ICC 0.972 (95% CI 0.969-0.975); Maximum % Volume Error 21.7% (95% CI 19.0-24.8)
—
—
—
—
Thyroid Nodule Volume
—
—
ICC 0.973 (95% CI 0.971-0.975); Maximum % Volume Error 22.9% (95% CI 20.0-26.0)
—
—
—
—
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.
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).
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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 (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) | |
|-------------------------------------------------------------------------------------|-----------------------------------------------|
| <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>dornoosh@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 /<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.
Two short videos show you everything — or skip straight to the written tutorial if you'd rather read. You can reopen this any time from the Tutorial button in the top bar.
Part 1 — Search, results, and everyday workflows 16 min
Part 2 — Embeddings: the galaxy map 3 min
1. Search: exact and fuzzy
Type a phrase like "coronary artery calcification" into the search box. You get two kinds of results. Exact results match the literal phrase — prefix searches work ("coronary artery calcificati") but suffix searches do not. Fuzzy results match on the meaning and intent of your phrase rather than the exact words, and are sorted by relevance score. Hover over the Exact or Fuzzy badge on any row to see exactly why it matched.
Use the checkboxes above the results to narrow: SaMD keeps only software-only devices, AI / ML keeps only devices with AI.
Exact vs. fuzzy search: what's the difference?
Exact matches on the literal phrase (prefix search works, suffix does not). Fuzzy matches on the meaning and intent of the phrase rather than the exact words. Hover over the badge on any row to see why it matched.
You search "coronary artery calcification" and want only software devices with AI. What two filters do you apply?
Narrow by SaMD (software-only devices), then narrow by AI/ML (devices with AI).
2. The results table
Scroll right in the results table. The intended use is extracted for you — no need to open the PDF. The device story gives a high-level snapshot of what the device does and how it's used. The AI Performance sub-table shows each output name, acceptance criteria, observed values, and development/test dataset descriptions — the same format Innolitics uses for regulatory strategy outputs, and the fastest high-level fingerprint of an AI device. It is AI-generated but has been very reliable in practice.
Where do you find a device's intended use without opening the PDF?
Scroll right in the search results table. The intended use column is extracted for you; no need to dig into the 510(k) summary PDF.
What does the AI Performance sub-table show, and why is it useful?
Output name, acceptance criteria, observed values, development dataset description, and test dataset description. It's the same format we use for regulatory strategy output and Fast 510(k) input, and the fastest high-level fingerprint of an AI device. AI-generated but reliable in practice.
3. Judging fuzzy relevance
Fuzzy results trail off in relevance as you scroll. Use three signals to decide how far down to go: the fuzzy badge explanations, the intended use column, and whether your target output (e.g., Cobb angle) still appears in the AI Performance sub-table. Once it stops appearing, you're past the relevant zone. A top hit with a low score (~0.4) and a stretched explanation is a hint the closest predicates are far away — the project may be headed for De Novo. Note the fuzzy search is a pattern match: it doesn't handle negation ("not") well, and hardware devices can appear — filter by SaMD/AI ML to cut them.
How do you judge how far down fuzzy search results to go?
Use the relevancy signals: the fuzzy badge explanations, the intended use column, and whether the target output (e.g., Cobb angle) still appears in the AI Performance sub-table. Once it stops appearing, results are trailing off in relevancy.
4. Device detail page: chat and citations
Click a device name to open its detail page: device facts on the left, a chat window on the right. Ask something like "Describe the training data". The answer carries little citation bubbles — click one to jump to the highlighted passage in the source PDF, so you can verify every AI answer against the document. There's also a Download PDF button for sharing.
How do you verify an AI chat answer on the device detail page?
Click the citation bubbles to jump to the relevant highlight in the source document.
Reading rule for every project: how many summaries do you read in full?
At least the three most relevant 510(k) or De Novo summaries, in full. After that, use targeted chat questions to confirm your memory quickly. The tool supports this professional habit — it doesn't replace it.
5. Side-by-side comparison
Select multiple rows in the results table (aim for under ~10), then open the PDF Viewer tab. Ask one question — it goes to all selected devices in parallel, each with citations. This is the fastest way to compare and contrast devices: training data, PCCP scope, how they handled adding new scanners, and so on.
What does the side-by-side PDF viewer mode do?
Select multiple devices, open the PDF viewer tab, and ask one question (e.g., "Describe the training data"). It queries all selected devices simultaneously with citations, so you can compare and contrast quickly.
6. Collections
With rows selected, go to the Collections tab and create a labeled collection (e.g., "Cobb Angle Project"). Reload that selection any time — before a client call, pull up the collection and ask questions across all of its devices at once.
How do you save a set of selected devices for later use?
Select the rows, go to the Collections tab, and create a labeled collection (e.g., "Cobb Angle Project"). You can reload the selection anytime and carry it into the PDF viewer and other tabs that support selections.
7. Product codes and the regulations tree
Click a product code in the results to jump to it in the regulations tree — identification text, sibling product codes, and devices you can open in a PDF viewer on the right. Click a regulation number to see its identification, special controls, and related product codes. You can also search by product code or regulation number at the top of the tree. Always read the special controls if any exist for your device — it broadens your search and sharpens pre-kickoff research.
What can you do from the regulations tree view?
Browse product codes and regulation numbers, read the identification text and special controls, browse sibling product codes, open device PDFs on the right, and search by product code or regulation number at the top of the tree.
8. Chart view
Click Show Chart and segment by regulation number (or product code) to see which regulations dominate your result set. Clicking a regulation takes you into the regulations tree. Great for spotting that most matches are, say, hardware laparoscopic devices — a cue to go back and filter.
How do you see which regulations dominate a search result set?
Click "Show Chart" and segment by Regulation Number. Clicking a regulation takes you to the regulations tree.
9. The predicate graph
Open the Predicates tab for a family-tree view of predicate relationships. Click a node to trace its parents and children; selections from search carry over pre-selected. Commonly predicated devices are worth reading — a lot of people predicated them for a reason. The visual lineage is also handy on client calls, e.g. to show how a predicate family evolved and justify why your predicate still holds.
In the predicate graph, why are commonly predicated devices worth reading?
A lot of people predicated them for a reason. Clicking a node traces parents and children, and selections from search carry over pre-selected.
10. Embeddings: the galaxy map
The Embeddings tab plots every matching document in a 2-D "galaxy map" where semantically similar devices cluster together. Hover or click clusters to explore, and let AI label the clusters for you. Embeddings beat product codes for grouping: two devices can carry different product codes (LLZ vs. QIH) yet do the same thing — the embedding captures the meaning of the intended use and device story. This is also exactly how retrieval-augmented generation (RAG) works under the hood, and it makes a great visual on client calls.
Try it yourself
Head to the search page and work through a few of these AI/ML fuzzy searches to build intuition: perivascular fat on CT · aortic valve calcification opportunistic screening on noncontrast CT · breast cancer prediction on digital pathology slides · autism detection · gestational age prediction · a hearing aid that can also detect a pulse · foundation model based analysis of ECG · large language models · penetration test. Watch how the relevance scores, intended use, and AI Performance tables tell you when results stop being meaningful.