: Eclipse Treatment Planning System (v18.0)
• Predicate Device: Eclipse Treatment Planning System v16.1 (K201607)
• Indications for Use: To plan radiotherapy treatments for patients with malignant or benign diseases, using photon, electron, proton, and brachytherapy beams.
• Performance Data: "Software verification and validation was conducted and documentation was provided... Test results demonstrate conformance to applicable requirements and specifications."
• No Clinical/Animal Studies: Explicitly stated that "No animal studies or clinical tests have been included in this pre-market submission."
• Software Level of Concern: "Major."
• Standards Conformance: A list of IEC, ISO, and EN ISO standards is provided (e.g., IEC 62304, IEC 62366-1, IEC 82304-1, IEC 62083, IEC 61217, ISO 15223-1, ISO 20417, ISO 14971, EN ISO 13485).
• Conclusion: The device is considered "safe and effective and perform at least as well as the predicate device" based on non-clinical data, verification, and validation.

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The Eclipse MINI Model 98900 is a portable non-invasive continuous ambulatory ECG patch recorder intended to record the patient's electrocardiogram. The recorder is intended to be used by either paediatric or adult patients suspected of cardiac arrhythmias in either a clinical setting or at home. The recorder does no cardiac analysis and is used with Spacelabs Ambulatory ECG Analysis Software.

The Eclipse MINI Model 98900 (Eclipse MINI) is an ambulatory electrocardiograph (ECG) recorder capable of providing a 3-lead recording. It is connected to the patient using a custom, disposable, single-patient only, 3-lead sensor patch that is adhesively attached to the patient's chest. The Eclipse MINI is powered by batteries that are integrated in the Eclipse Sensor Patch. These batteries are primary cells which cannot be recharged and power the recorder for up to 15 days. For an extended recording multiple Eclipse Sensor Patches may be required. The single patient event button allows the patient to indicate symptomatic episodes in the recording for correlation with the patient diary. The Eclipse MINI is fully sealed and waterproof. The Eclipse MINI is attached to the "holster" of the Eclipse MINI Sensor Patch by inserting the USB connector on the sensor patch into the USB receptacle on the bottom of Eclipse MINI housing. Patient data from the Eclipse MINI is downloaded to a PC upon which the Spacelabs Sentinel Cardiology Information Management System (Sentinel), cleared in 510(k) submission K152881, has been installed. This allows the clinician to download, view, and analyze patient data from the Eclipse MINI, and create reports. Further analysis of these patient data can be performed by using Spacelabs Pathfinder SL Holter Analyzer (cleared in 510(k) submission K110001) and/or Spacelabs Lifescreen PRO Analyzer (cleared in 510(k) submission K201921). Finally, a non-medical device mobile phone app is available for patient use as an electronic note taking option in lieu of a manual, written patient diary.

This is a 510(k) premarket notification for the Spacelabs Eclipse MINI Model 98900. A 510(k) submission generally aims to demonstrate substantial equivalence to a predicate device, rather than proving that a device meets specific acceptance criteria through a clinical study for a novel device. The document primarily focuses on demonstrating that the Eclipse MINI Model 98900 is substantially equivalent to the Reynolds Medical Ltd. Lifecard CF 7-Day Holter Recorder (K011837) based on technological characteristics and performance testing in accordance with regulatory standards.

Therefore, the requested information regarding acceptance criteria and a study proving the device meets them, in the typical sense of a clinical trial for a new device, is not fully applicable or explicitly detailed in this 510(k) summary. The "acceptance criteria" here largely refer to meeting recognized standards for safety and performance to demonstrate substantial equivalence.

Here's an attempt to extract and interpret the information based on the provided document within the context of a 510(k) submission:

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

The document does not present a single table of "acceptance criteria" and "reported device performance" in the way one might expect for a specific clinical endpoint. Instead, the device's performance is demonstrated through compliance with various national and international standards and internal requirements, and by performing comparably to the predicate device.

The "Technology Comparison" table (page 5) lists various characteristics and indicates that the Eclipse MINI's performance is consistent with these standards and the predicate. The "Summary of Performance Testing" sections (pages 6 and 7) state that the device "complies with internal requirements, applicable Standards, and the guidance document."

