Pivotal study: 475 evaluable patients from six sites in the United States
>1 (expert clinicians)
Social Disability Index
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Pivotal study: 475 evaluable patients from six sites in the United States
>1 (expert clinicians)
Verbal Ability Index
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—
—
—
Pivotal study: 475 evaluable patients from six sites in the United States
>1 (expert clinicians)
Non-verbal Ability Index
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—
—
—
Pivotal study: 475 evaluable patients from six sites in the United States
>1 (expert clinicians)
Indications for Use
The EarliPoint System is intended for use by healthcare providers to objectively diagnose and assess children for ASD using software algorithm to analyze a child's response to an external stimulus in the form of videos.
Device Story
EarliPoint System is a diagnostic aid for Autism Spectrum Disorder (ASD) in children aged 16-30 months. The device uses a near-infrared eye-tracking module to capture a patient's visual response to age-appropriate social videos. An operator monitors the session via a separate module. Eye-tracking data is uploaded to a secure WebPortal where AI software analyzes looking behavior. The system outputs an ASD diagnosis and three severity indices: Social Disability Index (correlating to ADOS-2), Verbal Ability Index, and Non-verbal Ability Index (correlating to Mullen scales). Used in specialized developmental disabilities centers by clinicians, the output assists in clinical decision-making and assessment of ASD symptoms. The device is a compact, portable version of the predicate, maintaining identical diagnostic algorithms and performance specifications.
Clinical Evidence
Pivotal prospective, double-blind, multi-center study (N=500, 475 evaluable) compared device performance against expert clinician diagnosis (reference standard). For the mITD population (N=475), sensitivity was 71% (95% CI: 64.6%-76.9%) and specificity was 80.7% (95% CI: 75.3%-85.4%). For the CertainDx subgroup (N=335), sensitivity was 78.0% (95% CI: 70.5%-84.3%) and specificity was 85.4% (95% CI: 79.5%-90.2%). No serious adverse events were reported.
Technological Characteristics
Near-infrared eye-tracking system (120 Hz sampling rate, 0.5° accuracy). Components include patient console, operator module, and web-portal. Complies with IEC 60601-1 (electrical safety) and IEC 60601-1-2 (EMC). Photobiological safety per IEC/EN 62471. Software designed per IEC 62304. Connectivity via web-portal for data analysis. Class I protection, Type B applied part.
Indications for Use
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.
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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EarliTec Diagnostics, Inc % Sew-Wah Tay, Ph.D. Regulatory Consultant Libra Medical Inc 8401 73rd Ave N. Suite 63 Brooklyn Park, Minnesota 55428
Re: K230337
Trade/Device Name: EarliPoint Regulation Number: 21 CFR 882.1491 Regulation Name: Pediatric Autism Spectrum Disorder Diagnosis Aid Regulatory Class: Class II Product Code: QPF Dated: May 31, 2023 Received: June 1, 2023
Dear 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
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requirements, including, but not limited to: registration and listing (21 CFR Part 807); labeling (21 CFR Part 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 (OS) 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,
# Patrick Antkowiak -S
for 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)
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 ASD patients ages 16 months through 30 months
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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# 510(K) SUMMARY
#### ADMINISTRATIVE INFORMATION 1
| 510(k) Submission Type: | Traditional 510(k) |
|----------------------------|----------------------------------------------------------------------------------------------------------------------------------------------------------------------|
| Device Type (Common Name): | Pediatric Autism Spectrum Disorder Diagnostic Tool |
| Device Classification Name | Pediatric Autism Spectrum Disorder Diagnostic Aid |
| Device Trade Name | EarliPoint System |
| Predicate Device Name | EarliPoint System (K213882) |
| 510(k) Submitter: | Thomas Ressemann, CEO<br>EarliTec Diagnostics, Inc.<br>755 Commerce Drive, Suite 700<br>Decatur, GA 30030<br>Tel:+1-320-267-7266<br>Email: tressemann@earlitecdx.com |
| Primary Correspondent: | Sew-Wah Tay<br>Regulatory Consultant<br>Libra Medical, Inc.<br>Tel: 612-801-6782<br>Email: swtay@libramed.com |
| Classification Regulation: | 21 CFR 882.1491 |
| Class: | II |
| Panel: | Neurology Devices Panel |
| Product Code: | OPF |
#### 510(K) TYPE AND REASON FOR SUBMISSION 2
This 510(k) is submitted as a traditional 510(k) to obtain marketing clearance for the modified EarliPoint System, a modification to the form factor of the device to improve usability and portability.
