K233577 · Big Health, Inc. · QVO · Aug 5, 2024 · Neurology
Device Facts
Record ID
K233577
Device Name
Sleepio®
Applicant
Big Health, Inc.
Product Code
QVO · Neurology
Decision Date
Aug 5, 2024
Decision
SESE
Submission Type
Traditional
Regulation
21 CFR 882.5801
Device Class
Class 2
Attributes
Software as a Medical Device, Therapeutic
Indications for Use
Sleepio is a digital therapeutic intended for the treatment of chronic insomnia disorder as an adjunct to usual care in patients aged 18 and older. Sleepio is a prescription device delivering Cognitive Behavioral Therapy for Insomnia (CBT-I) and can be made available on the order of a licensed healthcare provider.
Device Story
Sleepio is a digital therapeutic (SaMD) delivering Cognitive Behavioral Therapy for Insomnia (CBT-I) to treat chronic insomnia. Input data includes patient symptoms and daily sleep tracking; device transforms inputs via evidence-based techniques to consolidate sleep-wake cycles, restructure dysfunctional beliefs, and reduce sleep-related anxiety. Output is delivered via smartphone/tablet apps (iOS/Android) and web; healthcare providers access a dashboard to track patient engagement. Used as an adjunct to usual care in clinic or home settings. Benefits include improved insomnia symptoms, reduced sleep onset latency, and decreased wake after sleep onset. Clinical decision-making is supported by provider review of patient progress via an online portal.
Clinical Evidence
Evaluated in the CrEDIT trial, a two-arm, parallel-group RCT (n=336) comparing Sleepio to online sleep hygiene education (SHE) in adults aged 22+. Co-primary endpoints at 10 weeks: Insomnia Severity Index (ISI), sleep onset latency (SOL), and wake after sleep onset (WASO). Sleepio showed statistically significant improvements: ISI adjusted difference -2.37 (p<0.001), WASO adjusted difference -8.86 (p=0.003). Responder analysis at 10 weeks showed 2.52 odds of response and 5.78 odds of remission for Sleepio vs SHE. No adverse events reported.
Technological Characteristics
SaMD; mobile application (iOS/Android) and web-based platform. Delivers CBT-I via digital content. Software architecture includes patient-facing interface, clinician-facing dashboard, and backend services. Moderate level of concern for software. No specific hardware materials; connectivity via standard internet protocols.
Indications for Use
Indicated for treatment of chronic insomnia disorder in patients aged 18 and older as an adjunct to usual care. Prescription only.
Regulatory Classification
Identification
A computerized behavioral therapy device for psychiatric disorders is a prescription only device intended to provide a computerized version of condition-specific behavioral therapy as an adjunct to clinician supervised outpatient treatment to patients with psychiatric conditions. The digital therapy is intended to provide patients access to therapy tools used during treatment sessions to improve recognized treatment outcomes.
Special Controls
*Classification.* Class II (special controls). The special controls for this device are:(1) Clinical data must be provided to fulfill the following:
(i) Describe a validated model of behavioral therapy for the psychiatric disorder; and
(ii) Validate the model of behavioral therapy as implemented by the device.
(2) Software must be described in detail in the software requirements specification (SRS) and software design specification (SDS). Software verification, validation, and hazard analysis must be performed. Software documentation must demonstrate that the device effectively implements the behavioral therapy model.
(3) The following labeling must be provided:
(i) Patient and physician labeling must include instructions for use, including images that demonstrate how to interact with the device.
(ii) Patient and physician labeling must list compatible devices.
(iii) Patient and physician labeling must include a warning that the device is not intended for use as a standalone therapy.
(iv) Patient and physician labeling must include a warning that the device does not represent a substitution for the patient's medication.
(v) Physician labeling must include a summary of the clinical testing with the device.
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August 5, 2024
Big Health, Inc. Reuben Lawson Vice President, Regulatory Affairs & Quality Systems 461 Bush St. Suite 200 San Francisco, California 94108
Re: K233577
Trade/Device Name: Sleepio® Regulation Number: 21 CFR 882.5801 Regulation Name: Computerized Behavioral Therapy Device For Psychiatric Disorders Regulatory Class: Class II Product Code: QVO Dated: June 24, 2024 Received: June 24, 2024
Dear Reuben Lawson:
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 (the 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 available 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.
