(273 days)
Not Found
Yes
The device description explicitly states that the device represents "application of AI technology to endoscopic images" and is a "new AI-based CAD system".
No
The device is a computer-assisted reading tool designed to aid endoscopists in detecting lesions, not to provide therapy.
Yes
Explanation: The "Intended Use" states that the software is "designed to aid endoscopists in detecting colonic mucosal lesions (such as polyps and adenomas)", and the "Device Description" mentions that it is a "computer-aided diagnosis (CAD) system". These phrases directly indicate its role in diagnosis.
Yes
The device description explicitly states "this software (EW10-EC02), a new AI-based CAD system" and the intended use describes it as "This software is a computer-assisted reading tool". The summary focuses on software performance and does not mention any associated hardware components included with the device itself.
Based on the provided information, this device is likely an IVD (In Vitro Diagnostic).
Here's why:
- Intended Use: The device is designed to aid endoscopists in detecting colonic mucosal lesions during endoscopy examinations. While it's a "computer-assisted reading tool" and "not intended to replace clinical decision making," its primary function is to analyze images obtained from a patient's body (in vitro) to provide information about potential disease (lesions). This aligns with the definition of an IVD, which involves examining specimens derived from the human body to provide information for diagnosis, monitoring, or screening.
- Device Description: The description explicitly states it's an "AI-based CAD system" to support the detection of polyps in colonoscopic images. CAD systems for diagnosis are commonly classified as medical devices, and in this context, the analysis of images from within the body falls under the scope of IVDs.
- Input Imaging Modality: It uses standard White Light Imaging (WLI) and Linked Color Imaging (LCI) endoscopy imaging. These are methods of obtaining images from inside the body, which are then analyzed by the software.
- Anatomical Site: The analysis is performed on images of colonic mucosal lesions.
- Performance Studies and Key Metrics: The detailed performance studies, including sensitivity, specificity, PPV, NPV, and AUC, are typical metrics used to evaluate the performance of diagnostic devices, including IVDs. The clinical trial also evaluates diagnostic endpoints like Adenoma Per Colonoscopy (APC) and Positive Predictive Value (PPV).
- Predicate Device: The mention of a predicate device (K211951; GI Genius) which is also a computer-assisted detection device for colonoscopy, further suggests that this type of device is regulated as a medical device, and likely an IVD given its function.
While the device analyzes images rather than a physical specimen like blood or tissue in a lab, the regulatory definition of IVD can encompass devices that analyze images derived from the body for diagnostic purposes. The key is that the analysis is performed in vitro (outside the living organism, on the obtained images) to provide information relevant to diagnosis or screening.
Therefore, based on the intended use, device description, and the type of analysis performed on images from within the body for diagnostic purposes, this device fits the characteristics of an In Vitro Diagnostic medical device.
No
The letter does not explicitly state that the FDA has reviewed and approved or cleared a Predetermined Change Control Plan (PCCP) for this specific device.
Intended Use / Indications for Use
This software is a computer-assisted reading tool designed to aid endoscopists in detecting colonic mucosal lesions (such as polyps and adenomas) in real time during standard endoscopy examinations of patients undergoing screening and surveillance endoscopic mucosal evaluations. This software is used with standard White Light Imaging (WLI) and Linked Color Imaging (LCI) endoscopy imaging. This software is not intended to replace clinical decision making.
Product codes
ONP, QNP
Device Description
The subject device represents, an application of AI technology to endoscopic images to assist in detecting the presence of potential lesions. This development greatly contributes to improving the quality of colonoscopy.
Against this background, the company has developed this software (EW10-EC02), a new AI-based CAD system, to support Health Care Provider (HCP) detection of large intestine polyps in colonoscopic images. EW10-EC02 detects suspected large intestine polyps in the endoscope video image in real-time.
Mentions image processing
Yes
Mentions AI, DNN, or ML
Yes
Input Imaging Modality
standard White Light Imaging (WLI) and Linked Color Imaging (LCI) endoscopy imaging
Anatomical Site
colonic mucosal / Large intestine
Indicated Patient Age Range
The clinical study included subjects aged 45 or older.
Intended User / Care Setting
endoscopists
Description of the training set, sample size, data source, and annotation protocol
Not Found
Description of the test set, sample size, data source, and annotation protocol
Standalone performance testing was carried out using a dataset of 149 (WLI mode) and 144 (LCI mode) colonoscopy videos.
WLI dataset: 149 patients (119 with lesions, 30 without lesions), age range 23-85 (mean 56.6 +/- 10.8), 81% male, 100% Asian.
LCI dataset: 144 patients (114 with lesions, 30 without lesions), age range 23-85 (mean 56.5 +/- 10.7), 81% male, 100% Asian.
Lesion types included Adenoma, HP (Hyperplastic Polyps), and Others, with sizes ranging from 1mm to >=10mm and forms including Polypoid and Non-Polypoid.
