Pivotal study: 475 evaluable patients from six sites in the United States.
>1 (expert clinicians)
Indications for Use
The EarliPoint System is indicated for use in specialized developmental disabilities centers as a tool to aid clinicians in the diagnosis and assessment of Autism Spectrum Disorder (ASD) for patients ages 16 months through 30 months.
Device Story
EarliPoint System aids clinicians in diagnosing ASD in children aged 16-30 months. Device uses remote eye-tracking hardware to capture patient visual responses to age-appropriate social videos. AI software analyzes eye-tracking data to provide an ASD diagnosis and calculates three severity indices (Social Disability, Verbal Ability, Non-verbal Ability) that proxy ADOS-2 and Mullen scales. Used in specialized developmental disabilities centers; operated by clinicians. Output presented to families by clinicians to inform clinical decision-making. Benefits include objective, non-invasive assessment tool for early ASD identification.
Clinical Evidence
Prospective, double-blind, multi-center pivotal study (n=475 evaluable) compared EarliPoint to expert clinician diagnosis (gold standard). Primary endpoint: sensitivity and specificity. Results: 71% sensitivity (95% CI: 64.6-76.9%) and 80.7% specificity (95% CI: 75.3-85.4%). In the 'Certain Dx' subgroup (n=335), sensitivity was 78.0% and specificity 85.4%. Repeatability and reproducibility study confirmed device reliability, with subject-to-subject variability being the primary source of variance. No serious adverse events reported.
Technological Characteristics
System includes patient console with remote eye-tracking (120 Hz sampling, 0.5° accuracy) and operator console. Uses Near-Infrared (NIR) LED light source conforming to IEC/EN 62471. Class I electrical safety, Type B applied part. Complies with IEC 60601-1 and IEC 60601-1-2. Software compliant with ISO 62304. AI-based analysis of eye-tracking data.
Indications for Use
Indicated for use in specialized developmental disabilities centers as a tool to aid clinicians in the diagnosis and assessment of Autism Spectrum Disorder (ASD) for patients ages 16 months through 30 months.
Regulatory Classification
Identification
A pediatric Autism Spectrum Disorder diagnosis aid is a prescription device that is intended for use as an aid in the diagnosis of Autism Spectrum Disorder in pediatric patients.
Special Controls
In combination with the general controls of the FD&C Act, the pediatric Autism Spectrum Disorder diagnosis aid is subject to the following special controls:
- (1) Clinical performance testing must demonstrate that the device performs as intended under anticipated conditions of use, including an evaluation of sensitivity, specificity, positive predictive value, and negative predictive value using a reference method of diagnosis and assessment of patient behavioral symptomology.
- (2) Software verification, validation, and hazard analysis must be provided. Software documentation must include a detailed, technical description of the algorithm(s) used to generate device output(s), and a cybersecurity assessment of the impact of threats and vulnerabilities on device functionality and user(s).
- (3) Usability assessment must demonstrate that the intended user(s) can safely and correctly use the device.
- (4) Labeling must include:
- (i) Instructions for use, including a detailed description of the device, compatibility information, and information to facilitate clinical interpretation of all device outputs:
- (ii) A summary of any clinical testing conducted to demonstrate how the device functions as an interpretation of patient behavioral symptomology associated with Autism Spectrum Disorder. The summary must include the following:
- (A) A description of each device output and clinical interpretation;
- (B) Any performance measures, including sensitivity, specificity, positive predictive value (PPV) and negative predictive value (NPV);
- (C) A description of how the cut-off values used for categorical classification of diagnoses were determined; and
- (D) Any expected or observed adverse events and complications.
(iii)A statement that the device is not intended for use as a stand-alone diagnostic.
*Classification.* Class II (special controls). The special controls for this device are:(1) Clinical performance testing must demonstrate that the device performs as intended under anticipated conditions of use, including an evaluation of sensitivity, specificity, positive predictive value, and negative predictive value using a reference method of diagnosis and assessment of patient behavioral symptomology.
(2) Software verification, validation, and hazard analysis must be provided. Software documentation must include a detailed, technical description of the algorithm(s) used to generate device output(s), and a cybersecurity assessment of the impact of threats and vulnerabilities on device functionality and user(s).
(3) Usability assessment must demonstrate that the intended user(s) can safely and correctly use the device.
(4) Labeling must include:
(i) Instructions for use, including a detailed description of the device, compatibility information, and information to facilitate clinical interpretation of all device outputs; and
(ii) A summary of any clinical testing conducted to demonstrate how the device functions as an interpretation of patient behavioral symptomology associated with Autism Spectrum Disorder. The summary must include the following:
(A) A description of each device output and clinical interpretation;
(B) Any performance measures, including sensitivity, specificity, positive predictive value (PPV) and negative predictive value (NPV);
(C) A description of how the cutoff values used for categorical classification of diagnoses were determined; and
(D) Any expected or observed adverse events and complications.
