K211326 · Chengdu Wision Medical Device Co., Ltd. · QNP · Nov 19, 2021 · Gastroenterology, Urology
Device Facts
Record ID
K211326
Device Name
EndoScreener
Applicant
Chengdu Wision Medical Device Co., Ltd.
Product Code
QNP · Gastroenterology, Urology
Decision Date
Nov 19, 2021
Decision
SESE
Submission Type
Traditional
Regulation
21 CFR 876.1520
Device Class
Class 2
Attributes
AI/ML, Software as a Medical Device
AI Performance
Output
Algorithm
Acceptance
Observed
Dev DS
Dev Readers
Test DS
Test Readers
Colorectal polyp detection
Customized deep learning model
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1,138 consecutive polyp patients with histology confirmation
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Multi-center, tandem colonoscopy, randomized controlled trial at four United States academic medical centers including 223 patients
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Indications for Use
EndoScreener is intended as a stand-alone software for real-time automatic detection of polyps in colonoscopy video stream during the procedure. Physicians are responsible for reviewing the identified areas of suspect polyps presented by EndoScreener and confirming the presence or absence of a polyp on the evaluation of the colonoscopy image on their own medical judgment. EndoScreener is not intended to replace a full patient evaluation, nor is it intended to be relied upon to make or confirm a diagnosis.
Device Story
EndoScreener is a computer-aided detection (CADe) software for real-time polyp identification during colonoscopy. It ingests live colonoscopy video streams; processes frames using a deep learning algorithm to detect potential polyps; and outputs visual indicators (blue bounding boxes) overlaid on the monitor. It may also provide audible alerts. Used in clinical settings by endoscopists, the device serves as an adjunct to standard visual inspection. Physicians must review and confirm all detections based on their own medical judgment; the device does not replace clinical evaluation or confirm diagnoses. By highlighting suspicious lesions, it aims to assist clinicians in identifying polyps, potentially reducing adenoma miss rates.
Clinical Evidence
Clinical evidence includes a multi-center, tandem colonoscopy, randomized controlled trial at four U.S. academic medical centers (n=223 patients). The study compared CADe-assisted colonoscopy to routine colonoscopy. Primary endpoints included adenoma miss rate (AMR) and adenoma per colonoscopy (APC); results showed significantly lower AMR and higher APC in the CADe-first group. Nonclinical validation included testing on a dataset of 1,138 polyp patients with histology confirmation, evaluating per-image sensitivity/specificity, per-polyp sensitivity, and AUC. Bench testing confirmed no imaging degradation and negligible latency.
Technological Characteristics
Software-based CADe system; utilizes a deep learning model for real-time image analysis. Inputs: colonoscopy video stream. Outputs: visual bounding box overlays and optional audible alerts. Operates in real-time. Designed for integration with existing endoscopy hardware.
Indications for Use
Indicated for use by licensed endoscopists performing white light colonoscopy in adults for real-time automatic detection of polyps.
Regulatory Classification
Identification
A gastrointestinal lesion software detection system is a computer-assisted detection device used in conjunction with endoscopy for the detection of abnormal lesions in the gastrointestinal tract. This device with advanced software algorithms brings attention to images to aid in the detection of lesions. The device may contain hardware to support interfacing with an endoscope.
Special Controls
In combination with the general controls of the FD&C Act, the gastrointestinal lesion software detection system is subject to the following special controls:
*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 detection of gastrointestinal lesions and evaluation of all adverse events.
(2) Non-clinical performance testing must demonstrate that the device performs as intended under anticipated conditions of use. Testing must include:
(i) Standalone algorithm performance testing;
(ii) Pixel-level comparison of degradation of image quality due to the device;
(iii) Assessment of video delay due to marker annotation; and
(iv) Assessment of real-time endoscopic video delay due to the device.
(3) Usability assessment must demonstrate that the intended user(s) can safely and correctly use the device.
(4) Performance data must demonstrate electromagnetic compatibility and electrical safety, mechanical safety, and thermal safety testing for any hardware components of the device.
