Cognoa ASD Diagnosis Aid Validation Study: 425 subjects
>1 (specialist clinicians)
Indications for Use
The Cognoa ASD Diagnosis Aid is intended for use by healthcare providers as an aid in the diagnosis of Autism Spectrum Disorder (ASD) for patients ages 18 months through 72 months who are at risk for developmental delay based on concerns of a parent, caregiver, or healthcare provider. The device is not intended for use as a stand-alone diagnostic device but as an adjunct to the diagnostic process.
Device Story
Software as a medical device (SaMD) aiding ASD diagnosis in pediatric patients. Inputs: caregiver-provided video/questionnaire data via mobile app; trained analyst video review; HCP-provided behavioral observations via portal. Machine-learning algorithm processes inputs to generate scalar value (1-6); compared against thresholds to output 'Positive for ASD', 'Negative for ASD', or 'No Result'. Used in clinical settings by HCPs; adjunct to clinical history/observation. Benefits: facilitates earlier diagnosis/intervention compared to standard clinical pathways; optimizes neurodevelopmental outcomes. Output informs clinical decision-making; does not replace professional diagnostic judgment.
Clinical Evidence
Special controls require clinical performance testing to demonstrate intended use. Must evaluate sensitivity, specificity, positive predictive value (PPV), and negative predictive value (NPV) using a reference method of diagnosis and assessment of patient behavioral symptomology.
Technological Characteristics
Software-based diagnostic aid; utilizes algorithms to interpret patient behavioral symptomology. Requires software verification, validation, and hazard analysis. Includes cybersecurity assessment. Operates as prescription device under 21 CFR 801.109.
Indications for Use
Indicated for pediatric patients ages 18 to 72 months at risk for developmental delay due to caregiver or provider concerns. Contraindicated for patients with suspected hallucinations, schizophrenia, deafness, blindness, physical hand impairment, major dysmorphic features, fetal alcohol syndrome, genetic conditions (Rett's, fragile X), microcephaly, epilepsy/seizures, history of neglect, or brain injury/malformation requiring surgery or chronic medication.
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.
Submission Summary (Full Text)
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## DE NOVO CLASSIFICATION REQUEST FOR COGNOA ASD DIAGNOSIS AID
### REGULATORY INFORMATION
FDA identifies this generic type of device as:
Pediatric Autism Spectrum Disorder diagnosis aid. 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.
NEW REGULATION NUMBER: 21 CFR 882.1491
CLASSIFICATION: Class II
PRODUCT CODE: QPF
#### BACKGROUND
DEVICE NAME: Cognoa ASD Diagnosis Aid
SUBMISSION NUMBER: DEN200069
DATE DE NOVO RECEIVED: November 3, 2020
#### SPONSOR INFORMATION:
Cognoa, Inc. 2185 Park Blvd. Palo Alto, California 94306
#### INDICATIONS FOR USE
The Cognoa ASD Diagnosis Aid is intended for use by healthcare providers as an aid in the diagnosis of Autism Spectrum Disorder (ASD) for patients ages 18 months through 72 months who are at risk for developmental delay based on concerns of a parent, caregiver, or healthcare provider.
The device is not intended for use as a stand-alone diagnostic device but as an adjunct to the diagnostic process.
### LIMITATIONS
The sale, distribution, and use of the device are restricted to prescription use in accordance with 21 CFR 801.109.
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The device is not intended for use as a stand-alone diagnostic device but as an adjunct to the diagnostic process.
The device is intended for use in conjunction with patient history, clinical observations, and other clinical evidence the healthcare provider determines are necessary before making clinical decisions. For instance, additional standardized testing may be sought to confirm the device output, especially when the device result is not Positive or Negative for ASD.
The device may give unreliable results if used in patients with other conditions that would have excluded them from the clinical study. Among those conditions are the following:
- . Suspected auditory or visual hallucinations or with prior diagnosis of childhood onset schizophrenia.
- Known deafness or blindness. ●
- . Known physical impairment affecting their ability to use their hands.
- Major dysmorphic features or prenatal exposure to teratogens such as fetal . alcohol syndrome.
