(201 days)
Not Found
Yes
The device description explicitly states that CADDIE is "cloud based artificial intelligence medical device software" and mentions "Cecum AI algorithm".
No
The device is intended to assist in detection and does not provide therapy or treatment.
No
Explanation: The "Intended Use" explicitly states that the device "is not intended to be relied upon to make a primary interpretation of endoscopic procedures, medical diagnosis, or recommendations of action for patients." It is purely an assistive tool for detection, and the medical professional remains responsible for the final diagnosis.
Yes
The device description explicitly states that CADDIE is "cloud based artificial intelligence medical device software" and that it "interfaces with the video feed generated by an endoscopic video processor". This indicates it is a software component that processes data from existing hardware, without including its own hardware.
Based on the provided information, the CADDIE device is not an In Vitro Diagnostic (IVD).
Here's why:
- IVDs are used to examine specimens derived from the human body. The CADDIE device analyzes real-time video feed from an endoscope during a colonoscopy. This is direct imaging of the inside of the body, not the examination of a specimen taken from the body.
- The intended use is to assist in the detection of suspected polyps during a procedure. The device is a tool to aid the gastroenterologist during the colonoscopy itself, not a test performed on a sample in a laboratory setting.
- The device description explicitly states it interfaces with the video feed generated by an endoscopic video processor. This confirms it's working with live or recorded video data from the procedure, not a biological sample.
While the device uses AI and image processing and provides data that contributes to a medical assessment, its function and the type of input it processes (live video from within the body) clearly place it outside the definition of an In Vitro Diagnostic device.
No
The clearance 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
The CADDIE computer-assisted detection device is intended to assist the gastroenterologist in detecting suspected colorectal polyps only. The gastroenterologist is responsible for reviewing CADDIE suspected polyp areas and confirming the presence or absence of a polyp based on their own medical judgment.
CADDIE is not intended to replace a full patient evaluation, nor is it intended to be relied upon to make a primary interpretation of endoscopic procedures, medical diagnosis, or recommendations of action for patients. The CADDIE computer-assisted detection device is limited for use with standard white-light endoscopy imaging only.
Product codes (comma separated list FDA assigned to the subject device)
QNP, SBX
Device Description
CADDIE is cloud based artificial intelligence medical device software. CADDIE interfaces with the video feed generated by an endoscopic video processor during a colonoscopy procedure.
The software is intended to be used by trained and qualified healthcare professionals as an accompaniment to video endoscopy for the purpose of drawing attention to regions with visual characteristics consistent with colonic mucosal lesions (such as polyps and adenomas).
CADDIE analyses the data from the endoscopic video processor in real-time and provides information to aid the endoscopist in detecting suspected colorectal polyps, if they are in the field of view of the endoscope.
The areas highlighted by CADDIE are not to be interpreted as definite polyps or adenomas. The responsibility to make a decision as to whether or not a highlighted region contains a polyp or is an adenoma lies with the user. The endoscopist is responsible for reviewing CADDIE suspected polyp areas and confirming the presence or absence of a polyp and its classification based on their own medical judgement.
Mentions image processing
Yes
Mentions AI, DNN, or ML
Yes
Input Imaging Modality
standard white-light endoscopy imaging
Anatomical Site
colorectal, colonic mucosal
Indicated Patient Age Range
45 and over
Intended User / Care Setting
trained and qualified healthcare professionals / Hospitals and clinics or in other secure endoscopy units where colonoscopies are performed.
Description of the training set, sample size, data source, and annotation protocol
Not Found (Development datasets mentioned but not explicitly as training)
Description of the test set, sample size, data source, and annotation protocol
Polyp Detection Standalone Bench-testing Dataset:
A set of recorded colonoscopy videos was analyzed by CADDIE and the results were compared to the historical control (known polyp status per frame).
Sample size: 389 subjects.
Annotation was performed on a per-frame basis, where a team of trained clinical annotators labelled polyp structures with a bounding box, followed by an additional layer of review by a separate team of experts (with over 2000 endoscopic procedures experience). Each polyp was histologically confirmed. These polyp annotations were used as ground truth reference standards.
Cecum AI Standalone Bench-testing Dataset:
A set of recorded colonoscopy frames were analyzed by the Cecum AI and the results were compared to the historical control (known cecal structure status per frame).
Total frames: 5092 (397 static positive, 1041 static negative, 2436 video positive, 1218 video negative).
Annotation was performed on a per-frame basis, where a team of trained clinical annotators labelled cecal structures with a bounding box. These annotations were used as ground truth reference standards.
Summary of Performance Studies (study type, sample size, AUC, MRMC, standalone performance, key results)
Special Control Technical Testing:
- Pixel-level comparison of degradation of image quality due to the device: average per-pixel difference was 0.94 and 0.87 (CV-1500 and CV-190).
- Assessment of video delay due to marker annotation: average video delay was 32.59ms and 33.61ms (CV-1500 and CV-190).