Additional information about changes that may require a new premarket notification are provided in the FDA guidance documents entitled "Deciding When to Submit a 510(k) for a Change to an Existing Device" (https://www.fda.gov/media/99812/download) and "Deciding When to Submit a 510(k) for a Software Change to an Existing Device" (https://www.fda.gov/media/99785/download).
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Your device is also subject to, among other requirements, the Quality System (QS) regulation (21 CFR Part 820), which includes, but is not limited to, 21 CFR 820.30, Design controls; 21 CFR 820.90, Nonconforming product; and 21 CFR 820.100, Corrective and preventive action. Please note that regardless of whether a change requires premarket review, the QS regulation requires device manufacturers to review and approve changes to device design and production (21 CFR 820.30 and 21 CFR 820.70) and document changes and approvals in the device master record (21 CFR 820.181).
Page 2
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 801); medical device reporting of medical device-related adverse events) (21 CFR Part 803) for devices or postmarketing safety reporting (21 CFR Part 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 Part 4, Subpart A) for combination products; and, if applicable, the electronic product radiation control provisions (Sections 531-542 of the Act); 21 CFR Parts 1000-1050.
Also, please note the regulation entitled, "Misbranding by reference to premarket notification" (21 CFR 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,
# Robert Kang -S
for Pamela Scott Assistant Director DHT5B: Division of Neuromodulation and Rehabilitation 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) K233577
Device Name Sleepio
Indications for Use (Describe)
Sleepio is a digital therapeutic intended for the treatment of chronic insomnia disorder as an adjunct to usual care in patients aged 18 and older. Sleepio is a prescription device delivering Cognitive Behavioral Therapy for Insomnia (CBT-I) and can be made available on the order of a licensed healthcare provider.
| Type of Use (Select one or both, as applicable) | |
|----------------------------------------------------------------------------------------------------------------------------------|---------------------------------------------------------------------------------------------------------------------------------|
| <div> <span> <span style="font-size: 16px;">☑</span> Prescription Use (Part 21 CFR 801 Subpart D) </span> </div> | <div> <span> <span style="font-size: 16px;">☐</span> Over-The-Counter Use (21 CFR 801 Subpart C) </span> </div> |
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## 510(k) Summary - K233577
#### Submitter 1.
| Submitted by: | Big Health Inc. |
|---------------------|------------------------------------------------------------------------|
| Contact Name: | Reuben Lawson,<br>Vice President, Quality Systems & Regulatory Affairs |
| Contact Phone: | (949) 439 3629 |
| Additional Contact: | Dr. Alasdair L. Henry, Senior Manager, Clinical<br>Research |
- 2. Device
| Device name: | Sleepio |
|----------------------|-------------------------------------------------------------------------------|
| Classification name: | Computerized Behavioral Therapy for<br>Psychiatry Disorders (21 CFR 882.5801) |
| Regulatory class: | Class II (Special Controls) |
| Product code: | QVO |
#### 3. Predicate device
| Device name: | Somryst | Category | Sleepio (this submission) | Somryst (predicate) |
|----------------------|-------------------------------------------------------------------------------|----------------------------------------|----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|
| Manufacturer: | Pear Therapeutics | 510(k) number | K233577 | K191716 |
| Classification name: | Computerized Behavioral Therapy for<br>Psychiatry Disorders (21 CFR 882.5801) | Classification regulation | 21 CFR 882.5801<br>Computerized behavioral<br>therapy device for psychiatric<br>disorders | 21 CFR 882.5801<br>Computerized behavioral<br>therapy device for psychiatric<br>disorders |
| Regulatory class: | Class II (Special Controls) | Intended use | SaMD intended to be<br>computerized behavioral<br>therapy device to treat<br>patients with chronic<br>insomnia / insomnia disorder | SaMD intended to be<br>computerized behavioral<br>therapy device to treat<br>patients with chronic<br>insomnia |
| 510(k) Number: | K191716 | Indications for use | Sleepio is a digital<br>therapeutic intended for the<br>treatment of chronic<br>insomnia / insomnia disorder<br>as an adjunct to usual care in<br>patients aged 18 years and<br>older. Sleepio is a<br>prescription device delivering<br>Cognitive Behavioral<br>Therapy for Insomnia (CBT-I)<br>and can be made available<br>on the order of a licensed<br>healthcare provider. | Somryst is a prescription-only<br>digital therapeutic intended to<br>provide a neurobehavioral<br>intervention (Cognitive<br>Behavioral Therapy for<br>Insomnia - CBT-I) in patients<br>22 years of age and older<br>with chronic insomnia.<br>Somryst treats chronic<br>insomnia by improving a<br>patient's insomnia symptoms |
| Product code: | QVO | Mechanism of action | Consolidation and<br>regularization of sleep-wake<br>cycle, restructuring<br>dysfunctional beliefs and<br>attitudes about sleep,<br>cognitive-behavioral<br>reconditioning, reducing<br>sleep-related anxiety and<br>arousal | Consolidation and<br>regularization of sleep-wake<br>cycle, restructuring<br>dysfunctional beliefs and<br>attitudes about sleep,<br>cognitive-behavioral<br>reconditioning, reducing<br>sleep-related anxiety and<br>arousal |
| Medical Device Type | Software as a Medical<br>Device (SaMD) | Software as a Medical Device<br>(SaMD) | | |