(5) Software verification, validation, and hazard analysis must be provided. Software description must include a detailed, technical description including the impact of any software and hardware on the device's functions, the associated capabilities and limitations of each part, the associated inputs and outputs, mapping of the software architecture, and a description of the video signal pipeline.
(6) Labeling must include:
(i) Instructions for use, including a detailed description of the device and compatibility information;
(ii) Warnings to avoid overreliance on the device, that the device is not intended to be used for diagnosis or characterization of lesions, and that the device does not replace clinical decision making;
(iii) A summary of the clinical performance testing conducted with the device, including detailed definitions of the study endpoints and statistical confidence intervals; and
(iv) A summary of the standalone performance testing and associated statistical analysis.
Predicate Devices
GI Genius (DEN 200055)
Submission Summary (Full Text)
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November 19, 2021
Chengdu Wision Medical Device Co., LTD. % John Smith Partner Hogan Lovells US LLP 555 Thirteenth St, NW Washington, DC 20004
Re: K211326
Trade/Device Name: EndoScreener Regulation Number: 21 CFR 876.1520 Regulation Name: Gastrointestinal lesion software detection system Regulatory Class: Class II Product Code: QNP Dated: November 18, 2021 Received: November 18, 2021
Dear John Smith:
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.
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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 803) for devices or postmarketing safety reporting (21 CFR 4, Subpart B) for combination products (see https://www.fda.gov/combination-products/guidance-regulatoryinformation/postmarketing-safety-reporting-combination-products); good manufacturing practice requirements as set forth in the quality systems (QS) regulation (21 CFR Part 820) for devices or current good manufacturing practices (21 CFR 4, Subpart A) for combination products; and, if applicable, the electronic product radiation control provisions (Sections 531-542 of the Act); 21 CFR 1000-1050.
Also, please note the regulation entitled, "Misbranding by reference to premarket notification" (21 CFR Part 807.97). For questions regarding the reporting of adverse events under the MDR regulation (21 CFR Part 803), please go to https://www.fda.gov/medical-device-safety/medical-device-reportingmdr-how-report-medical-device-problems.
For comprehensive regulatory information about mediation-emitting products, including information about labeling regulations, please see Device Advice (https://www.fda.gov/medicaldevices/device-advice-comprehensive-regulatory-assistance) and CDRH Learn (https://www.fda.gov/training-and-continuing-education/cdrh-learn). Additionally, you may contact the Division of Industry and Consumer Education (DICE) to ask a question about a specific regulatory topic. See the DICE website (https://www.fda.gov/medical-device-advice-comprehensive-regulatoryassistance/contact-us-division-industry-and-consumer-education-dice) for more information or contact DICE by email (DICE@fda.hhs.gov) or phone (1-800-638-2041 or 301-796-7100).
Sincerely,
Shanil P. Haugen, Ph.D. Assistant Director DHT3A: Division of Renal, Gastrointestinal, Obesity and Transplant Devices OHT3: Office of GastroRenal, ObGyn, General Hospital and Urology Devices Office of Product Evaluation and Quality Center for Devices and Radiological Health
Enclosure
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510(k) Number (if known) K211326
Device Name
EndoScreener Indications for Use (Describe)
EndoScreener is intended as a stand-alone software for real-time automatic detection of polyps in colonoscopy video stream during the procedure.
Physicians are responsible for reviewing the identified areas of suspect polyps presented by EndoScreener and confirming the presence or absence of a polyp on the evaluation of the colonoscopy image on their own medical judgment. EndoScreener is not intended to replace a full patient evaluation, nor is it intended to be relied upon to make or confirm a diagnosis.
EndoScreener is indicated for use by licensed endoscopists who perform colonoscopy in adults. EndoScreener is indicated for use with white light colonoscopy.