- Assessment of real-time endoscopic video delay due to the device: average video delay was 32.50ms and 32.50ms (CV-1500 and CV-190).
Human Factors and Usability Testing:
Found to be safe and effective for intended users, uses, and environments.
Polyp Detection Standalone Bench-testing Dataset (Non-Clinical Performance Testing):
- Object-level true positive rate (TPR): 98.27% [97.33, 99.20]
- Frame-Level FPR: 1.54% [1.31, 1.76]
- Object-level false positive rate (FPR): 0.89 [0.79, 1.00]
- Frame-Level TPR: 54.92% [53.02, 56.81]
- AUC for the algorithm ROC curve: Model AUC 0.79601, Bootstrap AUC 0.79609 [0.79600, 0.79619]
Cecum AI Standalone Bench-testing Dataset (Non-Clinical Performance Testing):
- Frame-level accuracy: 90.27% [88.73, 91.80]
- Frame-level TPR: 94.05% [91.58, 96.52]
- Frame-level FPR: 12.08% [10.14, 14.02]
- AUC for the algorithm ROC curve: 90.28 [90.14, 90.41]
Summary of Clinical Performance:
- Study type: prospective, multi-center, MRMC, randomized controlled, parallel group trial.
- Sample size: 841 patients in the modified Intention to Treat (mITT) population (417 CADe, 424 SoC).
- Key results:
- Primary Endpoints:
- APC (Adenomas per Colonoscopy): CADe arm 0.82 ± 1.40, SoC arm 0.62 ± 1.19. Ratio (CADe/SoC) 1.33 (1.06, 1.67), P-value 0.01.
- PPA (Positive Percent Agreement): CADe arm 53.9%, SoC arm 53.4%. % difference 0.5% (-5.0%, ∞).
- Secondary Endpoints:
- ADR (Adenoma Detection Rate): CADe arm 42.9%, SoC arm 35.9%. Difference 7.1% (0.5%, 13.7%), P-value 0.04.
- SSAPC (Sessile serrated adenomas per colonoscopy): CADe arm 0.09 ± 0.46, SoC arm 0.04 ± 0.21. Ratio (CADe/SoC) 2.15 (1.07, 4.32), P-value 0.03.
- SSAPC*: CADe arm 0.08 ± 0.45, SoC arm 0.03 ± 0.16. Ratio (CADe/SoC) 3.30 (1.41, 7.57), P-value 0.006.
- NPPC (Neoplastic Polyps Per Colonoscopy): CADe arm 0.91 ± 1.48, SoC arm 0.66 ± 1.20. Ratio (CADe/SoC) 1.39 (1.12, 1.73), P-value 0.002.
- PPC (Polyps Per Colonoscopy): CADe arm 1.41 ± 1.85, SoC arm 1.06 ± 1.53. Ratio (CADe/SoC) 1.35 (1.13, 1.61), P-value 0.001.
- Withdrawal time (All procedures): CADe arm 11 [8, 15], SoC arm 10 [8, 13]. Ratio 1.10 (1.05, 1.15), P-value
- Primary Endpoints:
§ 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 contains 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, which is a blue square with the letters "FDA" in white. To the right of the blue square is the text "U.S. FOOD & DRUG ADMINISTRATION" in blue.
July 24, 2024
Odin Medical Limited Luke Sampson, COO 43-45 Foley Street London. W1W 7TS United Kingdom
Re: K240044
Trade/Device Name: CADDIE Regulation Number: 21 CFR 876.1520 Regulation Name: Gastrointestinal Lesion Software Detection System Regulatory Class: Class II Product Code: QNP, SBX Dated: June 24, 2024 Received: June 24, 2024
Dear Luke Sampson:
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).
1
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).
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,
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
2
Indications for Use
510(k) Number (if known) K240044
Device Name CADDIE
Indications for Use (Describe)
The CADDIE computer-assisted detection device is intended to assist the gastroenterologist in detecting suspected colorectal polyps only. The gastroenterologist is responsible for reviewing CADDIE suspected polyp areas and confirming the presence or absence of a polyp based on their own medical judgment.
CADDIE is not intended to replace a full patient evaluation, nor is it intended to be relied upon to make a primary interpretation of endoscopic procedures, medical diagnosis, or recommendations of action for patients. The CADDIE computer-assisted detection device is limited for use with standard white-light endoscopy imaging only.