#### Device Description 4.
Sleepio is a digital therapeutic for the treatment of chronic insomnia / insomnia disorder. Sleepio treats chronic insomnia disorder by delivering evidence-based techniques targeting the cognitive and behavioral factors that maintain insomnia and chronic sleep problems. Patient experience is tailored based on symptoms and daily sleep tracking. In addition to core therapeutic components, there is in-the-moment therapeutic content for help falling asleep. Content is delivered via smartphone and tablet applications (iOS and Android), as well as via web. Sleepio is intended as an adjunct to usual care treatment for chronic insomnia / insomnia disorder by a healthcare provider. Healthcare providers have access to a dashboard to track patient engagement with Sleepio.
#### 5. Intended use / Indications for use
Sleepio is a digital therapeutic intended for the treatment of chronic insomnia / insomnia disorder as an adjunct to usual care in patients aged 18 and older. Sleepio is a prescription device delivering Cognitive Behavioral Therapy for Insomnia (CBT-I) and can be made available on the order of a licensed healthcare provider.
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#### 6. Substantial equivalence
Sleepio has an identical intended use and nearly identical technological characteristics compared to the predicate device, Somryst. Like Somryst, Sleepio delivers cognitive behavioral therapy for insomnia (CBT-I) by way of an app provided as an adjunct to usual care by the patient's healthcare provider. However, Sleepio is available for use in patients aged 18 and older, whereas Somryst is available for use in patients aged 22 and older. Patients can only access the product on the order of a licensed healthcare provider, who will themselves have access to patient progress through an online portal where salient patient information is provided.
Table 1: Substantial Equivalence of Technological Characteristics
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| Access | Prescription only | Prescription only |
|-----------------------|-----------------------------------------------------------------------------------------------------|---------------------------------------------------------------------------------------|
| Adjunct use | Adjunct to supervised<br>outpatient treatment | Adjunct to supervised<br>outpatient treatment |
| Mobile platform | Mobile application<br>(Smartphones, tablets [iOS<br>and Android]), and web<br>application | Mobile application<br>(Smartphones, tablets [iOS<br>and Android]) |
| Software architecture | Patient facing mobile<br>application or website,<br>clinician facing dashboard,<br>backend services | Patient facing mobile<br>application, clinician facing<br>dashboard, backend services |
#### 7. Performance data
#### 7.1. Summary of nonclinical performance data
Software verification and validation testing was completed and documentation was provided as recommended by Guidance for Industry and FDA Staff: Guidance for the Content of Premarket Submissions for Software Contained in Medical Devices (2005), for a Moderate level of concern device.
#### 7.2. Summary of clinical performance data
Sleepio was evaluated in the Clinical Effectiveness of Digital Insomnia Therapy (CrEDIT) trial which was a two-arm, parallel group, randomized controlled trial (RCT) comparing digital CBT (Sleepio) with online sleep hygiene education in 336 adults aged 22+ with insomnia disorder, as diagnosed in the DSM-5. Participants were recruited from across the United States via social media. Participants were allocated to receive Sleepio (n=168) or online sleep hyqiene education (SHE; n=168) in addition to their usual care.
The co-primary endpoints were insomnia severity, assessed using the insomnia severity index (ISI), sleep diary sleep onset latency (SOL) and wake after sleep onset (WASO) at 10 weeks post-randomization. Analyses were performed to evaluate the odds of insomnia response (reduction in IS) score of ≥8) and remission (ISI<8) at 10 weeks. Follow-up assessments occurred at 16 and 24 weeks post- randomization. Secondary outcomes included insomnia symptoms assessed by the Sleep Condition Indicator (SCI-8).