Here is a summary of the broad performance areas and the reported compliance:

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

This information is not provided in a way that relates to a "test set" for a diagnostic study. The performance testing described (shelf-life, biocompatibility, software V&V, electrical safety, EMC, bench performance) are engineering and laboratory tests, not clinical studies with a patient test set in the traditional sense. Therefore, details like "sample size used for the test set" and "data provenance" (country/retrospective/prospective) are not applicable or detailed for this type of submission.

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)

This device, the Eclipse MINI Model 98900, is an ECG recorder and "does no cardiac analysis" itself. It is used with "Spacelabs Ambulatory ECG Analysis Software" (which would have its own clearances, e.g., K152881, K110001, K201921). As such, it is not a diagnostic algorithm that provides an output requiring ground truth established by experts. Its function is to accurately record ECG data, and its performance is assessed against technical standards for signal acquisition and safety, not diagnostic accuracy. Therefore, this information is not applicable to the device described.

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

Not applicable, as there is no diagnostic "test set" and no "ground truth" adjudicated by experts for this recording device as described in the 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 an ECG recorder and not an AI/CADe/CADx system. No MRMC study or AI assistance improvement is mentioned or relevant to the device's function.

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

Not applicable. This device is an ECG recorder, not an algorithm, and performs "no cardiac analysis."

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

Not applicable. As described in points 3, 4, 5, and 6, this device does not perform analysis that would require a ground truth for diagnostic accuracy. Its "ground truth" for performance is compliance with engineering and safety standards, and reliable recording of ECG data.

8. The sample size for the training set

Not applicable. This device is primarily hardware an embedded software for recording. It is not an AI/machine learning model that requires a training set. The software mentioned is developed through robust software development processes (as noted in the "Software" testing section) and validated against specifications, not "trained" on a data set in the ML sense.

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

Not applicable, as there is no training set for an AI/ML model for this device.

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The Eclipse Blood Collection Set with holder is intended to be used with vacuum blood collection tube for the collection of venous blood. The safety shield is intended to aid in the protection against accidental needle stick injury.

The Eclipse Blood Collection Set is a winged blood collection needle with flexible tubing intended for venipuncture to obtain blood samples. It is provided with a safety shield for covering the used venipuncture needle prior to disposal to aid in the prevention of needle stick injury if manually activated after the blood draw. For blood collection, the set also includes a blood collection holder for connection to vacuum-based collection vials

The provided text is a U.S. FDA 510(k) summary for the Eclipse Blood Collection Set. It describes the device's characteristics and its comparison to a predicate device to demonstrate substantial equivalence. However, it does not contain the detailed information requested regarding specific acceptance criteria, study methodologies, sample sizes, expert qualifications, or ground truth establishment for a diagnostic AI device.

The document states: "The Eclipse Blood Collection Set was tested for validation and verification of functions based on risk analysis and the results passed predetermined acceptance criteria." However, it does not specify what those predetermined acceptance criteria were or present a table comparing them to reported device performance.

It also mentions various types of performance testing, such as biocompatibility, performance data (per ISO 80369-7:2016 and ISO 80369-20:2015), and sterilization/shelf-life testing against relevant ISO and ASTM standards. While these indicate tests were performed to ensure the device meets safety and functionality standards relevant to its type, they are largely about manufacturing and material safety, not diagnostic performance or AI model validation.

Crucially, the document explicitly states: "Clinical Study: Not applicable". This indicates that a clinical study, which would typically involve human subjects and the kind of performance data (e.g., sensitivity, specificity) relevant to AI diagnostic devices, was not performed or deemed necessary for this type of medical device clearance.

Therefore, the requested information, which is more applicable to diagnostic AI devices, cannot be extracted from this document, as the Eclipse Blood Collection Set is a physical blood collection device and not an AI-powered diagnostic tool.

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The software performs digital enhancement of a radiographic image generated by an x-ray device. The software can be used to process adult and pediatric x-ray images. This excludes mammography applications.