The total number of frames in WLI was 1,359,980 (29,441 with lesions, 1,330,539 without lesions).
The total number of frames in LCI was 361,603 (26,589 with lesions, 335,014 without lesions).
The evaluation of sensitivity per lesion and FP Objects/Patient was conducted based on counting consecutive frames of each metric.
Summary of Performance Studies (study type, sample size, AUC, MRMC, standalone performance, key results)
- Software Verification and Validation: Conducted to validate for intended use, with passing results on all applicable unit, integration, and requirements testing.
- Non-clinical Performance Testing (Standalone Performance):
- Study Type: Standalone performance evaluation.
- Sample Size: 149 colonoscopy videos for WLI mode, 144 for LCI mode.
- Key Metrics (Object-level performance):
- Sensitivity per lesion: WLI mode: 95.1% (91.1-98.3%), LCI mode: 95.5% (91.5-98.7%).
- FP Objects/Patient: WLI mode: 1.42 (1.09-1.81), LCI mode: 0.76 (0.42-1.21).
- Sensitivity per lesion by subgroups: Adenoma (WLI 96.2%, LCI 96.1%), HP (WLI 92.3%, LCI 92.0%), Lesion Size 1-5mm (WLI 95.0%, LCI 93.8%), 6-9mm (WLI 97.1%, LCI 100%), >=10mm (WLI 90.9%, LCI 100%), Polypoid (WLI 97.8%, LCI 97.0%), Non-Polypoid (WLI 80.0%, LCI 87.0%).
- FP Objects/Patient by subgroups: Adenoma (WLI 1.44, LCI 0.71), HP (WLI 0.88, LCI 0.35), Lesion Size 1-5mm (WLI 1.41, LCI 0.79), 6-9mm (WLI 1.03, LCI 0.25), >=10mm (WLI 0.80, LCI 0), Polypoid (WLI 1.36, LCI 0.75), Non-Polypoid (WLI 0.83, LCI 0.18).
- Key Metrics (Frame-level performance):
- Sensitivity per frame (WLI): Case with lesion 71.9%, Screening 72.0%, Post-treatment surveillance 68.2%, Cases with Others polyps 73.4%.
- Sensitivity per frame (LCI): Case with lesion 81.7%, Screening 81.8%, Post-treatment surveillance 76.7%, Cases with Others polyps 98.6%.
- False Positive Rate per frame (WLI): All cases 5.08%, Screening 5.12%, Post-treatment surveillance 3.43%, Cases with Others polyps 4.14%, Cases without lesion 5.32%.
- False Positive Rate per frame (LCI): All cases 6.21%, Screening 6.22%, Post-treatment surveillance 4.01%, Cases with Others polyps 0.54%, Cases without lesion 6.42%.
- AUC: WLI mode: 0.79 (95% CI 0.77-0.80), LCI mode: 0.87 (95% CI 0.86-0.88).
- FROC Analysis: Curves demonstrating sensitivity per lesion vs. FPc per case.
- Conclusions: Achieved all criteria in both modes with good results for each subgroup on sensitivity and false positives. ROC-AUC and FROC analysis supported algorithm performance.
- Special Control Testing:
- No visually detectable differences in image quality.
- Video delay due to marker annotation was 4.00 frames average (66.7 msec).
- Real-time endoscopic video delay was 3.67 frames average (61.2 msec).
- Human Factors: Data from pivotal clinical study sufficient to support usability.
- Clinical Testing:
- Study Design: Multi-center, prospective, randomized controlled trial.
- Sample Size: 1,031 subjects analyzed (600 average risk, 565 follow-up).
- Co-Primary Endpoints:
- Adenoma per colonoscopy (APC): CAC group: 0.990 +/- 1.610, CC group: 0.849 +/- 1.484. Incidence Rate Ratio: 1.17 (1.01, 1.36), Difference: 0.141 (0.01, 0.28), P-Value: 0.018. Met success criteria.
- Positive predictive value (PPV): CAC: 48.6%, CC: 54.0%. Bootstrapped 95% CI: -9.56%, -1.48%. Met success criteria.
- Positive percent agreement (PPA): CAC: 60.7%, CC: 66.2%. Bootstrapped 95% CI: -10.50%, -2.30%.
- Secondary Endpoints:
- Polyp per colonoscopy (PPC): P-value
§ 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.
0
Image /page/0/Picture/0 description: The image shows the logo of the U.S. Food and Drug Administration (FDA). On the left is the Department of Health & Human Services logo. To the right of that is the FDA logo in blue, with the words "U.S. FOOD & DRUG ADMINISTRATION" in blue as well. The FDA is a federal agency responsible for regulating and supervising the safety of food, drugs, and other products.
