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510(k) Data Aggregation
(264 days)
The Trinias is an angiographic X-ray system which is used for diagnostic imaging and interventional procedures. The Trinias is intended to be used for cardiac angiography, neurovascular angiography, abdominal angiography, peripheral angiography, rotational angiography, multi-purpose angiography and whole body radiographic/fluoroscopic procedures
Trinias is an interventional fluoroscopic x-ray system which uses digital x-ray receptor panels for image acquisition. The system has been modified to include new image enhancement software feature called "SCORE Opera." This new feature applies AI (deep learning technology) filter technology to enable efficient noise suppression and contrast enhancement, and to improve the visibility of devices that are generally difficult to achieve under low dose conditions, catheters for example.
The provided FDA 510(k) clearance letter and its associated summary for the "Trinias X-Ray System" (K252099) offer some details about the device's enhanced features and the studies conducted. However, it does not contain enough specific information to fully describe the acceptance criteria and the study that proves the device meets those criteria in the comprehensive manner requested.
Specifically, the document states:
- "The software was then subjected to non-clinical testing. Summary of non-clinical testing: We provided these detailed non-clinical test reports: The SCORE Opera Development process of leaning model AND a 2D Image Quality Evaluation Report."
- "Clinical testing: A clinical image quality study was conducted. The objective of the study was to evaluate the clinical image quality of X-ray images processed by the Trinias system's AI algorithm. The study aims to confirm that the AI-enhanced images (AI-ON) maintain diagnostic quality compared to standard image processing (AI-OFF). This assessment is submitted to support the determination of substantial equivalence. The results confirm that the AI-ON processing frequently provides improved visibility of interventional devices and vessels."
While these passages indicate that studies were performed, they lack crucial quantitative and qualitative details required to answer the specific questions below. The FDA 510(k) summary is generally an abbreviated public document; more detailed information would typically be found in the full 510(k) submission itself (which is not provided here).
Therefore, I will extract and present what can be deduced from the provided text, and explicitly state when information is missing.
Acceptance Criteria and Device Performance Study (K252099 - Trinias X-Ray System)
1. Table of Acceptance Criteria and Reported Device Performance
| Criterion Description (Inferred from Study Objective) | Acceptance Criteria (Not explicitly stated in the provided text, but inferred goal) | Reported Device Performance (From "Clinical Image Quality Study") |
|---|---|---|
| Clinical Image Quality: Diagnostic Quality | AI-enhanced images (AI-ON) must maintain diagnostic quality compared to standard image processing (AI-OFF). | "The results confirm that the AI-ON processing frequently provides improved visibility of interventional devices and vessels." |
| Clinical Image Quality: Visibility of Devices | Specific metric or threshold not stated. (Implied: Improve or at least not degrade visibility) | "Frequently provides improved visibility of interventional devices and vessels." |
| Noise Suppression & Contrast Enhancement | Specific metrics or thresholds not stated in document. | AI (deep learning technology) filter applied "to enable efficient noise suppression and contrast enhancement." |
| Safety and Effectiveness | Not to present any new issues of safety and effectiveness compared to predicate. | "Does not present any new issues of safety and effectiveness." "Performs as well as or better than our predicate." |
Missing Information: The document does not provide specific, quantifiable acceptance criteria (e.g., "AI-ON images must achieve a minimum diagnostic rate of X%," or "visibility scores must improve by Y points on a Z-point scale"). The reported performance is qualitative and comparative.
2. Sample Size Used for the Test Set and Data Provenance
- Sample Size for Test Set: Not specified. The document mentions "A clinical image quality study was conducted" but provides no details on the number of images or cases included in this study.
- Data Provenance: Not specified. The country of origin of the data (e.g., images) used in the clinical study is not mentioned. It is unclear if the data was retrospective or prospective.
3. Number of Experts Used to Establish Ground Truth for the Test Set and Qualifications
- Number of Experts: Not specified.
- Qualifications of Experts: Not specified.
4. Adjudication Method for the Test Set
- Adjudication Method: Not specified. No information is given on how discrepancies among experts (if multiple were used) were resolved or how the final "ground truth" for diagnostic quality and visibility was established.
