(144 days)
MEDO-Thyroid is designed to view and quantify ultrasound thyroid image data using machine learning techniques to aid in analysis of thyroid lobes and identify thyroid nodules, including evaluation, quantification and documentation of any such nodule. The device is intended to be used on adult patient images of 18 years or older.
MEDO-Thyroid is a cloud-based standalone software as a medical device (SaMD) that helps qualified users with image-based assessment of thyroid ultrasound images in adult patients of 18 years and older. It is designed to support the workflow by helping the radiologist to evaluate, quantify, and generate reports for thyroid ultrasound images.
MEDO-Thyroid Software takes as an input imported Digital Imaging and Communications in Medicine (DICOM) images from ultrasound scanners and allows users to upload, browse, and view images, measure thyroid lobes and thyroid nodule volumes of single frame and multi-frame ultrasound images, as well as create and finalize examination reports. It provides users with a specific toolset for viewing ultrasound Thyroid images, placing landmarks, and creating reports.
Key features of the software are:
- Single and multi-frame visualization .
- Cross Referencing .
- . Manual and semi-automatic landmark placements
- Thyroid Lobes (left and right) and thyroid nodule volume measurements ●
- TI-RADS Score and Classification (based on user manual input) .
- Report generation ●
Here's a breakdown of the acceptance criteria and the study proving the device meets them, based on the provided text:
Device: MEDO-Thyroid (Automated Radiological Image Processing Software)
Intended Use: To view and quantify ultrasound thyroid image data using machine learning techniques to aid in analysis of thyroid lobes and identify thyroid nodules, including evaluation, quantification and documentation of any such nodule. Intended for adult patients 18 years or older.
1. Table of Acceptance Criteria and Reported Device Performance
The document doesn't explicitly state "acceptance criteria" in a separate section. However, the "Performance Data" section implies that the device's ability to accurately measure thyroid lobe and nodule volume, demonstrated by high Intraclass Correlation Coefficient (ICC) and acceptable maximum percentage volume error compared to reference data, are the key performance metrics. This implies the acceptance criteria were defined by thresholds for these metrics.
| Acceptance Criterion (Implied) | Performance Metric | Reported Device Performance (Overall) |
|---|---|---|
| Thyroid Lobe Volume Measurement Accuracy | Intraclass Correlation Coefficient (ICC) between AI and Reference Data | 0.972 (95% CI 0.969-0.975) |
| Maximum % Volume Error between AI and Reference Data | 21.7% (95% CI 19.0-24.8) | |
| Thyroid Nodule Volume Measurement Accuracy | Intraclass Correlation Coefficient (ICC) between AI and Reference Data | 0.973 (95% CI 0.971-0.975) |
| Maximum % Volume Error between AI and Reference Data | 22.9% (95% CI 20.0-26.0) |
The document also notes the device performed successfully on nodules ranging from 0.13 cc to 36.5 cc, implying the performance is independent of nodule size within this range.
2. Sample Size Used for the Test Set and Data Provenance
The document does not explicitly state the exact sample size for the test set. However, it indicates that the device was trained and tested on images from Philips, GE, and Siemens ultrasound devices. The performance tables (5.7.2 and 5.7.3) show aggregated data for "All" manufacturers, implying the test set includes images from these vendors.
Data Provenance:
- Country of Origin: Not explicitly stated, but the company is based in Singapore with an address in Canada provided in the FDA letter. The ultrasound machines listed (Philips, GE, Siemens) are globally used.
- Retrospective or Prospective: Not explicitly stated. Given that the data is being used for performance analysis and summarized, it is likely retrospective data collected from existing image archives.
3. Number of Experts Used to Establish Ground Truth for the Test Set and Qualifications
The document does not explicitly state the number of experts or their specific qualifications (e.g., "Radiologist with 10 years of experience") for establishing the ground truth. It refers to "Ref. data" for volume measurements, indicating that ground truth was established by a reference method, presumably by experts.
4. Adjudication Method for the Test Set
The document does not explicitly describe an adjudication method (e.g., 2+1, 3+1). The "Ref. data" suggests a single established ground truth measurement for comparison.
5. If a Multi-Reader Multi-Case (MRMC) Comparative Effectiveness Study Was Done
No. The document presents a standalone (algorithm only) performance evaluation against "Ref. data." There is no mention of a human-in-the-loop study or human reader improvement with AI assistance.