- History or diagnosis of genetic conditions (such as Rett's syndrome or fragile X).
- . Microcephaly.
- History or prior diagnosis of epilepsy or seizures. ●
- History of or suspected neglect. .
- . History of brain defect injury or insult requiring interventions such as surgery or chronic medication.
# PLEASE REFER TO THE LABELING FOR A COMPLETE LIST OF WARNINGS, PRECAUTIONS AND CONTRAINDICATIONS.
# DEVICE DESCRIPTION
The Cognoa ASD Diagnosis Aid is a software as a medical device (SaMD) that utilizes a machine-learning algorithm that receives independent information from caregivers or parents, trained analysts, and healthcare professionals (HCPs) to aid in the diagnosis of ASD. It consists of multiple software applications and hardware platforms. Input data is acquired via a Mobile App, a Video Analyst Portal, and a HCP Portal.
- . Mobile App: User interface (UI) for the caregiver or parent to upload videos of the patient via Wi-Fi connection and answer questions about key developmental behaviors. Interfaces with Application Programming Interface (API) server for transmission and management of patient data. Compatible with both iOS (versions 12 and 13) and Android platforms (versions 9 and 10).
- Video Analyst Portal: UI for trained analysts to review uploaded patient videos . remotely and answer questions about the patients' behaviors observed in the videos.
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- . HCP Portal: UI for the HCP to answer questions about key developmental behaviors for the patient's age group, view device output and access the interactive dashboard to view all patient results, patient videos, answers to questionnaires administered and device performance data. Compatible with computer operating systems macOS (Catalina or Mojave) and Windows 10, and browsers Safari (versions 12 or 13) and Chrome (versions 84 or 85).
Following analysis of the input data, the Cognoa ASD Diagnosis Aid machine-learning algorithm produces a single scalar value between (1) and (6) which is then compared to preset thresholds to determine the classification. If the value is greater than the upper threshold, then the device output is 'Positive for ASD.' If the value is less than the lower threshold, then the device output is 'Negative for ASD.' If the available information does not allow the algorithm to render a reliable result, the device output is 'No Result.'
### SUMMARY OF NONCLINICAL/BENCH STUDIES
The Cognoa ASD Diagnosis Aid is a SaMD implemented on a general purpose computing platform. Non-clinical or bench testing was generally not needed to evaluate the hardware that the software is intended to be run on. Software documentation and a usability assessment were both provided to demonstrate the safety and effectiveness of the device.
#### SOFTWARE/CYBERSECURITY
Software verification and validation testing and documentation was provided according to a MODERATE level of concern and FDA's guidance document, "Guidance for the Content of Premarket Submissions for Software Contained in Medical Devices" (May 11, 2005), to demonstrate that the device software performs as intended. Adequate documentation describing the software, firmware, software specifications, architecture design, software development environment, traceability, revision level history, and unresolved anomalies conclude that the software will operate in the manner described in the specifications. Hazard analysis characterized software and cybersecurity risks, including device malfunction, measurement-related errors, protection of patient data when stored or in transit (including data encryption), and unauthorized access by malicious end users. The submission describes verification and validation testing to address the potential hazards with satisfactory results. The device algorithm was provided describing how the data are collected and analyzed by the underlying model that is applied to produce the final device outputs.
Regarding the cybersecurity, the documentation included all the recommended information from the FDA guidance document. "Content of Premarket Submissions for Management of Cybersecurity in Medical Devices." This includes a threat model, cybersecurity mitigation information, an upgrade and maintenance plan, and other information for safeguarding the device algorithms.
### HUMAN FACTORS-USABILITY
An observational, simulated use study was performed in order to evaluate HCP completion of critical tasks associated with use of the HCP Portal component of the
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Cognoa ASD Diagnosis Aid. A total of "" primary care providers who completed residency training in Pediatrics, Medicine/Pediatrics, or Family Practice and who see pediatric patients were tested. Participants performed critical tasks across anticipated use scenarios, including (1) login per the instructions in the labeling to ensure access to the HCP Portal using user-specific account information and ability to locate the indications for use, precautions and warnings in the HCP Portal Instructions for Use; (2) ensure access to the patient dashboard for viewing patient data, labeling, and HCP Portal logout; (3) ensure completion of the patient questionnaire; and (4) to ensure the patient's results can be viewed and querying how the HCP interpreted the results provided for each available device output (Positive for ASD, Negative for ASD, No Result).