December 15, 2023
FUJIFILM Corporation % Kotei Aoki Manager, Regulatory Affairs FUJIFILM Healthcare Americas Corporation 81 Hartwell Avenue, Suite 300 Lexington, MA 02421
Re: K230751
Trade/Device Name: EW10-EC02 Endoscopy Support Program Regulation Number: 21 CFR 876.1520 Regulation Name: Gastrointestinal Lesion Software Detection System Regulatory Class: Class II Product Code: ONP Dated: November 16, 2023 Received: November 16, 2023
Dear Kotei Aoki:
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"
1
(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).
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 OS 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).
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).
2
Sincerely,
Shanil P. Haugen -S
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
3
Indications for Use
510(k) Number (if known) K230751
Device Name Endoscopy Support Program EW10-EC02
Indications for Use (Describe)
This software is a computer-assisted reading tool designed to aid endoscopists in detecting colonic mucosal lesions (such as polyps and adenomas) in real time during standard endoscopy examinations of patients undergoing screening and surveillance endoscopic mucosal evaluations. This software is used with standard White Light Imaging (WLI) and Linked Color Imaging (LCI) endoscopy imaging. This software is not intended to replace clinical decision making.
| Type of Use (Select one or both, as applicable) |
|---|
| ------------------------------------------------- |
| Prescription Use (Part 21 CFR 801 Subpart D) | ☑ |
|---|---|
| Over-The-Counter Use (21 CFR 801 Subpart C) | ☐ |
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4
ട. 510(k) Summary
- a. Company Name, Address: FUJFILM Corporation 798 Miyanodai Kaisei-Machi Ashigarakami-Gun, Kanagawa, 258-8538, Japan
- b. Contact: Kotei Aoki Manager, Regulatory Affairs E-Mail: kotei.aoki@fujifilm.com Telephone: (765) 246-2931
- c. Date prepared December 15, 2023
| d. Subject Device | |
|---|---|
| 510(k) Applicant/Owner: | FUJIFILM Corporation |
| Device Name: | EW10-EC02 Endoscopy Support Program |
| Common Name: | EW10-EC02 |
| Classification Product Code: | QNP |
| Device Class: | Class II |
| Regulation Number: | 21 CFR 876.1520 |
| Regulation Description: | Gastrointestinal Lesion Software Detection System |
| Review Panel: | Gastroenterology/Urology |
5
e. Predicate Device
The EW10-EC02 Endoscopy Support Program is substantially equivalent to:
| 510(k) Number: | K211951 |
|---|---|
| Applicant: | Cosmo Artificial Intelligence - AI Ltd |
| Device Name: | GI Genius |
| Common Name: | GI Genius |
| Classification Product Code: | QNP |
| Device Class: | Class II |
| Regulation Number: | 21 CFR 876.1520 |
| Regulation Description: | Gastrointestinal Lesion Software Detection |
| System | |
| Review Panel: | Gastroenterology/Urology |
Device Description f.
There is an increasing interest in the application of artificial intelligence (AI) in health care to improve disease diagnosis, management, and the development of effective therapies. The remarkable development of AI from deep learning in recent years has increased the possibility of machines assisting assessments by human visual observation. The subject device represents application of AI technology to endoscopic images to assist in detecting the presence of potential lesions. This development greatly contributes to improving the quality of colonoscopy.
In recent years, computer-aided diagnosis (CAD) systems employing AI technologies have been approved and marketed as radiological medical devices for use with computed tomography (CT), X-ray, magnetic resonance imaging (MRI), and mammogram diagnostic images. In endoscopy as well, many images for diagnosis are taken. Since increasing the polyp detection rate is also in demand, CAD systems for endoscopy are being actively developed.
Against this background, the company has developed this software (EW10-EC02), a new AI-based CAD system, to support Health Care Provider (HCP) detection of large intestine polyps in colonoscopic images. EW10-EC02 detects suspected large intestine polyps in the endoscope video image in real-time.
g. Intended Use / Indications for Use
This software is a computer-assisted reading tool designed to aid endoscopists in detecting colonic mucosal lesions (such as polyps and adenomas) in real time during
6
standard endoscopy examinations of patients undergoing screening and surveillance endoscopic mucosal evaluations. This software is used with standard WLI (White Light Imaging) and LCI (Linked Color Imaging) endoscopy imaging. This software is not intended to replace clinical decision making.
h. Statement of Substantial Equivalence
A comparison of the technological characteristics between the subject and predicate device is provided in Table 1 below. EW10-EC02 and GI Genius (K211951) share the same intended use and similar indications and technological characteristics. The differences in indications and technological characteristics between the subject and predicate devices do not raise new concerns regarding safety and effectiveness as demonstrated by the non-clinical and clinical performance evaluation results. Therefore, the Company believes that GI Genius is an appropriate predicate device for EW10-EC02, and EW10-EC02 can be submitted as a 510(k) under product code QNP.