| Type of Use (Select one or both, as applicable) |
|---|
| ------------------------------------------------- |
X Prescription Use (Part 21 CFR 801 Subpart D)
| Over-The-Counter Use (21 CFR 801 Subpart C)
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| Date Prepared: | 23-Jul-24 |
|---|---|
| Odin Medical Ltd, | |
| 43-45 Foley Street, | |
| London W1W 7TS | |
| United Kingdom | |
| Tel - +44 (0)7957 948411 | |
| Official Contact: | Luke Sampson - COO |
| Submission Correspondent: | Luke Sampson - COO |
| Proprietary or Trade Name: | CADDIE |
| 510(k) number: | K240044 |
| Common/Usual Name: | Gastrointestinal Lesion Software Detection System |
| Classification CFR: | 21 CFR 876.1520 |
| Classification Code: | QNP, SBX |
| Classification Name: | Gastrointestinal Lesion Software Detection System |
| Class: | Class II |
| Predicate Device: | DEN200055 GI Genius - Cosmo Artificial Intelligence. ALTD |
Device Description:
CADDIE is cloud based artificial intelligence medical device software. CADDIE interfaces with the video feed generated by an endoscopic video processor during a colonoscopy procedure
The software is intended to be used by trained and qualified healthcare professionals as an accompaniment to video endoscopy for the purpose of drawing attention to regions with visual characteristics consistent with colonic mucosal lesions (such as polyps and adenomas).
CADDIE analyses the data from the endoscopic video processor in real-time and provides information to aid the endoscopist in detecting suspected colorectal polyps, if they are in the field of view of the endoscope.
The areas highlighted by CADDIE are not to be interpreted as definite polyps or adenomas. The responsibility to make a decision as to whether or not a highlighted region contains a polyp or is an adenoma lies with the user. The endoscopist is responsible for reviewing CADDIE suspected polyp areas and confirming the presence or absence of a polyp and its classification based on their own medical judgement.
4
Indications for Use:
The CADDIE computer-assisted detection device is intended to assist the gastroenterologist in detecting suspected colorectal polyps only. The gastroenterologist is responsible for reviewing CADDIE suspected polyp areas and confirming the presence or absence of a polyp based on their own medical judgment.
CADDIE is not intended to replace a full patient evaluation, nor is it intended to be relied upon to make a primary interpretation of endoscopic procedures, medical diagnosis, or recommendations of treatment/course of action for patients.
The CADDIE computer-assisted detection device is limited for use with standard white-light endoscopy imaging only.
Patient Population:
CADDIE is intended to be used on patients aged 45 and over referred for screening and surveillance endoscopic mucosal evaluations. This does not include pregnant women, for which no clinical evaluation has been carried out.
Environments of use:
Hospitals and clinics or in other secure endoscopy units where colonoscopies are performed.
Summary of Technological Characteristics:
At a high level we present the technological comparison of the subject device and the predicate in the Table below.
5
Summary of Technological Characteristics
| Characteristics | Subject Device: CADDIE | Predicate Device: GI Genius
DEN200055 |
|----------------------------------|-----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|
| Manufacturer | Odin Medical Ltd | Cosmo Artificial Intelligence - AI, Ltd |
| Regulation
Number | 21 CFR 876.1520 | 21 CFR 876.1520 |
| Regulation Title | Gastrointestinal lesion software detection system | Gastrointestinal lesion software detection system |
| Classification | Class II | Class II |
| Classification
Product Code | QNP, SBX | QNP |
| Intended Use -
definition | 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. | 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. |
| Indications for
Use | The CADDIE computer-assisted detection device is
intended to assist the gastroenterologist in detecting
suspected colorectal polyps only. The gastroenterologist is
responsible for reviewing CADDIE suspected polyp areas
and confirming the presence or absence of a polyp based on
their own medical judgment.
CADDIE is not intended to replace a full patient evaluation,
nor is it intended to be relied upon to make a primary
interpretation of endoscopic procedures, medical diagnosis,
or recommendations of treatment/course of action for
patients. | 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. |
| Characteristics | Subject Device: CADDIE | Predicate Device: GI Genius
DEN200055 |
| | The CADDIE computer-assisted detection device is limited
for use with standard white-light endoscopy imaging only. | |
| Patient
Population | CADDIE is intended to be used on patients aged 45 and
over referred for screening and surveillance endoscopic
mucosal evaluations. This does not include pregnant
women, for which no clinical evaluation has been carried
out. | Adult patients undergoing screening and surveillance
endoscopic mucosal evaluations. |
| Technological
Characteristics | CADDIE is standalone software that is deployed on the
cloud and accessed via a web browser. The device is
designed to highlight portions of the colon where the device
detects potential colorectal polyps. CADDIE requires a
network connection. | The GI Genius system is composed of software and hardware
designed to highlight portions of the colon where the device
detects potential colorectal polyps. |
| Convenience
Features | When the clinician confirms the cecum and photo
documents the cecal landmarks in standard clinical
workflow, Cecum AI is triggered, and the image is sent for
analysis. If the cecal landmarks are confirmed in the image,
by the AI, the user will be given a reminder to check the
status of Polyp detection. If polyp detection is turned off,
the cross icon will flash three times along with the cecum
icon. If detection is on, the cecum icon will flash three times
next to the tick icon.