6. If a Standalone (i.e., Algorithm Only Without Human-in-the-Loop Performance) Was Done
Yes. The tables 5.7.2 and 5.7.3 directly compare the AI's measurements ("AI") against reference data ("Ref. data") for thyroid lobe and nodule volumes. This represents a standalone performance evaluation of the algorithm.
7. The Type of Ground Truth Used
The type of ground truth used is reference data for volume measurements. This implies that precise volume measurements were obtained by an established, reliable method, likely manual measurements performed by expert clinicians or a highly accurate segmentation method, to serve as the benchmark for the AI.
8. The Sample Size for the Training Set
The document does not specify the exact sample size used for the training set. It states: "MEDO Thyroid-AI has been primarily trained and tested on the Philips, GE and Siemens ultrasound devices."
9. How the Ground Truth for the Training Set Was Established
The document does not explicitly state how the ground truth for the training set was established. It only refers to "Ref. data" for the test set performance evaluation. However, it is a standard practice in machine learning for medical imaging that similar reference data establishment methods (e.g., expert annotations, manual measurements) would be used for training data as well.
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April 23, 2021
MEDO DX Pte. Ltd. Dornoosh Zonoobi CEO and Co-founder 4560 TEC Centre, 10230 Jasper Avenue Edmonton, Alberta T5J4P6 Canada
Re: K203502
Trade/Device Name: MEDO-Thyroid Regulation Number: 21 CFR 892.2050 Regulation Name: Picture Archiving And Communications System Regulatory Class: Class II Product Code: QIH Dated: March 22, 2021 Received: March 24, 2021
Dear Dornoosh Zonoobi:
We have reviewed your Section 510(k) premarket notification of intent to market the device referenced above and have determined the device is substantially equivalent (for the indications for use stated in the enclosure) to legally marketed predicate devices marketed in interstate commerce prior to May 28, 1976, the enactment date of the Medical Device Amendments, or to devices that have been reclassified in accordance with the provisions of the Federal Food, Drug, and Cosmetic Act (Act) that do not require approval of a premarket approval application (PMA). You may, therefore, market the device, subject to the general controls provisions of the Act. Although this letter refers to your product as a device, please be aware that some cleared products may instead be combination products. The 510(k) Premarket Notification Database located at https://www.accessdata.fda.gov/scripts/cdrh/cfdocs/cfpmn/pmn.cfm identifies combination product submissions. The general controls provisions of the Act include requirements for annual registration, listing of devices, good manufacturing practice, labeling, and prohibitions against misbranding and adulteration. Please note: CDRH does not evaluate information related to contract liability warranties. We remind you, however, that device labeling must be truthful and not misleading.
If your device is classified (see above) into either class II (Special Controls) or class III (PMA), it may be subject to additional controls. Existing major regulations affecting your device can be found in the Code of Federal Regulations, Title 21, Parts 800 to 898. In addition, FDA may publish further announcements concerning your device in the Federal Register.
Please be advised that FDA's issuance of a substantial equivalence determination does not mean that FDA has made a determination that your device complies with other requirements of the Act or any Federal statutes and regulations administered by other Federal agencies. You must comply with all the Act's requirements, including, but not limited to: registration and listing (21 CFR Part 807); labeling (21 CFR Part
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801and Part 809); medical device reporting of medical device-related adverse events) (21 CFR 803) for devices or postmarketing safety reporting (21 CFR 4, Subpart B) for combination products (see https://www.fda.gov/combination-products/guidance-regulatory-information/postmarketing-safety-reportingcombination-products); good manufacturing practice requirements as set forth in the quality systems (QS) regulation (21 CFR Part 820) for devices or current good manufacturing practices (21 CFR 4, Subpart A) for combination products; and, if applicable, the electronic product radiation control provisions (Sections 531-542 of the Act); 21 CFR 1000-1050.
Also, please note the regulation entitled, "Misbranding by reference to premarket notification" (21 CFR Part 807.97). For questions regarding the reporting of adverse events under the MDR regulation (21 CFR Part 803), please go to https://www.fda.gov/medical-device-safety/medical-device-reportingmdr-how-report-medical-device-problems.
For comprehensive regulatory information about mediation-emitting products, including information about labeling regulations, please see Device Advice (https://www.fda.gov/medicaldevices/device-advice-comprehensive-regulatory-assistance) and CDRH Learn (https://www.fda.gov/training-and-continuing-education/cdrh-learn). Additionally, you may contact the Division of Industry and Consumer Education (DICE) to ask a question about a specific regulatory topic. See the DICE website (https://www.fda.gov/medical-device-advice-comprehensive-regulatoryassistance/contact-us-division-industry-and-consumer-education-dice) for more information or contact DICE by email (DICE@fda.hhs.gov) or phone (1-800-638-2041 or 301-796-7100).