(iii) A statement that the device is not intended for use as a stand-alone diagnostic.
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June 8, 2022
Image /page/0/Picture/1 description: The image shows the logo of the U.S. Food and Drug Administration (FDA). The logo consists of two parts: the Department of Health & Human Services logo on the left and the FDA logo on the right. The FDA logo features the letters "FDA" in a blue square, followed by the words "U.S. FOOD & DRUG" in blue, with the word "ADMINISTRATION" underneath.
EarliTec Diagnostics, Inc. % Sew-Wah Tay, Ph.D. Regulatory Consultant Libra Medical, Inc. 8401 73rd Ave N. Suite 63 Brooklyn Park, Minnesota 55428
Re: K213882
Trade/Device Name: EarliPoint System Regulation Number: 21 CFR 882.1491 Regulation Name: Pediatric Autism Spectrum Disorder Diagnosis Aid Regulatory Class: Class II Product Code: QPF Dated: December 28, 2021 Received: December 29, 2021
Dear Dr. Sew-Wah Tay:
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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801); 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,
Jay Gupta Assistant Director DHT5A: Division of Neurosurgical, Neurointerventional and Neurodiagnostic Devices OHT5: Office of Neurological and Physical Medicine Devices 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) K213882
Device Name EarliPoint System
Indications for Use (Describe)
The EarliPoint System is indicated for use in specialized developmental disabilities centers as a tool to aid clinicians in the diagnosis and assessment of Autism Spectrum Disorder (ASD) for patients ages 16 months through 30 months.
| Type of Use (Select one or both, as applicable) |
|-------------------------------------------------|
|-------------------------------------------------|
X Prescription Use (Part 21 CFR 801 Subpart D)
| Over-The-Counter Use (21 CFR 801 Subpart C)
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# 510(K) SUMMARY
#### 1 SUBMITTER
### Sponsor/Manufacturer
EarliTec Diagnostics, Inc. 755 Commerce Drive, Suite 700 Decatur, GA 30030
### Correspondent
Sew-Wah Tay, PhD Regulatory Consultant, Libra Medical Inc. Phone: 612-801-6782 Email: swtay@libramed.com
Date prepared: March 17, 2022
#### 2 DEVICE
| Name of Device: | EarliPoint System |
|-----------------------|----------------------------------------------------|
| Common or Usual Name: | Pediatric Autism Spectrum Disorder diagnostic tool |
| Regulation Number: | 21 CFR 882.1491 |
| Regulatory Class: | II |
| Product Code: | OPF |
#### PREDICATE DEVICE 3
Predicate Device: Cognoa ASD Diagnosis Aid (DEN200069)
Technological Reference: 2D VOG - Video-Oculography (K972243)
#### DEVICE DESCRIPTION 4
The EarliPoint System is a medical device for diagnosis of Autism Spectrum Disorder (ASD) in children.
EarliPoint System consists of the following:
- EarliPoint WebPortal to enter the patient information and for access to the patient -
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evaluation results,
- -EarliPoint Device with eye-tracking capability captures the patient visual response to social information provided in the form of a series of age-appropriate videos
- -Artificial intelligence software analyzes the eye-tracking data and provides a diagnosis for ASD.
- -Eye-tracking data also outputs 3 indices (called EarliPoint Severity Indices) that proxy the ADOS-2 and Mullen validated ASD instruments
- o Social Disability Index correlates and proxies ADOS-2
- Verbal Ability Index correlates and proxies the age equivalent Mullen Verbal o Ability score
- o Non-verbal Ability Index correlates and proxies the age equivalent non-verbal Mullen Ability score
The eye-tracker used in the EarliPoint device has similar capability and specifications to the 2D VOG (K972243 - technological reference). The technical specifications of the 2D VOG are compared to the eye tracking component of EarliPoint.
