MEDO-Thyroid

K203502 · Medo DX Pte. , Ltd. · QIH · Apr 23, 2021 · Radiology

Device Facts

• Record ID: K203502
• Device Name: MEDO-Thyroid
• Applicant: Medo DX Pte. , Ltd.
• Product Code: QIH · Radiology
• Decision Date: Apr 23, 2021
• Decision: SESE
• Submission Type: Traditional
• Regulation: 21 CFR 892.2050
• Device Class: Class 2
• Attributes: AI/ML, Software as a Medical Device

Intended 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.

Device Story

Cloud-based SaMD; assists radiologists in thyroid ultrasound assessment. Inputs: DICOM ultrasound images (2D, 2D Cine, 3D). Processing: Machine learning techniques for semi-automatic landmark placement; volume measurement of thyroid lobes and nodules; TI-RADS classification (user-input based). Outputs: Visualized images, quantitative measurements, examination reports. Used in clinical settings by radiologists/qualified users. Benefits: Streamlined workflow; standardized quantification and documentation of thyroid nodules.

Clinical Evidence

Bench testing only. Performance evaluated using images from Philips, GE, and Siemens ultrasound systems. Primary endpoints: thyroid lobe and nodule volume measurement accuracy. Results: ICC values for lobe volume (0.972) and nodule volume (0.973) indicate high agreement with reference data. Maximum volume error ranged from 17.4% to 24.6% across subgroups. Nodule size range tested: 0.13 cc to 36.5 cc.

Technological Characteristics

Cloud-based SaMD. DICOM-compliant. Supports 2D, 2D Cine, and 3D ultrasound. Features: semi-automatic landmark placement, volume quantification, TI-RADS classification, report generation. Software developed per IEC 62304:2006/AC:2015. Machine learning-based analysis.

Indications for Use

Indicated for adult patients (18+ years) to aid in the analysis of thyroid lobes and identification, evaluation, quantification, and documentation of thyroid nodules using ultrasound image data.

Regulatory Classification

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.

Special Controls

*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).

Predicate Devices

• QLAB Advanced Quantification Software (K191647)

Submission Summary (Full Text)

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Image /page/0/Picture/0 description: The image shows the logo of the U.S. Food and Drug Administration (FDA). On the left is the Department of Health & Human Services logo. To the right of that is the FDA logo, which is a blue square with the letters "FDA" in white. To the right of the FDA logo is the text "U.S. FOOD & DRUG ADMINISTRATION" in blue.

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 ([e-mail]) or phone [phone] 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) | |
|-------------------------------------------------------------------------------------|-----------------------------------------------|
| <span style="font-size: 1em;">☑</span> 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)<br>32 Carpenter Street, Singapore 059911 |
| Correspondence<br>Person | Dornoosh Zonoobi |
| Contact Information | 780-991-9462<br>[e-mail] |
| 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 /<br>Function | Subject Device<br>MEDO-Thyroid | Predicate Device<br>QLAB Advanced<br>Quantification (K191647) |
|-----------------------------------------------|-------------------------------------------------------------------------------|------------------------------------------------------------------------------|
| Image input | Complies with DICOM<br>Standard | Complies with DICOM<br>Standard |
| Scan type | 2D, 2D Cine, and 3D<br>Ultrasound (Sing and Multi<br>frame images) | 2D, 2D Cine, and 3D<br>Ultrasound |
| Image display<br>mode | Static | Static |
| Image navigation<br>and manipulation<br>tools | Adjust image brightness and<br>contrast, slice-scroll, pane<br>layout, reset | Adjust image brightness and<br>contrast, slice-scroll, pane<br>layout, reset |
| Image review | Yes, capable of reviewing all<br>frames of multi-frame<br>(multi-slice) image | Yes |

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| Manual landmark<br>placement | Yes | Yes |
|--------------------------------------------------------------|---------------------------------------------------------------------------------|----------------------|
| Semi-automatic<br>landmark placement | Yes, user-modifiable | Yes, user-modifiable |
| Quantitative<br>analysis | • Volume (thyroid lobes and<br>user-identified thyroid<br>nodules<br>• Distance | • Distance<br>• Area |
| TI-RADS<br>Classification<br>(based on user<br>manual input) | Yes, based on ACR Standard<br>guidelines and user manual<br>input | 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.