Sincerely,
For
Thalia T. Mills, Ph.D. Director Division of Radiological Health OHT7: Office of In Vitro Diagnostics and Radiological Health Office of Product Evaluation and Quality Center for Devices and Radiological Health
Enclosure
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Indications for Use
510(k) Number (if known) K203502
Device Name MEDO-Thyroid
Indications for Use (Describe)
MEDO-Thyroid is designed to view and quantify ultrasound thyroid image data using machine learning techniques to aid in analysis of thyroid lobes and identify thyroid nodules, including evaluation, quantification and documentation of any such nodule. The device is intended to be used on adult patient images of 18 years or older.
| Type of Use (Select one or both, as applicable) | |
|---|---|
| ☑ Prescription Use (Part 21 CFR 801 Subpart D) | ☐ Over-The-Counter Use (21 CFR 801 Subpart C) |
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Section 5. 510(k) Summary
5.1. General Information
| 510(k) Sponsor | MEDO DX Pte. Ltd. (O/A MEDO.ai) |
|---|---|
| Address | MEDO DX Pte. Ltd. (O/A MEDO.ai)32 Carpenter Street, Singapore 059911 |
| CorrespondencePerson | Dornoosh Zonoobi |
| Contact Information | 780-991-9462dornoosh@medo.ai |
| Date Prepared | November 20, 2020 |
5.2. Proposed Device
| Proprietary Name | MEDO-Thyroid |
|---|---|
| Common Name | MEDO-Thyroid |
| Classification Name | Automated Radiological Image Processing Software |
| Regulation Number | 21 CFR 892.2050 |
| Product Code | QIH |
| Regulatory Class | II |
5.3. Predicate Device
| Proprietary Name | QLAB Advanced Quantification Software |
|---|---|
| Common Name | K191647 |
| Classification Name | Automated Radiological Image Processing Software |
| Regulation Number | 21 CFR 892.2050 |
| Product Code | QIH |
| Regulatory Class | II |
5.4. Device Description
MEDO-Thyroid is a cloud-based standalone software as a medical device (SaMD) that helps qualified users with image-based assessment of thyroid ultrasound images in adult patients of 18 years and older. It is designed to support the workflow by helping the radiologist to evaluate, quantify, and generate reports for thyroid ultrasound images.
MEDO-Thyroid Software takes as an input imported Digital Imaging and Communications in Medicine (DICOM) images from ultrasound scanners and allows users to upload, browse,
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and view images, measure thyroid lobes and thyroid nodule volumes of single frame and multi-frame ultrasound images, as well as create and finalize examination reports. It provides users with a specific toolset for viewing ultrasound Thyroid images, placing landmarks, and creating reports.
Key features of the software are:
- Single and multi-frame visualization .
- Cross Referencing .
- . Manual and semi-automatic landmark placements
- Thyroid Lobes (left and right) and thyroid nodule volume measurements ●
- TI-RADS Score and Classification (based on user manual input) .
- Report generation ●
5.5. Indications for Use
MEDO-Thyroid is designed to view and quantify ultrasound thyroid image data using machine learning techniques to aid in analysis of thyroid lobes and identify thyroid nodules, including evaluation, quantification and documentation of any such nodule. The device is intended to be used on adult patient images of 18 years or older.
| 5.6. Comparison of Technological Characteristics with the Predicate Device | |||||
|---|---|---|---|---|---|
| ---------------------------------------------------------------------------- | -- | -- | -- | -- | -- |
| Feature /Function | Subject DeviceMEDO-Thyroid | Predicate DeviceQLAB AdvancedQuantification (K191647) |
|---|---|---|
| Image input | Complies with DICOMStandard | Complies with DICOMStandard |
| Scan type | 2D, 2D Cine, and 3DUltrasound (Sing and Multiframe images) | 2D, 2D Cine, and 3DUltrasound |
| Image displaymode | Static | Static |
| Image navigationand manipulationtools | Adjust image brightness andcontrast, slice-scroll, panelayout, reset | Adjust image brightness andcontrast, slice-scroll, panelayout, reset |
| Image review | Yes, capable of reviewing allframes of multi-frame(multi-slice) image | Yes |
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| Manual landmarkplacement | Yes | Yes |
|---|---|---|
| Semi-automaticlandmark placement | Yes, user-modifiable | Yes, user-modifiable |
| Quantitativeanalysis | • Volume (thyroid lobes anduser-identified thyroidnodules• Distance | • Distance• Area |
| TI-RADSClassification(based on usermanual input) | Yes, based on ACR Standardguidelines and user manualinput | No |
| Cross Referencing | Yes | No |
| Report creation | Yes | No |
5.7. Performance Data
Safety and performance of MEDO-Thyroid have been evaluated and verified in accordance with software specifications and applicable performance standards through software verification and validation testing. Additionally, the software validation activities were performed in accordance with IEC 62304:2006/AC:2015 - Medical device software -Software life cycle processes, in addition to the FDA Guidance document, "Guidance for the Content of Premarket Submissions for Software Contained in Medical Devices."