A test moderator facilitated the testing, provided information regarding the test procedures beforehand, and asked questions specific to each use scenario evaluated. Specific to scenarios testing the use and interpretation of the device outputs, participants were asked to access patient records, discuss their clinical interpretation of each device output, and discuss how they would proceed with clinical decision making, both in the event that the device output affirmed or differed from how the patient presented clinically. Following completion of testing, post-evaluation interviews were conducted using 10 open-ended, neutrally worded questions to evaluate clinical end user perspectives of any use difficulties experienced during the testing.
Study results indicated that participants were able to complete identified critical tasks associated with use of the device. All participants understood and were able to navigate the labeling and recite or affirm understanding of the content. All participants were able to access patient records, complete the questionnaire and submit responses. All participants were able to access and review results, with 100% of participants reporting being able to communicate an accurate understanding of each type of device output. In response to probative inquiry, all clinical end users were able to communicate adequate understanding of use of the device as an aid in diagnosis of ASD and that the device does not provide a stand-alone diagnosis.
# SUMMARY OF CLINICAL INFORMATION
### Study Design
The clinical validation study was a prospective, double-blinded, single-arm study conducted at 14 sites in the United States ["Cognoa ASD Diagnosis Aid Validation Study"] to evaluate the safety and effectiveness of the Cognoa ASD Diagnosis Aid to aid in the diagnosis of ASD, with the comparator being the clinical reference standard. Both parents or caregivers and HCPs were blinded to the results as provided by the device. Additionally, the parents or caregivers, trained video analysts, and HCPs providing inputs to the device algorithm were blinded to data inputs provided by each other.
# Clinical Reference Standard
The clinical reference standard is the determination of clinical diagnosis based on the majority assessment of up to three specialists. This involved diagnosis by a site-specific specialist using the DSM-5 criteria, followed by validation via independent review by one or two central specialist clinicians. After the diagnosing clinician on-site completed the patient assessment, the
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patient case was reviewed by one central off-site reviewing specialist clinician who was provided with the standardized medical history and physical form, and a video of the diagnostic encounter. The diagnosing clinician was instructed to not state any diagnostic conclusion, decision on anv particular component of DSM-5 criteria, or diagnostic observation during the video of the assessment. If the assessment of the reviewing specialist clinician agreed with that of the diagnosing clinician, the diagnosis was considered validated and no further validation was conducted. If the reviewing specialist clinician disagreed with the diagnosing clinician, then the case was referred to a second reviewing specialist clinician. Majority rule was used to resolve discrepancies between the two central reviewers and the site diagnosing specialist who all evaluated the same subjects.
## Inclusion Criteria
- Caregiver must be able to read, understand and sign the Informed Consent Form (ICF). ●
- Caregiver or HCP concern for developmental delay. .
- Female or Male, ≥ 18 to < 72 months of age. ●
- Functional English capability in the home environment.
- Caregiver must have smartphone capabilities for downloading the Cognoa Research App. ●
- Participants must be willing to be videotaped as part of the diagnostic assessment by the specialist clinician.
## Exclusion Criteria
- Subjects with a prior diagnosis of ASD rendered by a healthcare professional.
- Subjects with suspected auditory or visual hallucinations or with prior diagnosis of childhood onset schizophrenia.
- Subjects with deafness or blindness.
- Subjects with known physical impairments affecting their ability to use their hands. ●
- Subjects with major dysmorphic features or prenatal exposure to teratogens (such as fetal ● alcohol syndrome).
- Subjects with history, suspicion, or diagnosis of genetic conditions (such as Rett's ● Syndrome or Fragile X).
- Subjects with microcephaly. ●
- . Subjects with history or prior diagnosis of epilepsy or seizures.
- Subjects with a history of neglect.
- Subjects with a history of brain malformation, injury or insult requiring interventions such as surgery or chronic medication.
- Subjects whose age on the date of enrollment is outside the target age range.