This is a convenience feature that provides a check to the
user that the CADDIE polyp detection function is on and in
use. | Not Available |
| Software
Algorithm | CADDIE utilizes an artificial intelligence-based algorithm
to perform the polyp detection function. | The GI Genius system utilizes an artificial intelligence-based
algorithm to perform the polyp detection function. |
| Device Output | During a colonoscopy, CADDIE generates markers, which
look like green squares and are accompanied by a short,
low-volume sound, and superimposes them on the video
from the endoscope camera when it identifies a potential | During a colonoscopy, the GI Genius system generates
markers, which look like green squares and are accompanied
by a short, low-volume sound, and superimposes them on the
video from the endoscope camera when it identifies a potential |
| Characteristics | Subject Device: CADDIE | Predicate Device: GI Genius
DEN200055 |
| | lesion. | lesion. |
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Discussion of Differences
CADDIE and the predicate device have the same intended use and similar indications for use, patient populations, device outputs and software algorithms.
Both the subject device CADDIE and the predicate GI Genius are intended for use to aid physicians in detecting suspected lesions in the colorectal area. The devices are both intended to be used by trained healthcare providers (endoscopists and gastroenterologists) during standard white-light endoscopy. Neither device is intended to replace clinical decision making. The minor differences in the wording of the indications for use are not critical since these do not impact the purpose of the devices which for both devices is to assist in the detection of colorectal lesions in real time during standard white-light endoscopy examinations of adult patients undergoing screening and surveillance endoscopic mucosal evaluations. The devices have the same intended use.
Both devices take a colonoscopy video as an input from an endoscopy image processor and provide an output of a green bounding box that highlights the detected polyps. Both devices are used in real-time to aid the clinician in detecting abnormal lesions live during a colonoscopy.
The patient population is restricted for the subject device to patients aged 45 and over referred for screening and surveillance endoscopic mucosal evaluations. This aligns with the U.S. Multisociety Task Force (MSTF) on Colorectal Cancer, which represents the American College of Gastroenterology, the American Gastroenterological Association, and the American Society for Gastrointestinal Endoscopy recommendation that colonoscopy for colorectal cancer screening in average-risk patients starts at 45 and continuing through to age 75.
A technological difference between the predicate and the subject device (CADDIE) is that the subject device does not utilize bespoke hardware, as it is accessed using a networked connection on an off-the-shelf computer, operating system and browser. The submission is for network accessed cloud-based software. This difference, however, does not raise any additional questions of safety and effectiveness as demonstrated by the clinical and nonclinical performance testing based on the intended purpose of the device.
There is a minor difference between the two devices. Cecum AI, in the subject device, serves as a feature to remind the clinician to check the on/off status of the CADe polyp detection. It is the responsibility of the clinician to confirm that the cecum has been reached before photo documentation is taken that may trigger the reminder. It is the responsibility of the user to turn the CADe functionality on and off. this is identical to the predicate device. This difference does not raise different questions of safety and effectiveness. This function does not introduce any primary functionality that would be deemed inconsistent with regulatory standards, specifically those outlined in 21 CFR 876.1520.
9
The technological and other minor differences discussed above between the subject device and predicate device do not raise any new questions of safety and effectiveness as demonstrated by the non-clinical and clinical performance evaluation results.
Performance Testing
Special Control Technical Testing
Pixel-level comparison of degradation of image quality due to the device was below the predefined threshold in all test samples. The average per-pixel difference was found to be 0.94 and 0.87 with a CV-1500 and a CV-190 image processor respectively.
Note: The minor variation of the results between the systems is a statistical effect due to the variation of the images acquired in the tests.
Assessment of video delay due to marker annotation - The average video delay due to marker annotation was found to be 32.59ms and 33.61ms with a CV-1500 and a CV-190 image processor respectively.
Assessment of real-time endoscopic video delay due to the device - The average video delay was found to be 32.50ms and 32.50ms with a CV-1500 and a CV-190 image processor respectively.
Human Factors and Usability Testing
The CADDIE device has been found to be safe and effective for the intended users, uses and use environments based on the Human Factors Engineering/Usability Engineering activities performed and in accordance with the Human Factors Engineering and Usability Engineering Plan.
The usability assessment demonstrates that the intended users can safely and correctly use the device, in accordance with 21 CFR 876.1520 and special control 3.
CADDIE Data Description and Non-Clinical testing
Polyp Detection Development Datasets
The development data it is separate from the bench-testing data. It included a total of 1711 polyps, from 906 patients distributed across training, tuning and testing. In total 318,603 frames were used in the development datasets (162,207 polyp frames and 156,396 non-polyp frames). 1296 polyps from 714 patients had histology and additional meta data that may include size, location and morphology. The polyp histology was diverse, containing adenomas, hyperplastic polyps, and SSLs. Polyp sizes ranged from diminutive to large. Morphologically, the dataset included polypoid and non-polypoid
10
polyps. Polyp locations were well-distributed across proximal and distal regions. Polyp morphology and location was not always collected/made available by the hospitals due to limitations in local data acquisition/anonymization/sharing processes. The information was available for the majority of the polyps with histopathology. 415 polyps confirmed through resection or photo-documentation, but not histopathology, were included in the development data set. Furthermore, these polyps were optically confirmed by additional endoscopists in the truthing process.