Eclipse software runs inside the Image View product application software (not considered stand-alone software). Smart Noise Cancellation is an optional feature (module) that enhances projection radiography acquisitions captured from digital radiography imaging receptors (Computed Radiography (CR) and Digital Radiography (DR). Eclipse II with Smart Noise Cancellation supports the Carestream DRX family of detectors which includes all CR and DR detectors.

The Smart Noise Cancellation module consists of a Convolutional Network (CNN) trained using clinical images with added simulated noise to represent reduced signal-to-noise acquisitions.

Eclipse II with Smart Noise Cancellation incorporates enhanced noise reduction prior to executing Eclipse image processing software. The software has the capability to lower dose up to 50% when processed through the Eclipse II software with SNC, resulting in improved image quality. A 50% dose reduction for CSI panel images and 40% dose reduction for GOS panel images when processed with Eclipse II and SNC results in image quality as good as or better than nominal dose images

The provided document describes the modification of the Eclipse II software to include a Smart Noise Cancellation (SNC) module. The primary goal of this modification is to enable lower radiation doses while maintaining or improving image quality. The study discussed is a "concurrence study" involving board-certified radiologists to evaluate diagnostic image quality.

Here's the breakdown of the acceptance criteria and study details:

1. Table of Acceptance Criteria and Reported Device Performance:

The document doesn't explicitly state "acceptance criteria" in a table format with specific numerical thresholds for image quality metrics. Instead, it describes the objective of the study which effectively serves as the performance goal for the device.

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

• Sample Size for Test Set: Not explicitly stated. The document mentions "clinical images" and "exams, detector types and exposure levels" were used, but a specific number of images or cases for the test set is not provided.
• Data Provenance: Not explicitly stated. The document refers to "clinical images," but there is no information about the country of origin or whether the data was retrospective or prospective.

3. Number of Experts Used to Establish the Ground Truth for the Test Set and Qualifications of Those Experts:

• Number of Experts: Not explicitly stated. The study was performed by "board certified radiologists." The number of radiologists is not specified.
• Qualifications of Experts: "Board certified radiologists." No information is given regarding their years of experience.

4. Adjudication Method for the Test Set:

• Adjudication Method: Not explicitly stated. The document mentions a "5-point visual difference scale (-2 to +2) tied to diagnostic confidence" and a "4-point RadLex scale" for evaluating overall diagnostic capability. However, it does not describe how multiple expert opinions were combined or adjudicated if there were disagreements (e.g., 2+1, 3+1).

5. If a Multi-Reader Multi-Case (MRMC) Comparative Effectiveness Study Was Done, and the Effect Size of How Much Human Readers Improve with AI vs. Without AI Assistance:

• MRMC Study: The study appears to be a multi-reader study as it was "performed by board certified radiologists." However, it is not a comparative effectiveness study comparing human readers with AI assistance vs. without AI assistance. The study's aim was to determine if the software itself (Eclipse II with SNC) could produce diagnostic quality images at reduced dose, assessed by human readers. It's evaluating the output of the software, not the improvement of human readers using the software as an assistance tool.
• Effect Size: Not applicable, as it's not an AI-assisted human reading study.

6. If a Standalone (i.e., algorithm only without human-in-the-loop performance) Was Done:

• Standalone Performance: No, a standalone (algorithm only) performance evaluation was not done. The evaluation involved "board certified radiologists" assessing the diagnostic quality of the images processed by the software. This is a human-in-the-loop assessment of the processed images, not a standalone performance of the algorithm making diagnoses.

7. The Type of Ground Truth Used:

• Type of Ground Truth: The ground truth for image quality and diagnostic capability was established by expert consensus (or at least expert assessment), specifically "board certified radiologists," using a 5-point visual difference scale and a 4-point RadLex scale. This is a subjective assessment by experts, rather than an objective ground truth like pathology or outcomes data.

8. The Sample Size for the Training Set:

• Sample Size for Training Set: Not explicitly stated. The document mentions that the Convolutional Network (CNN) was "trained using clinical images with added simulated noise." However, no specific number of images or cases used for training is provided.