| Description | 2D VOG<br>(K972243) | EarliPoint Eye Tracking Sub-system |
|-----------------------------------------------------------|-----------------------------------------------|-------------------------------------------------------------------------------------------------------------------|
| Wavelength | Near-Infrared<br>Spectrum | Near-Infrared Spectrum |
| Intensity | Undefined but likely<br>meets IEC/EN<br>62471 | Conforms to safety limit for continue use per<br>IEC/EN 62471<br>(Photobiological safety of lamps/lamp<br>system) |
| Light source | IR LED | IR LED |
| Duration of<br>Use/Exposure | ~15 minutes | ~15 minutes |
| Accuracy/Precision<br>for intended use<br>(gaze position) | Unknown | 0.5° |
| Recording<br>Measurement | 2D Eye Movements | 2D Eye Movements |
| Video Hardware | Video Camera | Video Camera |
| Mounting Hardware | Mobile eye tracking<br>(Face Mask) | Remote eye-tracking system<br>(Display Screen) |
Table 1. Eye Tracking Technical Specifications
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| Description | 2D VOG<br>(K972243) | EarliPoint Eye Tracking Sub-system |
|--------------------|---------------------|------------------------------------|
| Processor Hardware | Personal computer | Personal computer |
| Sampling Rate | Undefined | 120 Hz |
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#### റ INDICATIONS FOR USE
The EarliPoint System is indicated for use in specialized developmental disabilities centers as a tool to aid clinicians in the diagnosis and assessment of ASD patients ages 16 months through 30 months.
#### 6 PERFORMANCE DATA
The following performance data were provided in support of the substantial equivalence determination.
#### 6.1 Mechanical/electrical safety and electromagnetic compatibility (EMC)
Mechanical/electrical safety and EMC testing were conducted on the EarliPoint device consisting of the patient console and the operator console for patient behavioral tracking. The EarliPoint device is classified as Class I for protection against electric shock with Type B applied part and is intended for continuous mode of operation. Compliance testing shows that the EarliPoint device complies with all the applicable tests of IEC 60601-1 standard for mechanical/electrical safety and the IEC 60601-1-2 standard for EMC.
#### 6.2 Software Verification and Validation Testing
Software verification and validation testing were conducted and documentation was provided as recommended by FDA's Guidance for Industry and FDA Staff, "Guidance for the Content of Premarket Submissions for Software Contained in Medical Devices." The software for this device was considered as a "minor" level of concern as a failure or latent flaw in the software is unlikely to result in injury.
#### 6.3 Clinical Studies
The safety and effectiveness of the EarliPoint System in diagnosing the presence ASD was evaluated in a pivotal study where patients were diagnosed by the device as well and expert clinicians (reference standard).
In addition, a repeatability and reproducibility study showed that the EarliPoint diagnosis is reliable and consistent. The pivotal study showed that the device is safe and effective as a tool to assist clinician in diagnosing young children for ASD.
#### 6.3.1 Pivotal Study Design
The pivotal study was a prospective, double-blind, multi-center, within-subject comparison where 500 patients from six sites in the United States were enrolled, of which 475 were evaluable for primary and secondary endpoint analysis and 25 patients had missing data of the device or the control diagnosis (standard of care).
All patients were evaluated for ASD by both the EarliPoint system and by expert clinician diagnosis (current best practice for diagnosis of ASD) to evaluate the sensitivity and specificity of the EarliPoint System diagnosis relative to the expert clinical diagnosis. The study also correlated the three EarliPoint Severity Indices of social disability, verbal ability and nonverbal ability against the corresponding expert clinical instruments of ADOS-2 and Mullen.
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The pivotal study showed that the EarliPoint device was safe and effective in the diagnosis of ASD in children. There was no reported serious adverse event related to the use of the EarliPoint system and, like the CanvasDx device study, there was evidence of EarliPoint device performance differences across subjects based on sex, race/ethnicity.
When compared to the predicate. CanvasDx, the sensitivity and specificity of the EarliPoint device are higher than the CanvasDx and hence the two devices are substantially equivalent.
| Population | Sensitivity<br>Mean (n/N)<br>95% CI | Specificity<br>Mean (n/N)<br>95% CI |
|--------------------------------------------------------------------------------|-------------------------------------|-------------------------------------|
| CanvasDx (mITD)<br>N=425 | 51.6% (63/122)<br>42.8% - 60.5% | 18.5% (56/303)<br>14.3% - 23.3% |
| EarliPoint (mITD)<br>N=475 | 71% (157/221)<br>64.6% - 76.9% | 80.7% (205/254)<br>75.3% - 85.4% |
| EarliPoint CertainDx<br>(Clinicians are Certain of<br>Diagnosis only)<br>N=335 | 78.0% (117/150)<br>70.5%, 84.3% | 85.4% (158/185)<br>79.5% - 90.2% |
The primary effectiveness endpoint was compared to the gold standard of the expert clinician diagnosis. Three populations were evaluated for the primary endpoint: mITD (n=475, all patients with evaluable data), Certain Dx Population (n=335, patients with expert clinician diagnosis with certainty rating > 80%), and Uncertain Dx Population (n=140, patients with expert clinician diagnosis with certainty rating ≤ 80%). As expected, the best results were obtained when the ground truth (clinicians' reference diagnoses) were more certain.