The dataset included a variety of video processors, including the Evis Exera II (CV-180), Evis Exera III (CV-190), Evis Lucera Elite (CV-290), Evis X1 (CV-1500) and endoscopes including Olympus 180, 190, 260, 290. The dataset included a diverse patient population distribution acquired in five European countries, as shown in the table below. Data collected in those sites represented a balanced distribution of sex, wide age range, and indications for colonoscopy, including surveillance, screening, or diagnostic. Ethnic and racial diversity was well-represented across different sites and reflects the target population.
| Data origin | United Kingdom (%) | 48.33% |
|---|---|---|
| Czech Republic (%) | 44.77% | |
| Norway (%) | 3.74% | |
| Spain (%) | 1.99% | |
| Germany (%) | 1.17% | |
| Sex | Male (%) | 54.96% |
| Female (%) | 45.04% | |
| Age | Mean (years) | 61.5 |
| Standard deviation (years) | 14.1 | |
| Reason for colonoscopy | surveillance | 50.25% |
| diagnostic | 35.82% | |
| therapeutic | 2.71% | |
| symptomatic | 1.17% | |
| screening | 10.06% | |
| Ethnicity | Other / Hispanic / Latino | 8.80% |
| Not Hispanic / Latino | 91.20% | |
| Race | American Indian or Alaska Native | 0.01% |
| Asian | 6.76% | |
| Black or African American | 3.15% | |
| Native Hawaiian or Other Pacific Islander | 1.91% | |
| White | 85.21% | |
| Other | 2.96% |
Demographics of Patients Undergoing Colonoscopies in Development Dataset Hospitals
Data limitations: As described above, the development dataset contains instances where the accompanying lesion information was not available e.g. histology, location, etc. This was due to local hospital constraints in data acquisition/anonymization processes, or limitations in local ethics/data-sharing procedures.
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Polyp Detection Standalone Bench-testing Dataset
Standalone performance testing was performed to assess the ability of CADDIE to discriminate between normal mucosa and polyp tissue on video frames from a standard colonoscopy procedure. A set of recorded colonoscopy videos was analyzed by CADDIE and the results were compared to the historical control (known polyp status per frame).
| Test Set (No. of subjects=389*) | ||
|---|---|---|
| Mean (years) | SD (years) | |
| Age | 58.83 | 8.67 |
| Sex | Count | % |
| Male | 193 | 49.61 |
| Female | 196 | 50.39 |
| Indication for Colonoscopy | ||
| Surveillance | 109 | 28.02 |
| Screening | 280 | 71.98 |
| Race/Ethnicity | ||
| African American | 110 | 28.28 |
| American Indian | 1 | 0.26 |
| Asian | 12 | 3.08 |
| Caucasian | 148 | 38.05 |
| Hispanic | 65 | 16.71 |
| Other | 2 | 0.51 |
| Unknown | 51 | 13.11 |
The following table presents the standalone bench-testing dataset for polyp detection patients' demographic distribution, including the reason for colonoscopy.
12
| Polyp count | |
|---|---|
| Histopathology | |
| Adenoma | 382 |
| Non-Adenoma | 368 |
| Polyp Location | |
| Cecum | 79 |
| Ascending Colon | 101 |
| Hepatic Flexure | 25 |
| Transverse Colon | 132 |
| Splenic Flexure | 5 |
| Descending Colon | 79 |
| Sigmoid Colon | 184 |
| Rectum | 145 |
| Polyp Morphology | |
| Polypoid | 503 |
| Non-Polypoid | 237 |
| Unknown | 10 |
| Polyp Size | |
| Diminutive | 567 |
| Small | 136 |
| Large | 47 |
| Video Processor | |
| CV-180 | 48 |
| CV-190 | 437 |
| CV-1500 | 265 |
| Endoscopic family | |
| CF-HQ190 | 184 |
| CF-H190 | 52 |
| CF-HQ1100 | 27 |
| CF-HQ190 or PCF-H190 | 485 |
Annotation was performed on a per-frame basis, where a team of trained clinical annotators labelled polyp structures with a bounding box, followed by an additional layer of review by a separate team of experts (with over 2000 endoscopic procedures experience). Each polyp was histologically confirmed. These polyp annotations were used as ground truth reference standards. The table above shows the distribution of polyp characteristics.