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

• Ground Truth for Training Set: The document states the CNN was "trained using clinical images with added simulated noise to represent reduced signal-to-noise acquisitions." This implies that the ground truth for training likely revolved around distinguishing actual image data from added simulated noise. This is an intrinsic ground truth generated by the method of simulating noise on known clean clinical images, rather than a clinical ground truth established by expert review for diagnostic purposes.

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The Spacelabs Model 98700 is a portable non-invasive Holter recorder intended to record the patient's ambulatory electrocardiogram. The device is intended to be used by either pediatric or adult patients in either or at home. The device does no cardiac analysis and is used with a Holter Analysis System.

The Eclipse PRO Model 98700 (Eclipse PRO) is an ambulatory electrocardiograph (ECG) recorder capable of providing a true 12-channel recording for up to 72 hours or a 3-lead recording for up to 14 days. It is connected to the patient using Eclipse PRO-specific 3, 4, or 10 lead wire cables with the recorder located in a pouch on a lanyard.

The Eclipse PRO has a large, color display which allows the user to view recorder status, configure the recorder and to view ECG lead application.

It has an internal, fast-charge, long-life, rechargeable lithium-ion battery, which is charged when connected by a cable between the USB port of a personal computer (PC) or a USB charger, and the recorder's USB-C connector.

Two (2) arrow keys on the front of the Eclipse PRO are used to navigate and select options in the recorder's menu or as a patient event button when the patient desires to indicate symptomatic episodes in the recording.

The Eclipse PRO is fully sealed and waterproof.

Patient cables are attached to the Eclipse PRO using the USB connector on the bottom of the housing and protective cable retention loop.

Patient data from the Eclipse PRO is downloaded to a PC upon which the Spacelabs Sentinel Cardiology Information Management System (Sentinel), cleared in 510(k) submission K152881, has been installed. This allows the clinician to download, view, and analyze patient data from the Eclipse PRO, and create reports. Further analysis of these patient data can be performed by using Spacelabs Pathfinder SL Holter Analyzer (cleared in 510(k) submission K110001) and/or Spacelabs Lifescreen PRO Analyzer (cleared in 510(k) submission K201921).

Finally, a non-medical device mobile phone app is available for patient use as an electronic note taking option in lieu of a manual, written patient diary.

The Spacelabs Eclipse PRO Model 98700 is an ambulatory electrocardiograph (ECG) recorder. The provided text outlines its characteristics and the performance testing conducted to demonstrate its substantial equivalence to the predicate device, Reynolds Medical Ltd. Lifecard CF 7 Day (K011837).

Here's an analysis of the acceptance criteria and the study that proves the device meets them, based on the provided text:

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

The document does not explicitly list "acceptance criteria" in a quantitative, metrics-based format for the overall device performance in recording ECGs. Instead, it focuses on demonstrating compliance with various regulatory standards and guidance documents, and technological equivalence to the predicate device. The performance testing section primarily reports that the device "complies" with these standards.

However, based on the "Technology Comparison" table, we can infer some functional acceptance criteria by comparing the proposed device against the predicate.

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

The provided document describes performance testing related to device functionality, electrical safety, EMC, software validation, and biocompatibility, but does not detail any clinical study or human subject trial for evaluating ECG recording performance. Therefore, there is no information on a "test set sample size" or "data provenance" related to patient data evaluation. The tests mentioned are primarily bench tests, engineering validations, and compliance checks against regulatory standards.

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)

As no clinical test set using patient data is described, this information is not applicable and not provided in the document. The performance evaluation focuses on technical compliance, not diagnostic accuracy requiring expert interpretation.

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

Since no clinical test set involving human interpretation of data is described, an adjudication method is not applicable and not provided.

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

The device described, Spacelabs Eclipse PRO, is a Holter recorder that "does no cardiac analysis" and is "used with a Holter Analysis System." The document explicitly states it's a recorder, not an analytical or AI-assisted diagnostic tool. Therefore, no MRMC comparative effectiveness study with AI assistance was performed or is relevant to this device.

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

As the device itself "does no cardiac analysis" and is a pure recorder, there is no algorithm for standalone performance to be evaluated. This question is not applicable to this device.