Comparison Table i.
| Table 1: Comparison of EW10-EC02 to GI Genius | |||
|---|---|---|---|
| -- | -- | ----------------------------------------------- | -- |
| Feature | EW10-EC02
Endoscopy Support Program
(Under Review) | GI Genius
(K211951) | Comment |
|------------------------------------------|----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|
| Manufacturer | FUJIFILM Corporation | Cosmo Artificial Intelligence – AI
Ltd | N/A |
| Product Code | QNP | QNP | Same |
| Intended Use /
Indications for
Use | This software is a
computer-assisted reading
tool designed to aid
endoscopists in detecting
colonic mucosal lesions
(such as polyps and
adenomas) in real time
during standard
endoscopy examinations
of patients undergoing
screening and
surveillance endoscopic
mucosal evaluations. This
software is used with
standard WLI (White
Light Imaging) and LCI
(Linked Color Imaging)
endoscopy imaging. This
software is not intended
to replace clinical
decision making. | The GI Genius System is
a computer-assisted
reading tool designed to
aid endoscopists in
detecting colonic mucosal
lesions (such as polyps
and adenomas) in real
time during standard
white-light endoscopy
examinations of patients
undergoing screening and
surveillance endoscopic
mucosal evaluations. The
GI Genius computer-
assisted detection device
is limited for use with
standard white-light
endoscopy imaging only.
This device is not
intended to replace
clinical decision making. | Substantially
Equivalent (The
only difference in
the intended use
indications for
use between the
subject device
and the predicate
device is the
EW10-EC02
Endoscopy
Support Program
can also be used
with LCI
endoscopy
imaging). |
| Site | Large intestine | Large intestine | Same |
| Modality | Colonoscopy | Colonoscopy | Same |
| CAD Function | Detection | Detection | Same |
| Method of reading | Concurrent read | Concurrent read | Same |
| Algorithm(s) | The EW10-EC02 Endoscopy | The GI Genius system utilizes an | Same |
7
| Feature | EW10-EC02
Endoscopy Support Program
(Under Review) | GI Genius
(K211951) | Comment |
|------------------|-----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|-----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|---------|
| | Support Program utilizes an
artificial intelligence-based
algorithm to perform the polyp
detection function. | artificial intelligence-based
algorithm to perform the polyp
detection function. | |
| | Algorithm failure leading to:
• False positives resulting in
unnecessary patient treatment; or
• False negatives resulting in
delayed patient treatment
Failure to identify lesions, resulting
in delayed patient treatment, due to
software/hardware failure including: | Algorithm failure leading to:
• False positives resulting in
unnecessary patient treatment; or
• False negatives resulting in
delayed patient treatment
Failure to identify lesions, resulting
in delayed patient treatment, due to
software/hardware failure including: | |
| Identified risks | • Incompatibility with hardware
and/or data source
• Inadequate mapping of software
architecture
• Degradation of image quality
• Prolonged delay of real-time
endoscopic video
False positive or false negative due
to user overreliance on the device | • Incompatibility with hardware
and/or data source
• Inadequate mapping of software
architecture
• Degradation of image quality
• Prolonged delay of real-time
endoscopic video
False positive or false negative due
to user overreliance on the device | Same |
Performance Data j.
The following testing was conducted for the EW10-EC02 Endoscopy Support Program.
• Software Verification and Validation
Software verification and validation was conducted for the EW10-EC02 Endoscopy Support Program to validate it for its intended use per the design documentation in line with recommendations outlined in General Principles of Software Validation, Guidance for Industry and FDA Staff. The EW10-EC02 Endoscopy Support Program demonstrated passing results on all applicable unit, integration, and requirements testing.
• Non-clinical Performance Testing
The purpose of the standalone performance testing is to demonstrate that the objectlevel, frame-level and overall algorithmic performance is sufficient to fulfill the indications for use of the EW10-EC02. The standalone performance evaluation was carried out using the dataset shown in Tables 2-4. This evaluation was performed in both WLI (White Light Imaging) and LCI (Linked Color Imaging) modes. Fujifilm performed this standalone performance testing using a total of 149 (WLI mode) and 144 (LCI mode) colonoscopy videos.