| Name | Description | Success criteria (if applicable) | Results [95% CI] |
|---|---|---|---|
| Object-level | |||
| true positive | |||
| rate (TPR)* | The proportion of polyps (%) detected | ||
| by the device (> 0.5 seconds & IoU > | |||
| 20%) and confirmed to be polyps using | |||
| pathology findings. | Object Level TPR > 80% (to | ||
| show a lower miss rate than in | |||
| clinical practice of 25%[1]) | 98.27% | ||
| [97.33, 99.20] | |||
| Frame-Level | |||
| FPR* | The proportion of frames (%) in which | ||
| CADDIE detects a box (> 0.5 seconds) | |||
| that is not a histopathologically | |||
| confirmed polyp. | Frame Level FPR 0.5 seconds) that are not | ||
| histopathology-confirmed polyps. | Not applicable | 0.89 | |
| [0.79, 1.00] | |||
| ------------------------------------------------ | ---------------------------------------------------------------------------------------------------------------------------------------------------- | ---------------- | ------------------------- |
| Frame-Level | |||
| TPR** | The proportion of frames (%) with | ||
| confirmed polyps in which CADDIE | |||
| detects the polyp (> 0.5 seconds & IoU |
20%) | Not applicable | 54.92
[53.02, 56.81] |
*primary endpoints; ** secondary endpoints.
Polyp Detection Non-Clinical Performance Testing Results
Non-clinical performance testing was performed on the standalone bench-testing dataset, which is separate to the development datasets.
Persistence is defined as the detection's duration on the same target. The following table shows the variation of the endpoint metrics when only considering predictions with IoU overlap criteria applied with respect to certain persistence. The metrics in the following table correspond to the polyp detection endpoints and are summarized as follows:
- . Frame-level TPR: proportion of frames with polyps detected by the device
- Object-level TPR: proportion of polyps detected by the device ●
- Frame-level FPR: proportion of non-polyp frames where the device detects a polyp ●
- Object-level FPR: number of false alarms per minute per patient. .
| Persistence | Frame-Level
TPR | Object-Level
TPR | Frame-Level
FPR | Object-Level
FPR |
|-------------|--------------------|---------------------|--------------------|---------------------|
| > 0 | 59.69% | 99.60% | 2.88% | 4.21 |
| > 100 ms | 59.54% | 99.33% | 2.88% | 4.20 |
| > 200 ms | 58.70% | 98.93% | 2.53% | 2.94 |
| > 300 ms | 57.37% | 98.53% | 2.09% | 1.77 |
| > 400 ms | 56.13% | 98.53% | 1.78% | 1.22 |
| > 500 ms | 54.92% | 98.27% | 1.54% | 0.89 |
| > 1000 ms | 49.09% | 94.13% | 0.84% | 0.29 |
| > 1500 ms | 43.98% | 88.80% | 0.53% | 0.14 |
| > 2000 ms | 39.77% | 85.05% | 0.35% | 0.07 |
The table below shows a subgroup analysis. These results are computed at a persistence > 500 ms and IoU > 20% (when applicable). The metrics in the following table correspond to the polyp detection endpoints and are summarized as follows:
- Frame-level TPR: proportion of frames with polyps detected by the device ●
- Object-level TPR: proportion of polyps detected by the device ●
- Frame-level FPR: proportion of non-polyp frames where the device detects a polyp ●
| | Frame-Level TPR
[95% CI] | Object-level TPR
[95% CI] | Detected polyps | Procedures |
|-------------|-----------------------------|------------------------------|-----------------|------------|
| Overall | 54.92% [53.02, 56.81] | 98.27% [97.33, 99.20] | 737/750 | 311 |
| Histology | | | | |
| Adenoma | 59.96% [57.43, 62.49] | 98.95% [97.93, 99.97] | 378/382 | 199 |
| Non-Adenoma | 49.68% [46.94, 52.42] | 97.55% [95.98, 99.13] | 359/368 | 201 |
Page 11 of 18
14
| Lesion Size | ||||
|---|---|---|---|---|
| Diminutive (0- | ||||
| 5mm) | 52.77% [50.58, 54.97] | 98.24% [97.15, 99.32] | 557/567 | 275 |
| Small (6-10mm) | 60.85% [56.55, 65.15] | 97.79% [95.33,100.0 ] | 133/136 | 95 |
| Large (> 10mm) | 63.58% [56.80, 70.36] | 100.0% [100.0, 100.0] | 47/47 | 38 |
| Video Processors | ||||
| CV-190 | 55.13% [52.62, 57.64] | 99.08% [98.19, 99.98] | 433/437 | 167 |
| CV-1500 | 52.76% [49.60, 55.92] | 96.98% [94.92, 99.04] | 257/265 | 117 |
| Endoscope Families | ||||
| CF-HQ190 | 52.85% [49.14, 56.56] | 96.20% [93.43, 98.96] | 177/184 | 85 |
| CF-H190 | 51.88% [44.73, 59.03] | 98.08% [94.34, 100.0] | 51/52 | 21 |
| CF-HQ1100D | 54.09% [43.17, 65.00] | 100.00% [100.0, 100.0] | 27/27 | 9 |
| CF-HQ-190 or | ||||
| PCF-H190 | 56.09% [53.73, 58.46] | 98.97% [98.07, 99.87] | 480/485 | 194 |
| Frame-Level FPR | ||||
| [95% CI] | Procedures | |||
| Overall | 1.54% [1.31, 1.76] | 85 |
The algorithm Receiver Operating Characteristic (ROC) curve and area Under the Curve (AUC) are shown in the figure below. The ROC curves illustrate the False Positive Rate versus the True Positive Rate at the frame level. The left subplot displays the nonparametric frame-level ROC curve and the corresponding estimated area under the ROC curve (AUC), derived from model performance. The right subplot presents the nonparametric frame-level ROC curve, including the estimated AUC and 95% confidence intervals, derived from patient-level bootstrapping.