5. Multi-Reader Multi-Case (MRMC) Comparative Effectiveness Study
- MRMC Study Done: Not explicitly stated as an MRMC study, but a "clinical image quality study" comparing AI-ON and AI-OFF was conducted. This type of study often involves human readers, which aligns with the spirit of an MRMC study, even if not formally named such. The document states, "The study aims to confirm that the AI-enhanced images (AI-ON) maintain diagnostic quality compared to standard image processing (AI-OFF)."
- Effect Size of Human Readers' Improvement with AI vs. Without AI Assistance: Not quantified or reported. The document only qualitatively states that "the AI-ON processing frequently provides improved visibility of interventional devices and vessels." It does not provide any statistical effect size or direct measure of how much human readers improved their diagnostic performance or speed with AI assistance.
6. Standalone (Algorithm Only Without Human-in-the-Loop Performance) Study
- Standalone Study Done: Yes, implicitly. The AI algorithm's function is described as processing images ("AI (deep learning technology) filter technology to enable efficient noise suppression and contrast enhancement"). The "2D Image Quality Evaluation Report" mentioned under non-clinical testing likely includes standalone evaluations of the algorithm's output before human review, though specific metrics are not provided in this summary. The clinical study compared images from AI-ON vs. AI-OFF processing, implying these are the outputs of the standalone algorithm before human interpretation.
7. Type of Ground Truth Used
- Type of Ground Truth: Inferred to be expert consensus or expert interpretation. For a "clinical image quality study" aiming to assess "diagnostic quality" and "visibility of interventional devices and vessels," the ground truth would most typically be established by experienced clinicians (e.g., interventional radiologists/cardiologists) who review and rate the images. The document does not mention pathology, outcomes data, or other objective measures for ground truth.
8. Sample Size for the Training Set
- Sample Size for Training Set: Not specified. The document mentions "The SCORE Opera Development process of leaning model" (likely meaning "learning model"), indicating AI/deep learning training, but no details about the size or characteristics of the training data are provided.
9. How the Ground Truth for the Training Set Was Established
- How Ground Truth Was Established (Training Set): Not specified. For a deep learning model, the training set would require labeled data. The method for generating these labels (ground truth) is not described. This could involve manual annotation by experts, consensus, or other automated/semi-automated methods, but the document is silent on this point.
In summary, while the K252099 document confirms that a clinical image quality study and non-clinical evaluations were performed to support the new "SCORE Opera" AI feature, it lacks specific quantitative acceptance criteria, sample sizes for test and training data, details on expert qualifications, and the formal methodologies for ground truth establishment and adjudication. The reported performance is qualitative, stating "improved visibility" and maintenance of "diagnostic quality."
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(27 days)
The Trinias is an angiographic X-ray system, which is used for diagnostic imaging and interventional procedures. The Trinias is intended to be used for cardiac angiography, neurovascular angiography, abdominal angiography, peripheral angiography, rotational angiography, multi-purpose angiography and whole body radiographic/fluoroscopic procedures.
This notification is for a modification is the addition of the new MH-700 (ceiling mounted C-Arm). This is the functional equivalent of previous C-Arm MH-400. All options declared under K203535 remain available. The Lateral Angiographic C-arm Support MH-700 allows examination of a patient by fluoroscopy or radiography from different angles with the patient kept in a horizontal position in combination with a Frontal C-arm Support MH-600, an X-ray high voltage unit, X-ray tube unit, X-ray image recording unit (FPD), digital angiography system, catheterization table, etc. The range of available digital receptor panels remains unchanged from our predicate K203535. For installation, ceiling strength is important because the total weight of MH-700 exceeds 1200 kg. The MH-700 features faster movement speeds as compared to the previous model, the MH-400.
The provided document is a 510(k) Premarket Notification for a medical device called "Trinias." It focuses on a modification to the device (addition of a new C-Arm model MH-700) and its substantial equivalence to a predicate device (Trinias K203535).
Based on the information provided, no clinical study was conducted to establish acceptance criteria for device performance related to diagnostic accuracy or interventional outcomes with human-in-the-loop or standalone AI performance. This document primarily focuses on demonstrating substantial equivalence through non-clinical testing, compliance with standards, and comparison of technical specifications.
Therefore, many of the specific points requested regarding acceptance criteria and clinical study details cannot be answered from the provided text.