- Subjects or caregivers who have been previously enrolled in any Cognoa clinical study or survey.
- Subjects whose medical records had been included in any internal Cognoa training or validation sets.
Objectives Primary Effectiveness Objective
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- Achieve a composite of positive predictive value (PPV) greater than 65% and negative ● predictive value (NPV) greater than 85% for the device in relation to the clinical reference standard in the overall study population; and
- Measurement of the proportion of all patients for whom the device provides no result. .
# Secondarv Effectiveness Objective
- Evaluate sensitivity of the device in relation to the clinical reference standard in the . overall study population; and
- Evaluate specificity of the device in relation to the clinical reference standard in the ● overall study population.
## Safety Objective
Adverse events (AEs) and serious adverse events (SAEs) were collected and reported from enrollment through completion of the reference diagnosis clinic visit.
## Results
Enrollment: A total of 711 subjects were enrolled who signed the ICF, with 585 subjects having completed all inputs to the device assessment. Of these 585 subjects, 425 subjects were considered study completers (i.e., subjects who completed both the device assessment as well as the specialist assessment per the clinical reference standard) (see Figure 1). The 425 subjects who completed all study assessments per protocol was used as the analysis population for the assessment of the study endpoints.
Image /page/5/Figure/9 description: The image is a flowchart showing the progression of subjects through a study. It starts with 711 subjects who consented to participate. Of those, 585 completed all inputs to the Cognoa Device, while 126 did not. Among those who completed all inputs to the Cognoa Device, 425 also completed the specialist evaluation, and 160 did not.
Figure 1. Study Subject Diagram
Effectiveness: Of the 425 subjects who completed both the device assessment as well as the specialist assessment per the clinical reference standard, the device rendered either a positive or negative diagnostic output in 135 subjects (32%). This corresponds to a 'No Result' device output rate of 68% (64%, 73%).
Measures of PPV, NPV, sensitivity, and specificity with associated 95% confidence intervals were calculated for the subset of patients for whom the device rendered a diagnostic output of
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'Positive for ASD' or 'Negative for ASD', resulting in: PPV of 81% (70%, 89%), NPV of 98% (91%, 100%), sensitivity of 98% (92%, 100%), and specificity of 79% (68%, 88%) (see 1. and 2).
| | | Clinical Reference Standard | | |
|----------------------------|--------------|-----------------------------|--------------|-------|
| | | ASD Positive | ASD Negative | Total |
| Cognoa<br>Device<br>Output | ASD Positive | 63 | 15 | 78 |
| | ASD Negative | 1 | 56 | 57 |
| | No Response | 58 | 232 | 290 |
| | Total | 122 | 303 | 425 |
Table 1. Results Comparing Device to Clinical Reference Standard
| Table 2. Statistical Estimates of Device Performance Observed in the Study | | | | | |
|----------------------------------------------------------------------------|--|--|--|--|--|
|----------------------------------------------------------------------------|--|--|--|--|--|
| Metric | Point Estimate | 95% Confidence Interval |
|------------------|------------------|-------------------------|
| PPV | 80.77% (63/78) | 70.27%, 88.82% |
| NPV | 98.25% (56/57) | 90.61%, 99.96% |
| Sensitivity | 98.44% (63/64) | 91.6%, 99.96% |
| Specificity | 78.87% (56/71) | 67.56%, 87.67% |
| No Response Rate | 68.24% (290/425) | 63.58%, 72.64% |
Safety: No adverse events were reported during the study.
# LABELING
The labeling is sufficient and satisfies the requirements of 21 CFR 801.109 for prescription devices.
The labeling includes a detailed description of the device with images and computing requirements that must be met by any general computing hardware to run the software device, a description of the patient population for which the device is indicated for use, warnings, precautions, and instructions for use. The labeling also includes summary information about the clinical validation study performed with the device. These discussions have been provided in both a user manual for physicians and a user manual for patients, parents, and caregivers.
The labeling includes warnings that the device is intended for use by healthcare professionals trained and qualified to interpret the results of a behavioral assessment examination and to diagnose ASD, and that it should be used in conjunction with patient history, clinical observations, and other clinical evidence the healthcare professional determines are necessary before making clinical decisions. For instance, additional standardized testing may be sought to confirm the device output, or especially for further evaluation when the device provides 'No Result'.