For the subgroup of Uncertain Dx population, given the uncertain nature of the expert diagnosis, the EarliPoint system showed low specificity and sensitivity with mean specificity of 68.1% (p=0.69) and mean sensitivity of 56.3% (p=1.00).
#### 6.3.2 Repeatability and Reproducibility Study
In the EarliPoint repeatability and reproducibility study, the variance attributable to the device is 6.18 % repeatability and 0% for reproducibility assuming no subject and device interaction. Majority of the variance is a result of subject-to-subject variability.
#### 7 PREDICATE COMPARISON
#### 7.1 Intended Use Comparison
The EarliPoint system has the same intended use as the Cognoa ASD Diagnosis Aid (DEN200069). Both are intended for non-invasive objective evaluation of the ASD status of a child by using software to analyze the child's behavior in response to external stimuli. Both are intended for use in to evaluate young children.
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While the indication for use of the devices differs slightly, the intended use is the same. The EarliPoint system diagnostic tool outputs definite diagnosis of positive ASD or negative ASD for each and every patient while the CanvasDx device outputs an actual diagnosis of ASD or non-ASD in only 32% of the time. In 68% of the time, the CanvasDx output an indeterminate diagnosis. Hence the CanvasDx is indicated as a screening aid for diagnosis of ASD. Results from both devices are intended to be presented to the patient's family by a trained clinician.
While the indications for use statements are not identical, we believe that the intended use is substantially equivalent for the purpose of the device classification of the EarliPoint system as a Class II device. Hence the EarliPoint System met the first criteria for substantial equivalence
| Description | Subject Device:<br>EarliPoint System | Predicate Device:<br>Cognoa ASD Diagnosis Aid<br>(DEN200069) | Technological<br>Reference<br>Sensomotoric Eye<br>Tracker<br>(K972243) |
|--------------------------------------|-------------------------------------------------------------------------------------------------------------------------------|--------------------------------------------------------------------------------------------------------------------------------------------------------------------------|------------------------------------------------------------------------|
| Data collection | Patient console with eye-<br>tracking technology for data<br>collection and software<br>analysis for diagnosis of ASD. | Data collected using normal<br>commercially available camera<br>to videotaped behavior | Similar eye-tracking<br>technology for data<br>collection |
| ASD diagnosis | Use software algorithm to<br>analyze the eye tracking data<br>and compare it to data model<br>of normative data for diagnosis | Software analysis of video,<br>behavior assessments<br>questionnaires (parent, video-<br>based expert, and healthcare<br>professional) to generate<br>assessment of ASD. | NA |
| Electrical safety<br>testing | Meets electrical safety<br>standards per IEC 60601-1<br>2005:2012 | Not applicable as this is<br>software as a medical device | Met all electrical and<br>LED safety testing |
| Software | Compliant to ISO 62304 | Compliant to ISO 62304 | NA |
| Clinical performance<br>data for ASD | Pivotal data available to show<br>safety and effectiveness | Pivotal data available to show<br>safety and effectiveness | NA |
| Risk level of the<br>device | Low risk device, non-invasive | Low risk device | Low risk device |
#### 7.2 Technological Characteristics Comparison With The Predicate CanvasDx Device
The principle technological characteristic for both the subject and predicate device (CanvasDx) is digitizing the patient's reaction to social stimuli in the form of video and using software to analyze the child's reaction for the evaluation of ASD. Both devices are non-invasive and are of low risk. The eye-tracker is technological similar to the 2D VOG eye-tracker which have
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decades of safe use so there are no new safety questions.
The EarliPoint device analyzes the patient reaction to social stimuli by tracking the patient's eye movement when stimulated by a series of videos of social scenes while the predicate analyzes the videos of the patient's reaction when placed in a socially stimulating setting. The CanvasDx device analyzes the patient reaction to social stimuli by taking a video of the patient's and analyzing the video in combination with assessments from the caregiver and primary care giver after digitizing the responses.
Both devices use artificial intelligence software to analyze the patient's reaction to provide an ASD diagnosis or assist in the diagnosis for ASD.
At a high level. the subject and predicate devices are based on the following same technological elements:
- . Collection of digital information of patient's reaction when stimulated socially.
- Software analysis of patient physical reactions for evaluation of ASD. ●
Scientific data in the form of clinical performances of the device show that any technological differences between the subject and predicate devices do not raise new or different concerns of safety and efficacy.
#### 7.3 Substantial Equivalence Conclusion
Based on the same intended use, technological characteristics, and the summary of data submitted, the EarliPoint device is found to be substantially equivalent to the predicated device. Safety and performance test data including bench testing to IEC 60601-1 (2012) and comprehensive assessment including clinical data, demonstrated the device safety and effectiveness as intended without raising new and different questions of safety and effectiveness.
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.