The average age of the sample was 46.4 (SD=9.9) years. The study sample closely mapped onto the US population on racial and socioeconomic characteristics. See Table 2 for a summary of the study demographics and comparison with the US population.
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| Demographic characteristics | Sleepio pivotal trial<br>(CrEDIT) | Comparison to US<br>population |
|-------------------------------------------|-----------------------------------|--------------------------------|
| Gender, Female, % | 56% | 50.2%1 |
| Race, White, % | 71% | 59%5 |
| Education, College degree or<br>higher, % | 56% | 54%3 |
| Income | 61% with income<br><\$74.999 | Median = \$74,5804 |
| Employment, Full time, % | 48% | 52%5 |
Table 2: Summary of the CrEDIT trial study demographics and comparison with the US population.
# Study Results
A summary of the ITT sample results of the CrEDIT trial for Sleepio vs SHE is provided in Tables 3 and 4 below. A subsequent post-hoc analysis was conducted in participants who had ≤6.5 hours sleep duration at baseline and results for this subgroup are presented in Table 5. Note the data in table 4 was calculated post-hoc because LS Mean difference was not the statistical analysis method originally pre-specified in the statistical analysis plan.
Table 3: ISI, SOL and WASO summary statistics by group and time, and estimated treatment effects at week 10 (primary outcome), and week 16 (follow-up) and week 24 (longterm follow-up). Effects are between-group mean differences.
| Assessment | Unadjusted mean (SD); n | | Adjusted<br>difference<br>(SE) | 99% CI | <i>p</i> -value | Cohen's <i>d</i> |
|------------|--------------------------|-----------------------|--------------------------------|-------------------|-----------------|------------------|
| | SHE | Sleepio | | | | |
| ISI | | | | | | |
| Baseline | 18.33<br>(4.19); 168 | 18.12 (3.72);<br>168 | | | | |
| | | | | | | |
| Week 10 | 14.74<br>(5.00); 156 | 12.11 (6.10);<br>138 | -2.37 (0.56) | -3.81, -0.92 | <0.001 | 0.606 |
| Week 16 | 14.60<br>(5.19); 153 | 11.37 (5.67);<br>129 | -2.55 (0.57) | -4.01, -1.09 | <0.001 | 0.65 |
| Week 24 | 14.80<br>(5.49); 147 | 11.00 (5.93);<br>125 | -3.05 (0.59) | -4.56, -1.54 | | 0.77 |
| SOL | | | | | | |
| Baseline | 53.92<br>(41.83);<br>168 | 54.30<br>(38.41); 168 | | | | |
| Week 10 | 45.54<br>(48.82);<br>157 | 36.75<br>(32.00); 132 | -9.14 (3.63) | -18.49, 0.20 | 0.012 | 0.23 |
| Week 16 | 38.82<br>(38.23);<br>153 | 32.56<br>(29.70); 124 | -6.68 (3.69) | -16.18, 2.83 | 0.070 | 0.17 |
| Week 24 | 36.49<br>(40.73);<br>147 | 32.80<br>(43.59); 122 | -4.37 (3.99) | -14.64, 5.90 | | 0.11 |
| WASO | | | | | | |
| Baseline | 46.06<br>(31.45);<br>168 | 48.71<br>(50.71); 168 | | | | |
| Week 10 | 33.82<br>(30.06);<br>157 | 24.54<br>(21.40); 132 | -8.86 (2.94) | -16.42, -<br>1.29 | 0.003 | 0.21 |
| Week 16 | 35.27<br>(32.75);<br>153 | 23.97<br>(23.32); 124 | -11.69<br>(2.97) | -19.35, -<br>4.03 | <0.001 | 0.28 |
| Week 24 | 35.64<br>(35.10);<br>147 | 24.11<br>(27.03); 122 | -12.02<br>(3.19) | -20.24, -<br>3.80 | | 0.29 |
<sup>1</sup> US Census Bureau (2023). https://www.census.gov/content/dam/Census/newsroom/presskits/2023/paa/2023-paa-paper-financial-insecurity-hardship-pulse-gender-identity-sex.pdf
<sup>2</sup> US Census Bureau (2020). https://www.census.gov/quickfacts/fact/table/US/PST045222
<sup>3</sup> Lumina Foundation. (2023). https://www.luminafoundation.org/stronger-nation/report/#/progress
<sup>4</sup> US Census Bureau (2022). Income in the United States: 2022.