Here's a breakdown of what can be extracted and what cannot:
1. Table of acceptance criteria and the reported device performance:
The document doesn't present "acceptance criteria" in the context of diagnostic performance metrics (e.g., sensitivity, specificity, accuracy) or interventional success rates. Instead, the "performance" described pertains to technical specifications and compliance with safety and electrical standards.
| Feature/Parameter | Acceptance Criteria (Implied: Substantial Equivalence to Predicate, Compliance with Standards) | Reported Device Performance (Modified Trinias) |
|---|---|---|
| Indications for Use | Same as predicate device | Same as predicate: diagnostic imaging and interventional procedures including cardiac, neurovascular, abdominal, peripheral, rotational, multi-purpose angiography, and whole body radiographic/fluoroscopic procedures. |
| Patient Table | Functionally equivalent to predicate | Same as predicate (KS-100, table top can tilt) |
| Biplane C-arm (MH-700) | Safe and effective as predicate MH-400; compliance with standards, faster movement permissible | MH-700 is new, replaces MH-400. Features faster movement speeds. |
| C-arm Rotation Range (LL primary angle) | LAO120 - PA0 | LAO120 - PA0 |
| C-arm Rotation Speed (LL primary angle) | Max 15deg/sec (predicate) | Max 25deg/sec (Faster response) |
| C-arm Rotation Range (RL primary angle) | PA0 - RAO120 | PA0 - RAO120 |
| C-arm Rotation Speed (RL primary angle) | Max 15deg/sec (predicate) | Max 25deg/sec (Faster response) |
| C-arm Rotation Range (LL secondary angle) | CRAN45 - CAUD45 (predicate) | CRAN45 - CAUD45 |
| C-arm Rotation Speed (LL secondary angle) | Max 15deg/sec (predicate) | Max 25deg/sec (Faster response) |
| C-arm Rotation Range (RL secondary angle) | CRAN30°~CAUD30° (predicate) | CRAN45 - CAUD45 |
| C-arm Rotation Speed (RL secondary angle) | Max 15deg/sec (predicate) | Max 25deg/sec (Faster response) |
| SID range/speed | 95cm - 125cm, 8cm/sec max (predicate) | 95cm - 125cm, 10cm/sec max (Faster response) |
| Digital Image Processor | Same as predicate | Same as predicate (DAR-9500f) |
| Digital X-Ray Receptor Panels | No changes; same models and sizes as predicate | Same as predicate (SFD-0808AF, SFD-1212AF, SFD-1612AF with Varex PaxScan panels) |
| X-Ray Generator (Model #, Rated output, Control Method, Voltage, Current, mAs, Time) | Same as predicate | Same as predicate (D150GC-40, 100 kW, 50 kHz Inverter, various kV/mA/mAs/sec specifications) |
| Collimator | Same as predicate | Same as predicate (F-100) |
| US Performance Standard Compliance | Compliance with 21CFR1020.30, 21CFR1020.31, 21CFR2020.32 | Certified to comply with 21CFR1020.30, 21CFR1020.31, 21CFR2020.32 |
| IEC Safety Standards Compliance | Compliance with listed IEC standards | Certified to comply with IEC 60601-1, IEC 60601-1-2, IEC 60601-1-3, IEC 60601-1-6, IEC 60601-2-43, IEC 62366, IEC 62304, EN 60601-1, EN 60601-1-3, EN 60601-1-6, EN 60601-2-43, EN 62304, ANSI/AAMI ES60601-1. |
| Software Validation | Compliance with FDA Guidance: "Content of Premarket Submissions for Software Contained in Medical Devices" | Validated according to FDA guidance issued May 11, 2005. |
| Cybersecurity | Compliance with FDA Guidance: "Content of Premarket Submissions for Management of Cybersecurity in Medical Devices" | Recommendations from guidance issued Oct 2, 2014, were observed and incorporated. |
| Pediatric Information/Labeling | Compliance with FDA Guidance: "Pediatric Information for X-ray Imaging Device Premarket Notifications" | Labeling developed per guidance. Includes reference to Image Gently website. |
| Wireless Technology | Compliance with FDA Guidance: "Radio Frequency Wireless Technology in Medical Devices" | Recommendations from guidance issued August 2013, were incorporated into labeling. |
2. Sample size used for the test set and the data provenance: Not applicable. No clinical test set. The testing was non-clinical (bench and standards testing).
3. Number of experts used to establish the ground truth for the test set and the qualifications of those experts: Not applicable. No clinical test set.