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

For the technical performance testing (biocompatibility, software, electrical safety, EMC, bench performance), the "ground truth" used is compliance with established international standards and FDA guidance documents (e.g., ISO 10993-1, IEC 62304, IEC 60601 series). For the functional comparison, the "ground truth" for substantial equivalence is the predicate device's characteristics and specifications. There is no clinical data 'ground truth' in the context of expert diagnosis or patient outcomes.

8. The sample size for the training set

Since this is a medical device (Holter recorder) and not an AI/ML-based diagnostic algorithm, there is no concept of a "training set" as would be applicable to machine learning.

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

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

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The software performs digital enhancement of a radiographic image generated by an x-ray device. The software can be used to process adult and pediatric x-ray images. This excludes mammography applications.

Eclipse software runs inside the ImageView product application software (also namely console software). The Eclipse image processing software II with Smart Noise Cancellation is similar to the predicate Eclipse image processing software (K180809). Eclipse with Smart Noise Cancellation is an optional feature that enhances projection radiography acquisitions captured from digital radiography imaging receptors (Computed Radiography (CR) and Direct Radiography (DR). The modified software is considered an extension of the software (it is not stand alone and is to be used only with the predicate device supports the Carestream DRX family of detectors, this includes all CR and DR detectors. The primary difference between the predicate and the subject device is the addition of a Smart Noise Cancellation module. The Smart Noise Cancellation module consists of a Convolutional Network (CNN) trained using clinical images with added simulated noise to represent reduced signal-to-noise acquisitions. Eclipse with Smart Noise Cancellation (modified device) incorporates enhanced noise reduction prior to executing Eclipse II image processing software.

Here's a breakdown of the acceptance criteria and the study proving the device meets them, based on the provided text:

Based on the provided text, the device Eclipse II with Smart Noise Cancellation is considered substantially equivalent to its predicate Eclipse II (K180809) due to modifications primarily centered around an enhanced noise reduction feature. The acceptance criteria and the study that proves the device meets these criteria are inferred from the demonstrated equivalence to the predicate device and the evaluation of the new Smart Noise Cancellation module.

1. Table of Acceptance Criteria and Reported Device Performance

The acceptance criteria are implicitly tied to the performance of the predicate device and the new feature's ability to maintain or improve upon key image quality attributes without introducing new safety or effectiveness concerns.

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

• Sample Size for Test Set: Not explicitly stated. The text mentions "a range of exams, detector types and exposure levels" for the clinical evaluation, and "clinical images with added simulated noise" for the CNN training.
• Data Provenance: Not explicitly stated. The text mentions "clinical images," implying real-world patient data, but does not specify the country of origin or whether it was retrospective or prospective.

3. Number of Experts Used to Establish the Ground Truth for the Test Set and Qualifications

• Number of Experts: Not explicitly stated. The text mentions a "clinical evaluation was performed by board certified radiologists." It does not specify the number involved.
• Qualifications of Experts: "Board certified radiologists." No specific years of experience are provided.

4. Adjudication Method for the Test Set

• Adjudication Method: Not explicitly stated. The text mentions images were evaluated using a "5-point visual difference scale (-2 to +2) tied to diagnostic confidence" and a "4-point RadLex scale" for overall diagnostic capability. It does not describe a method for resolving discrepancies among multiple readers, such as 2+1 or 3+1.

5. If a Multi-Reader Multi-Case (MRMC) Comparative Effectiveness Study was done

• MRMC Comparative Effectiveness Study: Yes, a clinical evaluation was performed by board-certified radiologists comparing the investigational software to the predicate software. While it doesn't explicitly use the term "MRMC," the description of a clinical evaluation by multiple radiologists comparing two versions of software suggests this type of study was conducted.
• Effect Size of Human Readers Improvement with AI vs. without AI Assistance: The text states, "The statistical test results and graphical summaries demonstrate that the investigational software delivers diagnostic quality images that exceed the quality of the predicate software over a range of exams, detector types and exposure levels." This indicates an improvement in diagnostic image quality with the new software (which incorporates AI - the CNN noise reduction), suggesting that human readers benefit from this enhancement. However, a specific effect size (e.g., AUC improvement, percentage increase in accuracy) is not provided in the summary.

