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.

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.

AI/ML Overview

Based on the provided text, the EndoScreener device's acceptance criteria and the study proving it meets these criteria are described. However, the text does not provide a table of acceptance criteria with reported device performance values. It only states that "acceptable performance was obtained" and "the polyp detection accuracy observed was as expected."

Here's an attempt to extract the available information and structure it according to your request, with noted limitations due to the sparse details in the provided document:

Acceptance Criteria and Device Performance Study for EndoScreener

The EndoScreener is a computer-assisted detection device intended for real-time automatic detection of polyps in colonoscopy video streams. The performance of the device was evaluated through validation studies and a multi-center randomized controlled trial.

1. Table of Acceptance Criteria and Reported Device Performance

Acceptance Criteria Category	Specific Metric	Acceptance Threshold (Implicit)	Reported Device Performance
Per-Image Performance	Sensitivity	(Not specified, but "acceptable")	"acceptable performance was obtained"
	Specificity	(Not specified, but "acceptable")	"acceptable performance was obtained"
Per-Polyp Performance	Sensitivity	(Not specified, but "acceptable")	"acceptable performance was obtained"
	AUC (Area Under Curve)	(Not specified, but "acceptable")	"acceptable performance was obtained"
Clinical Efficacy (RCT)	Adenoma Miss Rate (AMR)	Significantly lower than control	Significantly lower in CADe-first group
	Adenoma per Colonoscopy (APC)	Higher than control	Higher in CADe-first group
Technical Performance	Imaging Degradation	None	"no imaging degradation"
	End-to-end Latency	Ignorable	"ignorable end-to-end latency"
	Functionality	As intended	"functioned as intended"
	Polyp Detection Accuracy	As expected	"polyp detection accuracy observed was as expected"

Note: The document only provides qualitative statements regarding "acceptable" or "as expected" performance for the per-image and per-polyp metrics, and does not list the specific quantitative acceptance thresholds or the actual numerical performance values achieved on these metrics.

2. Sample Sizes and Data Provenance

Test Set (Validation Datasets):
- Sample Size: 1,138 consecutive polyp patients (for histology-confirmed dataset). The document mentions "multiple datasets" but only specifies the size for one of them.
- Data Provenance: Not explicitly stated for the validation datasets regarding country of origin or whether it was retrospective/prospective. It's implied these were pre-existing datasets used for algorithm validation.
Test Set (Clinical Trial):
- Sample Size: 223 patients.
- Data Provenance: Multi-center randomized controlled trial performed at four United States academic medical centers. The study design (back-to-back colonoscopy procedures with randomization to CADe-routine and Routine-CADe groups) indicates it was a prospective study.

3. Number of Experts and Qualifications for Ground Truth (Test Set)

Validation Datasets: The document states the dataset for evaluating per-image and per-polyp performance used "histology confirmation." This suggests that the ground truth for polyps specifically was established by pathology/histology, not by a panel of medical experts. It does not mention experts used for image-level ground truth.
Clinical Trial: The ground truth for the clinical trial would be the confirmed polyp findings from the colonoscopy procedures. The primary endpoint, adenoma miss rate, implies that polyps were confirmed (likely by histology after removal). The document does not specify the number of endoscopists or their specific qualifications for establishing ground truth in terms of polyp presence/absence or adenoma identification within the study. However, it states the device is for "licensed endoscopists who perform colonoscopy in adults," implying these are the experts conducting the procedures.

4. Adjudication Method for the Test Set

Validation Datasets: For the 1,138 polyp patients, ground truth was established by "histology confirmation." This indicates that post-procedure pathological analysis of excised tissue was the definitive method for polyp presence. This doesn't typically involve a multi-reader visual adjudication process.
Clinical Trial: The study design (tandem colonoscopy, randomized controlled trial) focused on actual clinical outcomes (adenoma miss rate, adenoma per colonoscopy). Ground truth would derive from the findings of the colonoscopy procedure itself, including subsequent histology for removed polyps. There is no mention of a separate expert adjudication panel for the images/videos from the clinical trial data.

5. Multi-Reader Multi-Case (MRMC) Comparative Effectiveness Study

Yes, a form of comparative effectiveness study was done, but not explicitly described as a traditional MRMC study. The document describes a "multi-center, tandem colonoscopy, randomized controlled trial."
Effect Size of Human Readers Improvement with AI vs. without AI Assistance:
- The primary endpoint, adenoma miss rate (AMR), was "significantly lower in CADe-first group" compared to the Routine-CADe group. This directly indicates an improvement in detection when AI was used first.
- The 1st pass adenoma per colonoscopy (APC) was "higher in the CADe-first group." This also indicates an improvement in the number of adenomas detected by the endoscopist when aided by the AI.
- The document states these were "significant" improvements, but does not provide the numerical effect sizes (e.g., specific percentage reduction in AMR, or specific increase in APC per colonoscopy).

6. Standalone (Algorithm Only) Performance Study

Yes. The nonclinical testing section explicitly states that "performance was evaluated on a dataset of 1,138 consecutive polyp patients with histology confirmation and acceptable performance was obtained" for "per-image sensitivity and specificity as well as per-polyp sensitivity and AUC." This describes the algorithm's standalone performance preceding the clinical trial.

7. Type of Ground Truth Used

For the standalone performance evaluation: Histology confirmation for polyps.
For the clinical trial: Clinical outcomes data (adenoma miss rate, adenoma per colonoscopy) based on colonoscopy findings and confirmed histology.

8. Sample Size for the Training Set

Not specified. The document mentions the device uses a "customized deep learning model" but provides no information about the size or characteristics of the data used to train this model.

9. How the Ground Truth for the Training Set was Established

Not specified. Given that it's a deep learning model, it would require annotated data for training, but the document does not describe the process by which this training data was annotated or its ground truth established.

Summary

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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.

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

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.

Regulation Number and Section

§ 876.1520 Gastrointestinal lesion software detection system.

(a)
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.(b)
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.