MEDO Thyroid-AI has been primarily trained and tested on the Philips, GE and Siemens ultrasound devices. The device has been tested using images acquired from the following ultrasound machines using high frequency linear transducers as described in Table 5.7.1 (below):
| Ultrasound Manufacturer | Machine |
|---|---|
| Philips | EPIQ 5G |
| Philips | iU22 |
| Philips | CX50 |
| GE | LOGIQE9 |
| Siemens | S2000 |
Table 5.7.1: Breakdown of ultrasound machines used for testing
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Tables 5.7.2 and 5.7.3 (below) provide detailed breakdowns of device performance by ultrasound device subgroups:
| Thyroid Lobe Volume (cc) | |||||
|---|---|---|---|---|---|
| Subgroup | AI | Ref. data | ICC | Maximum % Volume Error | |
| Siemens | 4.27 ± 2.61 | 4.35 ± 2.66 | 0.974 (95% CI 0.967-0.978) | 18.2% (95% CI 12.0-24.0) | |
| Philips | 6.12 ± 3.73 | 5.95 ± 3.57 | 0.963 (95% CI 0.952-0.969) | 21.1% (95% CI 16.5-25.0) | |
| GE | 8.08 ±10.02 | 7.73 ± 9.99 | 0.974 (95% CI 0.969-0.977) | 24.4% (95% CI 20.0-29.5) | |
| All | 6.48 ± 6.54 | 6.29 ± 6.46 | 0.972 (95% CI 0.969-0.975) | 21.7% (95% CI 19.0-24.8) |
Table 5.7.2: Performance Analysis of device on thyroid lobe volume measurement for Ultrasound Device Subgroups
Table 5.7.3: Performance Analysis of device on nodule volume measurement for Ultrasound Device Subgroups
| Thyroid Nodule Volume (cc) | ||||
|---|---|---|---|---|
| Subgroup | AI | Ref. data | ICC | Maximum % Volume Error |
| Siemens | 0.85 ± 1.19 | 0.87 ± 1.22 | 0.978 (95% CI 0.975-0.979) | 17.4% (95% CI 12.0-24.0) |
| Philips | 1.76 ± 2.95 | 1.76 ± 3.07 | 0.972 (95% CI 0.967-0.975) | 23.5% (95% CI 16.5-25.0) |
| GE | 1.83 ± 5.71 | 1.93 ± 6.34 | 0.974 (95% CI 0.969-0.977) | 24.6% (95% CI 20.0-29.5) |
| All | 1.61 ± 3.71 | 1.64 ± 4.02 | 0.973 (95% CI 0.971-0.975) | 22.9% (95% CI 20.0-26.0) |
The performance of the MEDO-Thyroid device has been successfully assessed on a nodule size range between 0.13 cc and 36.5 cc, and is independent of the sizes of nodules being measured.
5.8. Conclusion
Based on the information submitted in this premarket notification, and based on the indications for use, technological characteristics, and performance testing, MEDO-Thyroid raises no new questions of safety or effectiveness and is substantially equivalent to the predicate device in terms of safety, efficacy, and performance.
§ 892.2050 Medical image management and processing system.
(a)
Identification. A medical image management and processing system is a device that provides one or more capabilities relating to the review and digital processing of medical images for the purposes of interpretation by a trained practitioner of disease detection, diagnosis, or patient management. The software components may provide advanced or complex image processing functions for image manipulation, enhancement, or quantification that are intended for use in the interpretation and analysis of medical images. Advanced image manipulation functions may include image segmentation, multimodality image registration, or 3D visualization. Complex quantitative functions may include semi-automated measurements or time-series measurements.(b)
Classification. Class II (special controls; voluntary standards—Digital Imaging and Communications in Medicine (DICOM) Std., Joint Photographic Experts Group (JPEG) Std., Society of Motion Picture and Television Engineers (SMPTE) Test Pattern).