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

• Standalone Performance: Partially. The "Ideal Observer Evaluation" seems to be a more objective, algorithm-centric assessment of detectability, stating that "detectability is preserved or improved with the investigational software." Also, the "Analysis of the Difference Images" checked for artifacts without human interpretation as the primary outcome. However, the overall "diagnostic quality" assessment was clinical, involving human readers.

7. The Type of Ground Truth Used

• Type of Ground Truth: The text implies a human expert consensus/evaluation as the primary ground truth for diagnostic quality. The "5-point visual difference scale" and "4-point RadLex scale" evaluated by "board certified radiologists" serve as the basis for assessing diagnostic image quality. For the "Ideal Observer Evaluation," the ground truth likely involved simulated lesions.

8. The Sample Size for the Training Set

• Training Set Sample Size: Not explicitly stated. The text mentions "clinical images with added simulated noise" were used to train the Convolutional Network (CNN).

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

• Ground Truth for Training Set: The ground truth for training the Smart Noise Cancellation module (a Convolutional Network) was established using "clinical images with added simulated noise to represent reduced signal-to-noise acquisitions." This suggests that the model was trained to learn the relationship between noisy images (simulated low SNR) and presumably clean or less noisy versions of those clinical images to perform noise reduction. The text doesn't specify how the "clean" versions were obtained or verified, but it implies a supervised learning approach where the desired noise-free output served as the ground truth.

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The Eclipse DermaFlex Cannula is intended to inject fluids intradermally.

The Eclipse DermaFlex Cannula is provided as a single-use, sterile device. It is comprised of a needle. hub and cap. This device comes in a variety of needle gauges and lengths. The Eclipse DermaFlex Cannula offers AN type and B Type. The AN type is a hypodermic single lumen needle intended to prepare the site for injection. The tip of this pilot needle is sharpened at one end, while the other end is joined to a hub. The B type has a metal tube with the tip, which is closed and blunt, while the cannula has an opening laterally at a lower point under the tip. On the other end, the device is joined to a female connector (hub) designed to mate with a male connector (nozzle) of a piston syringe or secondary medication sets to prepare and administer fluids/ medications/ drugs to a patient.

The Eclipse DermaFlex Cannula is a hypodermic single lumen needle intended to inject fluids intradermally. The submission demonstrates substantial equivalence to predicate device MV INTRADERMIC NEEDLES; MAGIC NEEDLE (K110606) by M.V. S.R.L.

Here's an analysis of the provided information regarding acceptance criteria and the study that proves the device meets them:

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

The document does not explicitly state acceptance criteria in a quantifiable manner for each test. Instead, it refers to performance testing conducted in accordance with various ISO and ASTM standards. The reported device performance is a general statement that the device "meets the established characteristics and performance requirements needed to perform its intended function." It also states, "Substantial equivalence is established as these are the same standards established for the predicate device as well. As the Eclipse meets the required criteria of the standards, its performance may be considered to support substantial equivalence."

Therefore, the table below reflects what can be inferred from the document:

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

The document does not provide details on the specific sample sizes used for each test or the data provenance (country of origin, retrospective/prospective). It only lists the standards followed for the tests.

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)

This information is not applicable to this type of device (hypodermic needle). The ground truth for performance testing of a medical device like this is established through adherence to recognized international and national standards, not by expert consensus on clinical findings.

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

This information is not applicable as the evaluation of this device is based on objective performance testing against established standards, not interpretation of data by multiple readers requiring adjudication.

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

An MRMC comparative effectiveness study is not applicable to this device. This is a physical medical device (cannula/needle), not an AI-powered diagnostic or assistive tool.

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

A standalone algorithm performance study is not applicable as this is a physical medical device, not an algorithm or AI system.

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

The ground truth for this device's performance is based on established national and international standards for medical device manufacturing, sterility, biocompatibility, and functional performance (e.g., needle strength, connection integrity, color coding). These standards define objective, measurable parameters.