8
| Table 2: Patient demographics | ||
|---|---|---|
| Item | WLI | LCI |
| Number of Patients | 149 | |
| (With lesion:119, Without lesion: 30) | 144 | |
| (With lesion: 114, Without lesion: 30) | ||
| Age (range) | $56.6 \pm 10.8 (23 - 85)$ | $56.5 \pm 10.7 (23 - 85)$ |
| Male sex | 81 % | 81 % |
| Race | Asian 100% | Asian 100% |
| Number of Patients for | ||
| screening | 146 | 142 |
| Number of Patients for | ||
| surveillance | 3 | 2 |
hla 2: Dationt domagnaphia
Table 3: Detailed Dataset of standalone performance testing
| | | Total
number | Lesion Type | | | Lesion Size (mm) | | | Lesion Form | |
|------|----------------------|-----------------|-------------|----|--------|------------------|-----|------|----------------------|-------------------------------|
| Mode | Item | | Adenoma | HP | Others | 1-5 | 6-9 | ≥ 10 | Polypoid
(Type I) | Non-
Polypoid
(Type II) |
| WLI | Number
of lesions | 164 | 133 | 26 | 5 | 119 | 34 | 11 | 139 | 25 |
| LCI | Number
of lesions | 154 | 127 | 25 | 2 | 112 | 32 | 10 | 131 | 23 |
Table 4: Summary of Number of Cases and Frames
| Mode | Number of Cases | | Number of Frames
With lesion | | | Without
lesion | Total
number |
|------|-----------------|-------------------|---------------------------------|--------|--------|-------------------|-----------------|
| | With lesion | Without
lesion | Adenoma | HP | Others | | |
| WLI | 119 | 30 | 23,861 | 4,680 | 900 | 1,330,539 | 1,359,980 |
| | 149 | | | 29,441 | | | |
| LCI | 114 | 30 | 21,932 | 4,297 | 360 | 335,014 | 361,603 |
| | 144 | | | 26,589 | | | |
Based on the results in the case of evaluation per frame, the evaluation values for each case were calculated by counting the number of consecutive frames of each metric. Since lesion detection was performed and evaluated at the lesion level (object level) in the actual clinical cases, we set sensitivity per lesion as the primary endpoint. We evaluated whether the lower limit of the confidence interval (95%) of sensitivity per lesion of EW10-EC02, exceeds the criteria. We also evaluated FP Objects/Patient. The confidence interval (95%) was calculated by non-parametric cluster bootstrap analysis, considering within-patient correlation.
Standalone performance results
The evaluation results of object level performance for Standalone performance testing were shown in Tables 5-6 below.
| Table 5: Main evaluation results of object level performance for standalone performance testing | ||
|---|---|---|
| Item | Results | |
|---|---|---|
| Sensitivity per lesion | 95.1% | 95.5% |
9
| (Lesion-based sensitivity) | 91.1 - 98.3% | 91.5 - 98.7% |
|---|---|---|
| FP Objects/Patient | 1.42 | 0.76 |
| (Number of FPc per Case) | 1.09 - 1.81 | 0.42 - 1.21 |
| Target lesion | Sensitivity per Lesion | FP Objects/Patient | |||
|---|---|---|---|---|---|
| WLI mode | LCI mode | WLI mode | LCI mode | ||
| Lesion Type | Adenoma | 96.2% | |||
| (92.3 - 99.3%) | 96.1% | ||||
| (91.9 - 99.2%) | 1.44 | ||||
| (1.06 - 1.93) | 0.71 | ||||
| (.26 - 1.34) | |||||
| HP | 92.3% | ||||
| (80.0 - 100%) | 92.0% | ||||
| (79.2 - 100%) | 0.88 | ||||
| (0.38 - 1.46) | 0.35 | ||||
| (0.13 - 0.61) | |||||
| Lesion Size | |||||
| (mm) | 1-5 | 95.0% | |||
| (89.9-99.1%) | 93.8% | ||||
| (88.4-98.2%) | 1.41 | ||||
| (0.99 - 1.94) | 0.79 | ||||
| (0.31 - 1.47) | |||||
| 6-9 | 97.1% | ||||
| (90.6 -100%) | 100% | ||||
| (-) | 1.03 | ||||
| (0.60 - 1.50) | 0.25 | ||||
| (0 - 0.57) | |||||
| ≥10 | 90.9% | ||||
| (70.0 - 100%) | 100% | ||||
| (-) | 0.80 | ||||
| (0.20 - 1.50) | 0 | ||||
| (-) | |||||
| Lesion Form | Polypoid | 97.8% | |||
| (94.9 - 100%) | 97.0% | ||||
| (93.4-99.3%) | 1.36 | ||||
| (0.97 - 1.86) | 0.75 | ||||
| (0.28 - 1.36) | |||||
| Non- | |||||
| Polypoid | 80.0% | ||||
| (62.5 - 95.8%) | 87.0% | ||||
| (70.8 - 100%) | 0.83 | ||||
| (0.38 - 1.38) | 0.18 | ||||
| (0-0.41) | |||||
| Type | |||||
| & | |||||
| Size | 1-5mm | ||||
| Adenoma | 95.7% | ||||
| (90.5 - 100%) | 94.4% | ||||
| (88.3 - 98.9%) | 1.54 | ||||
| (1.04 - 2.07) | 0.83 | ||||
| (0.28 - 1.65) | |||||
| 6-9mm | |||||
| Adenoma | 96.7% | ||||
| (89.7 - 100%) | 100% | ||||
| (-) | 1.19 | ||||
| (0.69 - 1.69) | 0.24 | ||||