Image /page/14/Figure/4 description: The image contains two ROC curves for polyp detection, one labeled "Model" and the other "Bootstrap." Both curves plot the true positive rate against the false positive rate. The "Model" curve has an AUC of 0.79601, while the "Bootstrap" curve has an AUC of 0.79609 with a 95% confidence interval of [0.79600, 0.79619].
Cecum AI Development Datasets
The dataset used to develop the Cecum AI algorithm consisted of a diverse sample of patients and images. It included data from 1467 patients and 17,844 images, spanning training, tuning, and testing phases. It was composed of informative static photo-
15
documentation images, as well as images extracted from videos of cecal landmarks including appendiceal orifice (AO), ileocecal valve (ICV).
Data limitations: demographic information was not available. The dataset contains instances where the video processor is unknown. Missing information is due to limitations in data acquisition/anonymization or local hospital collection/sharing protocols.
| Name | Description | Success criteria
(if applicable) | Results
[95% CI] |
|-------------------------------------------|-----------------------------------------------------------------------------------------------------------------------------------|-------------------------------------|--------------------------|
| Frame-Level true
positive rate (TPR) | The proportion (%) of all the frames
annotated with cecum which the Cecum AI
identifies correctly. | Frame-level TPR
80% | 94.05%
[91.58, 96.52] |
| Frame-level false
positive rate (FPR)* | The proportion (%) of all the frames
annotated without cecal landmarks which the
Cecum AI incorrectly identifies the cecum. | Frame-level FPR
25%.
A total of 841 patients were included in the modified Intention to Treat (mITT) population for primary analysis, including 417 who received a colonoscopy with the CADDIE and 424 who received a standard colonoscopy. The evaluation of our primary and secondary endpoints for the mITT population is summarized below.
| Patient Demographics | ||||||||
|---|---|---|---|---|---|---|---|---|
| Variable | Category | All Patients [N=841] | SoC [N=424] | CADe [N=417] | P | |||
| N | Summary | N | Summary | N | Summary | |||
| Colonoscopy | ||||||||
| Indication | Screening | |||||||
| Surveillance | 841 | 653 (77.7%) | ||||||
| 188 (22.3%) | 424 | 329 (77.6%) | ||||||
| 95 (22.4%) | 417 | 324 (77.7%) | ||||||
| 93 (22.3%) | 0.97 | |||||||
| Age | - | 841 | $58.5 \pm 9.3$ | 424 | $58.2 \pm 9.5$ | 417 | $58.7 \pm 9.0$ | 0.43 |
| Gender | Female | |||||||
| Male | ||||||||
| Other | 841 | 412 (49.0%) | ||||||
| 428 (50.9%) | ||||||||
| 1 (0.1%) | 424 | 218 (51.4%) | ||||||
| 205 (48.4%) | ||||||||
| 1 (0.2%) | 417 | 194 (46.5%) | ||||||
| 223 (53.5%) | ||||||||
| 0 (0.0%) | 0.21 | |||||||
| Ethnicity | Not Latino/Hispanic | |||||||
| Latino/Hispanic | 827 | 686 (82.9%) | ||||||
| 141 (17.1%) | 415 | 349 (84.1%) | ||||||
| 66 (15.9%) | 412 | 337 (81.8%) | ||||||
| 75 (18.2%) | 0.38 | |||||||
| Race | American Indian | |||||||
| African American | ||||||||
| White | ||||||||
| Other | 840 | 0 (0.0%) | ||||||
| 1 (0.1%) | ||||||||
| 837 (99.6%) | ||||||||
| 2 (0.2%) | 424 | 0 (0.0%) | ||||||
| 1 (0.2%) | ||||||||
| 423 (99.8%) | ||||||||
| 0 (0.0%) | 416 | 0 (0.0%) | ||||||
| 0 (0.0%) | ||||||||
| 414 (99.5%) | ||||||||
| 2 (0.5%) | 0.25 |
Summary statistics are mean ± standard deviation, or number (percentage)
Continuous variables were compared between groups using the unpaired t-test, if normally distributed, or the Man-Whitney test otherwise. Ordinal outcomes were analysed using the Mann-Whitney test, and categorical variables using the Chi-square test.