https://www.census.gov/library/publications/2023/demo/p60-279.html
<sup>5</sup> Bureau of Labor Statistics (2022). Work Experience of the Population.
https://www.bls.gov/news.release/pdf/work.pdf
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<sup>6</sup> Effect size when calculated using the standard deviation of ISI change at week 10, the effect size is d=0.47
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| | | Sleepio pivotal trial (CrEDIT) ITT analysis | | |
|------------|-------------------|---------------------------------------------|-------------|-------------------------------------|
| Assessment | Timepoint | Sleepio LS Mean | SHE LS Mean | Sleepio LS Mean difference (95% CI) |
| ISI | Post-treatment | -6.03 | -3.66 | -2.37 (-3.53, -1.21) |
| | 24-week follow-up | -6.82 | -3.86 | -2.97 (-4.30, -1.63) |
| SOL | Post-treatment | -17.50 | -8.05 | -9.45 (-17.90, -1.01) |
| | 24-week follow-up | -22.49 | -16.68 | -5.81 (-15.96, 4.34) |
| WASO | Post-treatment | -21.70 | -11.43 | -10.26 (-19.02, -1.49) |
| | 24-week follow-up | -20.45 | -10.13 | -10.32 (-20.10, -0.56) |
Table 4: LS mean differences for Sleepio and SHE at post-intervention and 24 week followup in the ITT sample
Table 4 note: The reported 95% Cls for week 24 follow-up are not from pre-specified hypothesis tests and are without multiplicity adjustment.
Table 5: LS mean differences for Sleepio and SHE at post-intervention and 24 week followup in participants with ≤6.5 hours sleep duration at baseline
| | | Sleepio pivotal trial (CrEDIT) with ≤6.5 hours sleep duration | | |
|------------|-------------------|---------------------------------------------------------------|-------------|-------------------------------------|
| Assessment | Timepoint | Sleepio LS Mean | SHE LS Mean | Sleepio LS Mean difference (95% CI) |
| ISI | Post-treatment | -6.44 | -3.98 | -2.46 (-4.42,-0.50) |
| | 24 week follow-up | -7.43 | -3.65 | -3.77 (-6.06, -1.48) |
| SOL | Post-treatment | -18.25 | -2.39 | -15.85 (-32.39, 0.69) |
| | 24 week follow-up | -29.24 | -16.87 | -12.37 (-26.93, 2.18) |
| WASO | Post-treatment | -33.79 | -10.69 | -23.10 (-42.37, -3.84) |
| | 24 week follow-up | -33.67 | -7.76 | -25.91 (-46.70, -5.11) |
Table 5 note: The reported 95% Cls are not from pre-specified hypothesis tests and are without multiplicity adjustment.
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# Remission and Responder Analyses
Treatment response and remission were assessed using the ISI. In pre-specified analyses, response was defined as a change of ≥6 points from baseline, and remission defined as a score of <8. At 10 weeks, Sleepio participants had 2.52 odds of response (OR=2.52; p<0.001, 99% Cl: (1.33, 4.75)), and 5.8 odds of remission (OR=5.78; p<0.001, 99% Cl: (2.11, 15.84)) compared with SHE. A post-hoc response analysis using a definition of a change of ≥8 points from baseline on the ISI showed that Sleepio participants had 3.30 odds ratio of response compared to SHE participants (OR=3.30; 95% CI: (1.92, 5.69)).
Note: Odds ratio >1 for ISI means the odds of being in remission/response are higher in Sleepio than Control Group.
No adverse or serious adverse events were reported by participants.
#### 8. Conclusion
Sleepio and the predicate Somryst have the same Intended Use as computerized behavioral therapy devices for psychiatric disorders. There are slight differences in indications for use in that Sleepio is considered appropriate for transitional adolescents (18-21) as well as adults (22+) but this does not constitute a new intended use. Sleepio has similar technological characteristics to Somryst, including software architecture, delivery of digital behavioral therapy through a mobile application, and therapeutic content. Software testing and pivotal clinical study results validate Sleepio towards its proposed Indications for Use. This validation reasonably assures that Sleepio is substantially equivalent to the predicate device. Further, Sleepio met all of the Special Controls per the requirements of the regulation (21 CFR 882.5801). Thus, Sleepio is substantially equivalent to Somryst.
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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.