| (0 - 0.60) | |||||
| ≥10mm | |||||
| Adenoma | 100% | ||||
| (-) | 100% | ||||
| (-) | 1.00 | ||||
| (0.25 - 1.75) | 0 | ||||
| (-) | |||||
| Screening | 95.0% | ||||
| (91.0-98.2%) | 95.4% | ||||
| (91.5 - 98.7%) | 1.43 | ||||
| (1.09 - 1.82) | 0.77 | ||||
| (0.42 - 1.25) | |||||
| Post-treatment surveillance | 100% | ||||
| (-) | 100% | ||||
| (-) | 0.67 | ||||
| (0-2.0) | 0 | ||||
| (-) | |||||
| Cases with Others polyps | (without any identified polyps | ||||
| subgroup) | 80.0% | ||||
| (40.0 - 100%) | 100% | ||||
| (-) | 0.20 | ||||
| (0 - 0.60) | 0 | ||||
| (-) | |||||
| Cases without lesion | - | - | 1.80 | ||
| (1.07 - 2.67) | 1.03 | ||||
| (0.47 - 1.77) |
Table 6: Evaluation results for each target lesions and cases
Figure 1 showed how detection persistence in time (the duration of time a mark persists on the same target based on an IoU overlap criterion applied to the EW10-EC02 marks across frames) correlates with sensitivity per lesion and FP Objects /Patient. This testing considers repeated marking overlays of the same target (lesions and false positives) as a single statistical event. This allows for an estimate of the number of unique targets (or objects) identified by EW10-EC02 as a function of the time those targets persist in the field of view. In this standalone performance testing, WLI has about four times more evaluation images than LCI, so there is a difference in FP Objects/Patient, but the relationship between FP Objects/Patient and Sensitivity per lesion in both modes was almost equivalent.
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Image /page/10/Figure/1 description: The image contains two plots comparing sensitivity per lesion vs the number of false positives per case. The plot on the left is labeled WLI and the plot on the right is labeled LCI. Both plots show a similar trend, with sensitivity increasing rapidly as the number of false positives increases from 0 to 20, and then leveling off. The sensitivity reaches nearly 100% with a small number of false positives.
Figure 1: Figure demonstrating how detection persistence in time correlates with sensitivity per lesion and the number of FPc per "Case" (FP Objects/Patient); (L)WLI mode, (R)LCI mode
The frame-level performance is an assessment of the accuracy of the algorithm at sorting endoscopic images for quantification of false positives, false negatives, true positives, and true negatives. The image-level evaluation results were shown in the Table 7.
| Items | Results | ||
|---|---|---|---|
| WLI mode | LCI mode | ||
| Frame level | |||
| performance | Total number of TPF | 21,166 | 21,723 |
| Total number of TNF | 1,273,229 | 317,600 | |
| Total number of FPF | 69,075 | 22,454 | |
| Total number of FNF | 8,275 | 4,866 | |
| Sensitivity per | |||
| frame | Case with lesion (All lesion) | ||
| (Total number of TPF/ (Total number | |||
| of TPF+ Total number of FNF)) | 71.9% | ||
| % of polyps: 97.6% | |||
| (71.4 - 72.4%) | 81.7% | ||
| % of polyps: 99.4 % | |||
| (81.2 – 82.2%) | |||
| Case for Screening | 72.0% | ||
| (71.4 – 72.5%) | 81.8% | ||
| (81.3 – 82.2%) | |||
| Case for Post-treatment | |||
| surveillance | 68.2% | ||
| (64.3 – 72.0%) | 76.7% | ||
| (72.2 – 81.1%) | |||
| Cases with Others polyps | 73.4% | ||
| (70.8 – 76.7%) | 98.6% | ||
| (97.2 – 99.7%) | |||
| False Positive | |||
| Rate per frame | All cases | ||
| (FPF / Number of all frames) | 5.08% | ||
| (4.46 – 5.88%) | 6.21% | ||
| (4.45 – 8.31%) | |||
| Case for Screening | 5.12% | ||
| (4.49 – 5.92%) | 6.22% | ||
| (4.54 – 8.28%) | |||
| Case for Post-treatment | |||
| surveillance | 3.43% | ||
| (2.10 – 4.27%) | 4.01% | ||
| (2.44 – 12.9%) | |||
| Cases with Others polyps | 4.14% | ||
| (3.56 – 4.83%) | 0.54% | ||
| (0 – 1.07%) | |||
| Cases without lesion | 5.32% | ||
| (3.92 – 7.26%) | 6.42% | ||
| (4.12 – 9.36%) |
Table 7: Results of frame level performance in Standalone performance testing
Figures 3-4 showed the results of ROC and FROC analysis. The AUC values of ROC curves were respectively 0.79(WLI) and 0.87(LCI). These curve and value showed that the recognizer algorithm has high accuracy.