# RISKS TO HEALTH
The table below identifies the risks to health that may be associated with use of a pediatric Autism Spectrum Disorder diagnosis aid and the measures necessary to mitigate these risks.
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| Identified Risks to Health | Mitigation Measures |
|-------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|----------------------------------------------------------------------------------------------------|
| Device failure or incorrect analysis leading to:<br>• False positives resulting in inappropriate patient treatment and potentially delayed diagnosis of a non-ASD condition<br>• False negatives resulting in delayed diagnosis and patient treatment | Clinical performance testing<br>Software verification, validation, and hazard analysis<br>Labeling |
| Use error or misinterpretation of results resulting in a false positive or false negative | Usability assessment<br>Labeling |
## 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.
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(iii)A statement that the device is not intended for use as a stand-alone diagnostic.
## BENEFIT-RISK DETERMINATION
The risks of the device are based on software verification and validation, and human factorsusability test data, as well as data collected in a clinical study described above.
The number of patients in the study who received a false negative device output was low (n=1). Risks associated with patients who receive a false negative result are mitigated by the device labeling, which states that a negative result does not ensure that the patient will not develop ASD in the future, and that performance of additional confirmatory assessment or testing among individuals with a negative result is at the discretion of the clinician. Similarly, the number of patients in the study who received a false positive was moderately low (n=15). Risks associated with patients who receive a false positive are mitigated by the device labeling, which reminds clinicians to always consider clinical presentation when making a diagnosis for ASD, and that such a diagnosis is based upon defined clinical characteristics outlined in the DSM-5. Furthermore, since the patients being evaluated by the device are already presenting with concerns of potential developmental delay as identified by either their parent, caregiver, or healthcare provider, patients who are incorrectly treated for ASD may still receive some benefit from these interventions, despite potentially delaying a correct diagnosis.
The probable benefits of the device are also based on software verification and validation and human factors-usability test data, as well as data collected in a clinical study described above.
Patients with true positive (n=63) or false positive results are likely to benefit from interventions for ASD. For true positive cases, getting a definite diagnosis can result in earlier confirmatory testing or assessment, in addition to earlier access to intervention. Currently, the average age of diagnosis for ASD in the United States is 4.3 years, as compared to 2.8 years observed in the pivotal trial of the Cognoa ASD Diagnosis Aid. Earlier confirmation of diagnosis can enable ASD patients to gain access to necessary intervention and optimize skill development at an earlier point in the neurodevelopmental window. Longitudinally, early intervention between 2 and 3 years of age has shown to result in positive outcomes upon reaching adulthood.
For patients who received a device output of 'No Result' (68.24% of subjects, or 290/425), additional standardized testing is recommended in order to drive clinical decision making. For patients who may be included that are ASD positive (n=58 in the study performed), the result may drive subsequent standardized testing and appropriate referrals, also in a more expeditious manner.
For the reasons described above, the probable benefits of the Cognoa ASD Diagnosis Aid outweigh the probable risks considering the listed special controls and general controls.
### Patient Perspectives
This submission did not include specific information on patient perspectives for this device.
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### Benefit/Risk Conclusion
In conclusion, given the available information above, for the following indication statement:
The Cognoa ASD Diagnosis Aid is intended for use by healthcare providers as an aid in the diagnosis of Autism Spectrum Disorder (ASD) for patients ages 18 months through 72 months who are at risk for developmental delay based on concerns of a parent, caregiver, or healthcare provider.
The device is not intended for use as a stand-alone diagnostic device but as an adjunct to the diagnostic process.
The probable benefits outweigh the probable risks for the Cognoa ASD Diagnosis Aid. The device provides benefits and the risks can be mitigated by the use of general controls and the identified special controls.
## CONCLUSION
The De Novo request for the Cognoa ASD Diagnosis Aid is granted and the device is classified as follows:
Product Code: OPF Device Type: Pediatric Autism Spectrum Disorder diagnosis aid Regulation Number: 21 CFR 882.1491 Class: II
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.