4. Adjudication method (e.g., 2+1, 3+1, none) for the test set: Not applicable. No clinical test set.
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. No MRMC study was done, nor does the device appear to include AI for interpretation or improvement of human reader performance.
6. If a standalone (i.e., algorithm only without human-in-the-loop performance) was done: Not applicable. This device is an X-ray system, not an AI algorithm for standalone performance.
7. The type of ground truth used (expert consensus, pathology, outcomes data, etc.): Not applicable to a clinical performance study. For non-clinical testing, "ground truth" would relate to engineering specifications and performance against established standards, confirmed by third-party laboratories.
8. The sample size for the training set: Not applicable. This document does not describe training of an AI algorithm based on a dataset.
9. How the ground truth for the training set was established: Not applicable.
In summary: The provided 510(k) summary focuses entirely on non-clinical aspects to demonstrate substantial equivalence of a modified X-ray system to its predicate. It explicitly states: "Summary of clinical testing: Not applicable. Clinical testing was not deemed to show substantial equivalence. We relied on non-clinical testing and compliance with standards." Therefore, questions related to clinical study design, performance metrics (like sensitivity, specificity), ground truth, expert readers, or AI-related evaluations cannot be answered from this document.
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(146 days)
The Trinias is an angiographic X-ray system, which is used for diagnostic imaging and interventional procedures. The Trinias is intended to be used for cardiac angiography, neurovascular angiography, abdominal angiography, peripheral angiography, rotational angiography, multi-purpose angiography and whole body radiographic/fluoroscopic procedures.
This notification is for a modified device. The modifications are: Updated user interfaces (wireless mouse, keyboard) A new model of catheterization table A new type of digital system console Additional x-ray tube choices Add alternate choices for the same sizes of digital flat panel detectors An additional size of available flat panel detector (12" x 16") An additional type of control cabinet.
This document is a 510(k) summary for the Shimadzu Trinias angiographic X-ray system. It describes modifications to an existing device and demonstrates substantial equivalence to a predicate device (K123508).
Based on the provided document, the device in question is a medical imaging system (angiographic X-ray system), not an AI/ML-based device. Therefore, the typical acceptance criteria and study designs associated with AI/ML systems (e.g., performance metrics like sensitivity/specificity, multi-reader multi-case studies, ground truth establishment by experts, training/test set provenance) are not applicable here.
The regulatory approval for this device is based on showing substantial equivalence to a previously cleared predicate device, rather than proving performance against specific AI/ML acceptance criteria. The modifications are hardware and software updates to the existing X-ray system.
Here's an analysis of the provided information in the context of device approval, highlighting why AI/ML-specific criteria are not met or relevant:
1. Table of Acceptance Criteria and Reported Device Performance
Not Applicable (for AI/ML performance).
Since this is not an AI/ML device, there are no acceptance criteria related to typical AI/ML performance metrics (e.g., accuracy, sensitivity, specificity, AUC).
The "acceptance criteria" for this submission are compliance with various safety and performance standards for X-ray systems. The reported "performance" is that the modified device meets these standards and is comparable to the predicate.
| Acceptance Criteria (based on standards compliance) | Reported Device Performance |
|---|---|
| Compliance with US Performance Standard 21CFR1020.30, .31, .32 | Device tested and certified to comply. |
| Compliance with IEC 60601-1 (general safety) | Device found to comply. |
| Compliance with IEC 60601-1-2 (EMC) | Device found to comply. |
| Compliance with IEC 60601-1-3 (radiation protection) | Device found to comply. |
| Compliance with IEC 60601-1-6 (usability) | Device found to comply. |
| Compliance with IEC 60601-2-43 (interventional procedures) | Device found to comply. |
| Compliance with IEC 62366 (usability engineering) | Evaluated in accordance with and found to comply. |
| Compliance with IEC 62304 (software life cycle processes) | Evaluated in accordance with and found to comply. |
| Software validation (FDA Guidance May 11, 2005) | Software was validated. |
| Cybersecurity management (FDA Guidance Oct 2, 2014) | Recommendations observed for Wi-Fi and Ethernet. |
| Pediatric Information Labeling (FDA Guidance Nov 2017) | Labeling developed in accordance, includes Image Gently. |
| Wireless Technology Recommendations (FDA Guidance Aug 2013) | Recommendations incorporated into labeling. |
| Safety and effectiveness comparable to predicate device K123508 | "as safe and effective as the predicate device," "few technological differences," "same indications for use." |
2. Sample Size Used for the Test Set and Data Provenance
Not Applicable (for AI/ML test set data).