#### 3 INTENDED USE
The EarliPoint System is intended for use by healthcare providers to objectively diagnose and assess children for ASD using software algorithm to analyze a child's response to an external stimulus in the form of videos.
#### INDICATION FOR USE ব
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.
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#### PREDICATE DEVICE റ
The predicate device is the EarliPoint System cleared under 510(k) K213882. Both devices have the same intended use as an aid to diagnose and assess the presence of autism spectrum disorders (ASD) using a proprietary algorithm based on the looking behavior of the patient.
#### DEVICE DESCRIPTION 6
The device is a more compact version of the predicate device but otherwise has similar functions and features. The system uses an eye tracker to capture the patient's looking behavior while viewing a series of videos. The system then remotely analyzes the looking behavior data using software and outputs a diagnosis of the patient's ASD status and assesses the symptoms associated with ASD.
The system has two modules:
EarliPoint System consists of the following:
- Eye-tracking module and a separate Operator Module that can control the Eye-tracking module remotely. The patient sits on a chair and the Eye-tracking module is adjusted by the operator such that the patient's eyes are within the specification of the eye tracking window
- -Eye-tracking module captures the patient visual response to social information provided in the form of a series of age-appropriate videos
- Operator's module is used to initiate and monitors the session remotely -
- -WebPortal securely stores all patient information, analyzes the eye tracking data, and outputs the results. Users can retrieve the results directly from the web-portal.
- -Artificial intelligence software analyzes the eye-tracking data and provides a diagnosis for ASD. In addition, it 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
- O Verbal Ability Index correlates and proxies the age equivalent Mullen Verbal 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 as the eye tracker used in the predicate device.
| Description | EarliPoint Eye Tracking Specifications |
|-----------------------------|----------------------------------------------------------------------------------------------------------------|
| Wavelength | Near-Infrared Spectrum |
| Intensity | Conforms to safety limit for continue use per IEC/EN<br>62471<br>(Photobiological safety of lamps/lamp system) |
| Light source | IR LED |
| Duration of<br>Use/Exposure | ~15 minutes |
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| Description | EarliPoint Eye Tracking Specifications |
|-----------------------------------------------------------|------------------------------------------------|
| Accuracy/Precision<br>for intended use<br>(gaze position) | 0.5° |
| Recording<br>Measurement | 2D Eye Movements |
| Video Hardware | Video Camera |
| Mounting Hardware | Remote eye-tracking system<br>(Display Screen) |
| Processor Hardware | Personal computer |
| Sampling Rate | 120 Hz |
#### PERFORMANCE TESTING 6.1
The basis of the substantial equivalence between the device and its predicate is the bench performance tests. Both devices meet the electrical safety standards and both software were designed and tested per IEC 62304.
To ensure that the device is substantially equivalent, the new eye tracker is verified to meet the predicate eye tracker requirement specifications for the device. In addition, the performance of the eye trackers used in the predicate device and the current device were compared directly using adult participants for identifications of blinks, saccades, non-missing samples, and fixation accuracy. The eye-tracker tests demonstrated that both the new eye tracker and the predicate eye tracker meet the requirements for the eye tracker. Hence the two eye trackers are functionally equivalent and interchangeable.