8. The sample size for the training set

This information is not applicable as this is a physical medical device and does not involve a training set for machine learning or AI.

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

This information is not applicable as there is no training set for this type of device.

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The Eclipse Treatment Planning System (Eclipse TPS) is used to plan radiotherapy treatments with malignant or benign diseases. Eclipse TPS is used to plan external beam irradiation with photon, electron and proton beams, as well as for internal irradiation (brachytherapy) treatments.

The Varian Eclipse™ Treatment Planning System (Eclipse TPS) provides software tools for planning the treatment of malignant or benign diseases with radiation. Eclipse TPS is a computer-based software device used by trained medical professionals to design and simulate radiation therapy treatments. Eclipse TPS consists of different applications, each used for specific purposes at a different phase of treatment planning.

Eclipse TPS is capable of planning treatments for external beam irradiation with photon, electron, and proton beams, as well as for internal irradiation (brachytherapy) treatments.

The provided document is a 510(k) summary for the Varian Eclipse Treatment Planning System, v16.1. It describes the device and claims substantial equivalence to a predicate device (Eclipse Treatment Planning System v16.0). However, it does not contain the detailed information necessary to fully answer all aspects of your request regarding acceptance criteria and a study proving the device meets those criteria.

Specifically, the document primarily focuses on software verification and validation, standards conformance, and a comparison to its predicate device. It explicitly states: "No animal studies or clinical tests have been included in this pre-market submission." This indicates that the type of performance data typically associated with studies proving a device meets specific clinical or diagnostic acceptance criteria (e.g., sensitivity, specificity, accuracy against a ground truth) is not present here.

Therefore, many of the requested fields cannot be directly extracted from this document.

Here's a breakdown of what can and cannot be answered based on the provided text:

1. Table of acceptance criteria and reported device performance:

• Acceptance Criteria: Not explicitly stated in terms of quantitative performance metrics (e.g., accuracy thresholds, sensitivity/specificity targets). The acceptance criteria mentioned are general conformance to requirements, specifications, and standards (e.g., IEC 62304, IEC 62366-1, IEC 61217, IEC 62083, IEC 82304-1).
• Reported Device Performance: The document states, "Test results demonstrate conformance to applicable requirements and specifications." and "There were no remaining discrepancy reports (DRs) which could be classified as Safety or Customer Intolerable." This is a general statement about meeting system-level requirements, not specific quantitative performance metrics against a defined ground truth for a clinical indication.

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

• Not specified. The document mentions "Software Verification and Validation Testing" but does not detail the size or nature of any specific test sets used for evaluating clinical performance or dose calculation accuracy with patient data. It also does not mention data provenance (e.g., country of origin, retrospective/prospective). Given that no clinical studies were performed, any "test set" would likely refer to internal engineering tests, rather than a clinical dataset.

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

• Not applicable/Not specified. Since no clinical studies or "patient-like" test sets with established ground truths for diagnostic/clinical accuracy were mentioned, this information is not provided.

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

• Not applicable/Not specified. This is relevant for studies involving human readers or expert consensus on ground truth, which were not part of this submission's provided performance data.

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 was not done. The document explicitly states "No animal studies or clinical tests have been included in this pre-market submission." This type of study would fall under clinical testing.

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

• Not explicitly stated in terms of clinical performance. The "Software Verification and Validation Testing" implies testing of the algorithm, but the performance endpoints are not reported in clinical terms (e.g., sensitivity, specificity, accuracy of dose calculation compared to a gold standard in patients). The changes (GPU calculation for Acuros PT dose, DECT for proton stopping power, preventing dose calculation for DECT Rho and Z images) are technical enhancements that would have been validated through internal engineering tests for accuracy and consistency, but the specific results of these standalone performance evaluations against clinical ground truth are not provided here.

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

• Not specified for clinical endpoints. For software verification and validation, the "ground truth" would be the expected output or behavior according to system requirements and specifications (e.g., a known correct dose calculation for a phantom, correct image processing results). However, this is not a ground truth related to clinical outcomes or expert consensus on patient data.