There is no "test set" in the sense of a clinical image dataset used to evaluate an AI algorithm's diagnostic performance. The testing performed was non-clinical bench and standards testing. This involves engineering tests, electrical safety tests, radiation safety compliance tests, and software validation tests.
The data provenance refers to the origin of the device's design, manufacturing, and testing; it does not refer to clinical image data. The manufacturer is Shimadzu Corporation, based in Kyoto, Japan.
3. Number of Experts Used to Establish the Ground Truth for the Test Set and Qualifications of Those Experts
Not Applicable.
No "ground truth" derived from expert interpretation of medical images was established for this submission, as it's not an AI/ML diagnostic aid. The device's performance is validated against engineering specifications, safety standards, and functional requirements.
4. Adjudication Method for the Test Set
Not Applicable.
Since there's no expert interpretation of a test set, there is no adjudication method.
5. If a Multi-Reader Multi-Case (MRMC) Comparative Effectiveness Study Was Done
No. A MRMC study was not done.
MRMC studies are typically performed for AI/ML diagnostic devices to assess how human reader performance (e.g., radiologists) improves with AI assistance compared to without it. This submission is for an X-ray imaging system itself, not an AI-assisted diagnostic tool.
Therefore, there is no effect size of how human readers improve with AI vs. without AI assistance.
6. If a Standalone (i.e., algorithm only without human-in-the-loop performance) Was Done
No.
This concept applies to AI/ML algorithms that can produce an output independently. The Trinias is an imaging system; its "performance" is its ability to acquire images, comply with safety standards, and function as intended.
7. The Type of Ground Truth Used
Compliance with regulated standards and functional specifications.
The "ground truth" for this device's approval lies in its adherence to international safety standards (e.g., IEC 60601 series, IEC 62304 for software) and U.S. performance standards (21 CFR 1020.30, .31, .32), as well as verification of its mechanical and electrical functions. This is demonstrated through "bench and standards testing" and "proper system operation is fully verified upon installation."
8. The Sample Size for the Training Set
Not Applicable.
This refers to training data for AI/ML models. The Trinias is a hardware and software system. While its internal software components undergo development and testing, there isn't a "training set" in the AI/ML sense.
9. How the Ground Truth for the Training Set Was Established
Not Applicable.
As there is no AI/ML training set, there is no ground truth established for it. Software validation (IEC 62304) and adherence to design specifications guide the software development, but this is distinct from training an AI model.
In summary: The provided document is a 510(k) submission for an updated medical imaging hardware system (X-ray). Its approval focuses on demonstrating substantial equivalence to a predicate device through non-clinical performance and safety testing, and compliance with established regulatory standards. It does not involve AI/ML technology or associated clinical performance studies with diagnostic accuracy endpoints.
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(470 days)
- The Trinias is an Image-intensified fluoroscopic X-ray system, which is used for diagnostic imaging and interventional procedures as described in 21 CFR 892.1650.
- The Trinias is intended to be used for cardiac angiography, abdominal angiography, abdominal angiography, peripheral angiography, rolational angiography, multi-purpose angiography and whole body radiographic filluoroscopic procedures.
- The Trinias is intended to be used for interventional procedures such as invasive cardiology and interventional neuroradiology.
Image-intensified fluoroscopic X-ray system
I am sorry, but the provided text does not contain the detailed information necessary to describe the acceptance criteria and the study that proves the device meets those criteria, as requested.
The document is a 510(k) premarket notification letter from the FDA to SHIMADZU Corporation regarding their "Trinias" image-intensified fluoroscopic x-ray system. It confirms that the device is substantially equivalent to legally marketed predicate devices.
While it mentions the device name, regulation number, regulatory class, and general indications for use, it does not include:
- A table of acceptance criteria and reported device performance.
- Details about sample sizes for test or training sets.
- Data provenance, number/qualifications of experts, or adjudication methods for ground truth.
- Information about MRMC comparative effectiveness studies or standalone algorithm performance.
- Specifics on how ground truth was established for either test or training sets.
The document is primarily a regulatory approval letter, not a technical study report.
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