The second performance test is to ensure that the analysis algorithm is equivalent and will output the same results as the original analysis algorithm. To demonstrate the equivalency of the software, the collected session data from the pivotal clinical study were processed through the current software. Each ASD diagnoses and Indices of Social Disability, Verbal Ability, and Nonverbal Ability provided by predicate and the current device were compared and found to be the same, demonstrating the equivalency of the current device's data analysis software to the predicate data analysis software.
#### 6.2 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
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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.
#### SOFTWARE VERIFICATION AND VALIDATION TESTING 6.3
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.
#### CLINICAL STUDIES 6.4
The safety and effectiveness of the EarliPoint System in collecting eve tracking data and analyzing the data for the diagnosing the presence of ASD was evaluated in a pivotal study where patients were diagnosed by the device as well and expert clinicians (reference standard).
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.
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.
| Population | Sensitivity<br>Mean (n/N)<br>95% CI | Specificity<br>Mean (n/N)<br>95% CI |
|--------------------------------------------------------------------------------|-------------------------------------|-------------------------------------|
| 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% |
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#### 7 SUBSTANTIAL EQUIVALENT COMPARISON
| Device<br>Characteristic | EarliPoint System<br>K213882 | Updated EarliPoint System<br>Candidate Device |
|------------------------------------------|--------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|--------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|
| Intended Use | The EarliPoint System is intended for use<br>by healthcare providers to objectively<br>diagnose and assess children for ASD<br>using software algorithm to analyze a<br>child's response to an external stimulus in<br>the form of videos. | The EarliPoint System is intended for use<br>by healthcare providers to objectively<br>diagnose and assess children for ASD<br>using software algorithm to analyze a<br>child's response to an external stimulus in<br>the form of videos. |
| Indications for<br>Use | The EarliPoint System is indicated for<br>use in specialized developmental<br>disabilities centers as a tool to aid<br>clinicians in the diagnosis and assessment<br>of ASD patients ages 16 months through<br>30 months. | The EarliPoint System is indicated for<br>use in specialized developmental<br>disabilities centers as a tool to aid<br>clinicians in the diagnosis and assessment<br>of ASD patients ages 16 months through<br>30 months. |
| Prescription Use | Yes | Yes |
| Product Code and<br>Regulation<br>Number | QPF<br>882.1491 | QPF<br>882.1491 |
| Device<br>Components | Patient console with eye-tracking<br>technology for data collection and<br>software analysis for diagnosis of ASD.<br>Both patient and Operator screens are<br>controlled by the same computer. | Similar components as EarliPoint System<br>with a smaller device form factor. Patient<br>and Operator screens are separate<br>computer devices connected indirectly<br>through the web-portal |
| ASD Diagnosis | Use software algorithm to analyze the eye<br>tracking data for ASD diagnosis ad<br>assessment | No change. Same software algorithm |
| Electrical Safety<br>Testing | Meets electrical safety standards per:<br>• IEC 60601-<br>1:2005/AMD1:2012/AMD2:2020<br>• IEC 60601-1-<br>2:2014/AMD1:2020 | Meets electrical safety standards per:<br>• IEC 60601-<br>1:2005/AMD1:2012/AMD2:2020<br>• IEC 60601-1-<br>2:2014/AMD1:2020 |
| Software | Compliant to ISO 62304 | Compliant to ISO 62304 |
| Clinical data | Pivotal data shows safety and<br>effectiveness. Data is analyzed using the<br>Data Analysis module. | Data Analysis software is verified so that<br>the output is the same as the predicate<br>device. |
| Risk level of the<br>device | Low risk device, non-invasive | Low risk device, non-invasive |
#### CONCLUSION 7.1
Both devices have the same intended use and the same indication for use. The technological differences between the two devices are minor and verified via bench testing to be equivalent. The conclusions drawn from the nonclinical demonstrate that the device is as safe, as effective, and performs as well as the legally marketed device. Hence, the subject device is substantially equivalent to the predicate device.
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.