Type of Use (Select one or both, as applicable)
区 Prescription Use (Part 21 CFR 801 Subpart D)
□ Over-The-Counter Use (21 CFR 801 Subpart C)
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# 510(k) SUMMARY
## Changdu Wision's EndoScreener
# Submitter
Chengdu Wision Medical Device Co., Ltd. Unit 802, Floor 8, Building 17, Yintaicheng No.1999 Yizhou Road, Wuhou District
Chengdu, Sichuan, China, 610041
Phone: +86 139-1030-8383
Contact Person: JingJia Liu
Date Prepared: November 16, 2021
Name of Device: EndoScreener
Common or Usual Name: Computer aided detection software for colorectal polyps
Classification Name: Gastrointestinal lesion software detection system
Requlatory Class: Class II (21 CFR 876.1520)
Product Code: QNP
Predicate Device: GI Genius (DEN 200055)
#### Device Description
The EndoScreener is a computer-assisted detection device for colorectal polyps. EndoScreener takes as input colonoscopy video stream from an endoscopy device, which is analyzed in real-time. The device output consists of blue boxes overlaid onto the colonoscopy images to highlight regions of potential polyp. EndoScreener also has the option to sound an alert to the physicians who perform the colonoscopy when a polyp has been detected. Following detection by EndoScreener, the physician must confirm the EndoScreener findings based on his/her own medical judgment.
#### Intended Use / Indications for Use
EndoScreener is intended as a stand-alone software for real-time automatic detection of polyps in colonoscopy video stream during the procedure.
Physicians are responsible for reviewing the identified areas of suspect polyps presented by EndoScreener and confirming the presence or absence of a polyp on the evaluation of the
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colonoscopy image on the monitor and their own medical judgment. EndoScreener is not intended to replace a full patient evaluation, nor is it intended to be relied upon to make or confirm a diagnosis.
EndoScreener is indicated for use by licensed endoscopists who perform colonoscopy in adults. EndoScreener is indicated for use with white light colonoscopy.
### Summary of Technological Characteristics
At a high level, the subject and predicate devices are based on the following same technological elements:
- . Both the EndoScreener and the GI Genius use artificial intelligence algorithms to assist clinicians in detecting colon polyps colonoscopy examination.
- Both devices take as input a colonoscopy video stream from an endoscopy device and provide as an output a bounding box that highlights the detected polyps.
- Both devices are used in real-time to aid the clinicians in identifying abnormal lesions. .
The following technological differences exist between the subject and predicate devices:
- . The subject device uses a customized deep learning model, which is likely to be slightly different from the deep learning model and customization used by GI Genius.
### Performance Data
In the nonclinical testing of the subject device included validation of the deep learning algorithm on multiple datasets to evaluate per-image sensitivity and specificity as well as per-polyp sensitivity and AUC. Specifically, performance was evaluated on a dataset of 1,138 consecutive polyp patients with histology confirmation and acceptable performance was obtained. For all assessments performed, the EndoScreener functioned as intended and the polyp detection accuracy observed was as expected. Endoscopic imaging degradation and latency due to the device were also evaluated, with appropriate hardware components, and the software device produced no imaging degradation and ignorable end-to-end latency.
EndoScreener performance was also evaluated in a multi-center, tandem colonoscopy, randomized controlled trial, performed at four United States academic medical centers. The study included 223 patients with screening and surveillance indications, whom were randomized to CADe-routine group and Routine-CADe group for back-to-back colonoscopy procedures. The primary endpoint adenoma miss rate (AMR) was significantly lower in CADefirst group and the 1st pass adenoma per colonoscopy (APC) was higher in the CADe-first group.
Based on the clinical performance as documented in the pivotal clinical study, the EndoScreener has a safety and effectiveness profile that is similar to the predicate device.
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### Conclusions
The EndoScreener is as safe and effective as the GI Genius. The EndoScreener has the same intended uses and similar indications, technological characteristics, and principles of operation as its predicate device. The minor differences in indications do not alter the intended use of the device and do not affect its safety and effectiveness when used as labeled. In addition, the minor technological differences between the EndoScreener and its predicate device raise no new issues of safety or effectiveness. Performance data demonstrate that the EndoScreener is as safe and effective as GI Genius. Thus, the EndoScreener is substantially equivalent.
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.