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Image /page/11/Figure/1 description: This image contains two ROC analysis graphs. The first graph is titled "ROC analysis in WLI mode (Overall view)" and has an AUC of 0.79 with a 95% confidence interval of 0.77-0.80. The second graph is titled "ROC analysis in LCI mode (Overall view)" and has an AUC of 0.87 with a 95% confidence interval of 0.86-0.88. Both graphs plot the True Positive Rate on the y-axis and the False Positive Rate on the x-axis.
Figure 3: ROC analysis; (L)WLI mode, (R)LCI mode
Image /page/11/Figure/3 description: The image contains two FROC analysis graphs, one in WLI mode and the other in LCI mode, both displaying an overall view. The x-axis of both graphs represents the number of FPc (False Positives per case), while the y-axis represents the sensitivity per lesion. The WLI mode graph shows the number of FPc per case ranging up to 200, while the LCI mode graph shows the number of FPc per case ranging up to 60. Both graphs show a similar trend, with sensitivity per lesion increasing rapidly at lower FPc values and then plateauing as FPc increases.
Figure 4: FROC analysis; (L)WLI mode, (R)LCI mode
Standalone Performance Conclusions
EW10-EC02 achieved all criteria in both modes and showed good results for each subgroup on both items of sensitivity and false positives. In addition, sensitivity analysis and FPc analysis further support robust results for EW10-EC02. ROC-AUC and FROC analysis also supports that performance of the EW10-EC02 algorithm.
Special Control Testing
- √ Pixel-level comparison of degradation of image quality due to the device; No visually detectable differences between images were found with the introduction of the EW10-EC02.
- √ Video delay due to marker annotation was 4.00 frames average (66.7 msec).
- √ Real-time endoscopic video delay due to the device was 3.67 frames average (61.2 msec).
• Human Factors
The data collected in the pivotal clinical study is sufficient to support usability as it demonstrates the ability to appropriately use the device in an actual clinical setting.
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•Clinical Testing
Study Design
This study was a multi-center, prospective, randomized controlled trial. Each subject had a colonoscopy, using FUJIFILM's High Definition (HD) endoscope, video processor and the EW10-EC02 endoscopy support program. The study was conducted at 12 centers in the United States.
Subjects met all eligibility criteria were randomly allocated to 1 of 2 arms:
- (1) CAC(Computer Assisted Colonoscopy) group: inspection with computer assisted colonoscopy. Or
- (2) CC(Conventional Colonoscopy) group: inspection with conventional colonoscopy.
As per standard of care, polyps were resected when found, and sent to histopathology.
Study Population
This prospective study enrolled a total of 1,166 subjects. Of these subjects enrolled. 135 subjects were excluded from the analysis due to exclusionary reasons. A total of 1,031 subjects were analyzed, 600 were average risk subjects undergoing average risk screening colonoscopy and 565 subjects scheduled for follow-up colonoscopy due to a previous history of polyps 3 years or greater.
These subjects provided an informed consent and were aged 45 or older. Patients were not enrolled if they were pregnant, refused to give an informed consent or had a history of colon resection, Inflammatory Bowel Disease (IBD), Familial Adenomatous Polynosis (FAP). severe end-stage cardiovascular/pulmonary/liver/renal disease. A key demographics is provided below:
| Total | Randomized to | |||
|---|---|---|---|---|
| n = 1031 | CAC | |||
| n = 509 | CC | |||
| n = 522 | P-Value | |||
| Age | $59.1 \pm 9.8$ | $58.9 \pm 9.5$ | $59.3 \pm 10.1$ | 0.498 |
| Sex | 0.304 | |||
| Male | 514 (49.9%) | 262 (51.5%) | 252 (48.3%) | |
| Female | 517 (50.1%) | 247 (48.5%) | 270 (51.7%) | |
| Hispanic | 57 (5.5%) | 25 (4.9%) | 32 (6.1%) | 0.391 |
| Race | 0.448 | |||
| 1 Caucasian | 743 (72.3%) | 363 (71.6%) | 380 (73.1%) | |
| 2 Af Am | 169 (16.5%) | 86 (17.0%) | 83 (16.0%) | |
| 3 Asian | 63 (6.1%) | 37 (7.3%) | 26 (5.0%) | |
| 4 Native Haw/PI | 4 (0.4%) | 2 (0.4%) | 2 (0.4%) | |
| 5 Native Am | 2 (0.2%) | 1 (0.2%) | 1 (0.2%) | |
| 6 Other | 46 (4.5%) | 18(3.6%) | 28 (5.4%) | |
| Missing | 4 | 2 | 2 | |
| Reason for the colonoscopy | 0.116 | |||
| 1 Screening Colonoscopy | 540 (52.4%) | 254 (49.9%) | 286 (54.8%) | |
| 2 Surveillance Colonoscopy | 491 (47.6%) | 255 (50.1%) | 236 (45.2%) |
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Equipment
The following Equipment was used in the study, as in real-world use of the device.
- . VP-7000 Video Processor
The Processor relays the image from the endoscope to a video monitor. The Processor provides the optional image enhancement function (BLI, LCI, FICE) at the user's option.