8. The sample size for the training set:

• Not applicable/Not specified. Treatment planning systems typically use physics models and algorithms, not machine learning models that require "training sets" in the conventional sense. While there might be internal data used for calibration or model development, it's not referred to as a "training set" in this context, nor is its size provided.

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

• Not applicable/Not specified. As there's no mention of a traditional machine learning "training set," this information is not relevant to the provided document.

In summary, the provided FDA 510(k) summary focuses primarily on software development practices, adherence to standards, and a comparison demonstrating substantial equivalence to a predicate device based on non-clinical testing. It explicitly states the absence of animal or clinical studies, meaning the type of performance evaluation you're asking about (related to clinical accuracy against ground truth using patient data) was not part of this specific submission.

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The Eclipse Treatment Planning System (Eclipse TPS) is used to plan radiotherapy treatments with malignant or benign diseases. Eclipse TPS is used to plan external beam irradiation with photon, electron and proton beams, as well as for internal irradiation (brachytherapy) treatments.

The Varian Eclipse™ Treatment Planning System (Eclipse TPS) provides software tools for planning the treatment of malignant or benign diseases with radiation. Eclipse TPS is a computer-based software device used by trained medical professionals to design and simulate radiation therapy treatments. Eclipse TPS consists of different applications, each used for specific purposes at a different phase of treatment planning.

Eclipse TPS is capable of planning treatments for external beam irradiation with photon, electron, and proton beams, as well as for internal irradiation (brachytherapy) treatments.

The provided text describes the 510(k) summary for the Eclipse Treatment Planning System v16.0. It primarily focuses on demonstrating substantial equivalence to a predicate device (Eclipse Treatment Planning System v15.6) through software verification and validation, rather than presenting a performance study with acceptance criteria for a novel AI/ML-driven medical device.

Therefore, many of the requested details such as a table of acceptance criteria, sample sizes for test sets, data provenance, number of experts for ground truth, adjudication methods, MRMC studies, standalone AI performance, and training set details are not applicable or not provided in this specific document.

This document outlines a regulatory submission for a software update to an existing medical device, not a new AI-powered diagnostic or treatment planning system that would typically undergo the extensive validation described in your prompt. The "performance data" section primarily refers to software verification and validation testing, not clinical performance or accuracy in a diagnostic or predictive sense.

Here's what can be extracted based on the provided text, and where information is not available:

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

• Not explicitly provided as acceptance criteria for AI model performance. The document states: "Test results demonstrate conformance to applicable requirements and specifications." This implies compliance with software requirements and design specifications, not performance on clinical metrics of accuracy or effectiveness in the way an AI diagnostic tool would be evaluated.
• The "performance data" discussed is related to software verification and validation against requirements, not diagnostic accuracy or clinical impact.

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

• Not applicable/Not provided. The document states: "No animal studies or clinical tests have been included in this pre-market submission." This indicates that the "test set" was for software testing and validation, not for evaluating performance on patient data. Therefore, details like data provenance or retrospective/prospective nature are not relevant to this submission.

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

• Not applicable. Since no clinical or patient-data-based studies were performed for performance evaluation in the context of diagnostic accuracy, there was no need for expert-established ground truth on a test set.

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

• Not applicable. As no clinical performance study involving human interpretation was conducted to establish ground truth.

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 comparative effectiveness study was not done. The submission explicitly states "No animal studies or clinical tests have been included in this pre-market submission."

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

• Not explicitly stated as a separate standalone performance study in the context of an AI algorithm. The device is a "Treatment Planning System" which is a software tool used by trained medical professionals. Its "performance" is evaluated by its conformance to software specifications and safety, not as a standalone AI diagnostic tool.

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

• Not applicable for clinical ground truth. The "ground truth" for the software validation would be the functional requirements and design specifications that the software was tested against.

8. The sample size for the training set

• Not applicable. This submission is for a software update to an existing treatment planning system, not for a new AI/ML model that requires a dedicated training set. The changes described are feature introductions and enhancements, not an AI model that learns from large datasets.

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

• Not applicable. For the same reason as #8.

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