● BL-7000 LED Light Source
The Fujifilm endoscope employs fiber bundles to transmit light from the light source and subsequently to the body cavity. The Light Source employs four LED lamps. Brightness control is performed by the user.
● 700 series Colonoscopes
The Fujifilm 700 series Colonoscopes are intended for the visualization of the lower digestive tract, specifically for the observation, diagnosis, and endoscopic treatment of the rectum and large intestine. The endoscope is used in combination with FUJIFILM's video processors, light sources and peripheral devices such as monitor, printer, foot switch, and cart.
Study Endpoint
The purpose of this study was to demonstrate the superiority of colorectal polyp detection using computer assisted colonoscopy compared to conventional colonoscopy. To accomplish this objective, two primary endpoints were evaluated:
Co-Primary Endpoints
-
Adenoma per colonoscopy (APC)
APC, defined as the total number of histologically confirmed adenomas detected in the colonoscopy divided by the total number of colonoscopies. -
Positive predictive value (PPV)
PPV, defined as the total number of histologically confirmed adenomas detected during the colonoscopy, divided by the total number of excisions in the colonoscopy. -
Positive percent agreement (PPA)
PPA, defined as the total number of histologically confirmed Clinically Significant Excised Lesions , divided by the total number of excisions. For calculating this endpoint, clinically Significant Excised Lesions defined as follows: -
Neoplastic lesions (classical adenomas and carcinomas) י
-
Sessile serrated lesions (SSL) classified according to the serrated lesion . classification
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- Hyperplastic polyps (HP) of the proximal colon (caecum, ascending colon, י hepatic flexure and transverse colon), classified according to the serrated lesion classification.
Secondary Endpoint
Additional secondary endpoints were assessed:
- Adenoma detection rate (ADR) ●
ADR, defined as the proportion of patients with at least one histologically confirmed adenomas detected in the colonoscopy.
● Sessile serrated lesion per colonoscopy (SPC)
SPC, defined as the total number of histologically confirmed sessile serrated lession detected in the colonoscopy divided by the total number of colonoscopies.
● Sessile serrated lesion detection rate (SDR)
SDR, defined as the proportion of patients with at least one histologically confirmed sessile serrated lesion detected in the colonoscopy.
● Polyp per colonoscopy (PPC)
PPC, defined as the total number of histologically confirmed clinically relevant detected in the colonoscopy divided by the total number of colonoscopies.
● Polyp detection rate (PDR)
PDR, defined as the proportion of patients with at least one histologically confirmed clinically relevant polyp detected in the colonoscopy.
-
Adverse events
Adverse Events number and severity. -
Cecal intubation rates and withdrawal times
-
Serrated Lesions per Colonoscopy (SLPC)
SLPC, defined as the number of histologically confirmed serrated detected, divided by the total number of colonoscopies.
● Serrated Lesions Detection Rate (SLDR)
SLDR, defined as the proportion of patients with at least one histologically confirmed serrated lesions detected.
● Advanced Adenoma Detection Rate (aADR)
aADR, defined as proportion of patients with at least one histologically confirmed adenoma > 10 mm or any adenoma 10 mm) hyperplastic polyps of the proximal colon resected, divided by the total number of excisions in the colonoscopy.
Study Primary Results
The study met primary success criteria with APC in CAC being superior with a p value of 0.018. PPV criteria was also met with a margin of -9.56%.
Adenoma Per Colonoscopy: APC
| | Total
n = 1031 | Randomized to | | Incidence Rate
Ratio
(95% CI) | Difference in APC
(95% CI) | P-Value |
|-------------------------------------|-------------------|----------------|---------------|-------------------------------------|-------------------------------|---------|
| | | CAC
n = 509 | CC
n = 522 | | | |
| Mean Number of
Adenomas (+/- sd) | 0.919 ± 1.548 | 0.990 ± 1.610 | 0.849 ± 1.484 | 1.17 (1.01, 1.36) | 0.141 (0.01, 0.28) | 0.018 |
Positive Predictive Value: PPV
| Total | Randomized to | |||
|---|---|---|---|---|
| n = 1857 | CAC | |||
| n = 1037 | CC | |||
| n = 820 | Bootstrapped | |||
| 95% Confidence Interval | ||||
| Adenoma Detected | 947 (51.0%) | 504 (48.6%) | 443 (54.0%) | -9.56%, -1.48% |
Positive Percent Agreement (PPA)
| | Total | Randomized to | | Bootstrapped
(95% CI) |
|-------------------|--------------|-----------------|---------------|--------------------------|
| | n = 1857 | CAC
n = 1037 | CC
n = 820 | |
| Positive Detected | 1172 (63.1%) | 629 (60.7%) | 543 (66.2%) | -10.50%, -2.30% |
Study Secondary Results
Additional secondary endpoints of note were:
- . Polyp per colonoscopy (PPC) with a p-value