Primary Endpoints:
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The co-primary endpoints of the study included a performance endpoint (adenomas per colonoscopy, APC) and a safety endpoint (positive percent agreement, PPA).
- APC: The total number of histologically confirmed adenomas and adenocarcinomas divided by the total number of colonoscopies.
- PPA: Percentage of histologically confirmed adenomas, sessile serrated lesions, ● and large (> 10 mm) hyperplastic polyps of the proximal colon (caecum, ascending colon, hepatic flexure, and transverse colon) out of the total number of resections.
| Primary Endpoints | |||||
|---|---|---|---|---|---|
| Endpoint | Study Arm | N patients | Mean ± SD | Ratio (95% CI) | P-value |
| APC | SoC | 424 | 0.62 ± 1.19 | 1 | |
| CADe | 417 | 0.82 ± 1.40 | 1.33 (1.06, 1.67) | 0.01 | |
| Endpoint | Study Arm | N polyps | n positive (%) | % difference | |
| (1 sided 97.5% CI) | P-value | ||||
| PPA | SoC | 517 | 276 (53.4%) | 0 | - |
| CADe | 700 | 377 (53.9%) | 0.5% (-5.0%, ∞) |
| APC Stratified by Lesion Size - Exploratory Endpoints | ||||
|---|---|---|---|---|
| Lesion size | Study Arm | Mean ± SD | Difference (95% CI) | P-value |
| ≤ 5 mm | SoC | 0.44 ± 0.83 | 1 | 0.04 |
| CADe | 0.57 ± 1.07 | 1.29 (1.01, 1.64) | ||
| 6 – 9 mm | SoC | 0.13 ± 0.49 | 1 | 0.25 |
| CADe | 0.17 ± 0.48 | 1.28 (0.84, 1.94) | ||
| ≥ 10 mm | SoC | 0.04 ± 0.23 | 1 | 0.04 |
| CADe | 0.09 ± 0.36 | 1.93 (1.03, 3.62) |
Secondary Endpoints:
The secondary endpoints included the following:
- Adenoma Detection Rate (ADR). Proportion of examinations with at least one ● histologically confirmed adenoma detected.
- Sessile serrated adenomas per colonoscopy (SSAPC). Total number of histologically confirmed sessile serrated adenomas, traditional serrated adenomas, or serrated lesions with cytological dysplasia, excluding hyperplastic polyps.
- Sessile serrated adenomas per colonoscopy (SSAPC*). Total number of ● histologically confirmed sessile serrated adenomas or serrated lesions with cytological dysplasia.
- Neoplastic Polyps Per Colonoscopy (NPPC). Total number of histologically confirmed adenomas, adenocarcinomas, sessile serrated adenomas, and traditional serrated adenomas divided by the total number of colonoscopies.
- Polyps Per Colonoscopy (PPC). The total number of histologically confirmed polyps detected (adenomas, adenocarcinomas, sessile serrated, traditional serrated adenomas, and hyperplastic), divided by the total number of colonoscopies.
- Colonoscope withdrawal time of negative procedures only. The time it takes to withdraw the colonoscope from the furthest point (TI or caecum) for colonoscopies that did not require any biopsies.
19
- Colonoscope withdrawal time. The time it takes to withdraw the colonoscope ● from the furthest point (TI or caecum) including time for polypectomy or other interventions.
- Total procedure time. Entire duration of the procedure. ●
| Secondary Endpoints | |||||
|---|---|---|---|---|---|
| Outcome | Study | ||||
| Arm | N | ||||
| patients | Summary (*) | Difference (#) | |||
| (95% CI) | P- | ||||
| value | |||||
| ADR | SoC | ||||
| CADe | 424 | ||||
| 417 | 152 (35.9%) | ||||
| 179 (42.9%) | 0 | ||||
| 7.1% (0.5%, 13.7%) | 0.04 | ||||
| SSAPC | SoC | ||||
| CADe | 424 | ||||
| 417 | 0.04 ± 0.21 | ||||
| 0.09 ± 0.46 | 1 | ||||
| 2.15 (1.07, 4.32) | 0.03 | ||||
| SSAPC* | SoC | ||||
| CADe | 424 | ||||
| 417 | 0.03 ±0.16 | ||||
| 0.08 ± 0.45 | 1 | ||||
| 3.30 (1.41, 7.571 | 0.006 | ||||
| NPPC | SoC | ||||
| CADe | 424 | ||||
| 417 | 0.66 ± 1.20 | ||||
| 0.91 ± 1.48 | 1 | ||||
| 1.39 (1.12, 1.73) | 0.002 | ||||
| PPC | SoC | ||||
| CADe | 424 | ||||
| 417 | 1.06 ± 1.53 | ||||
| 1.41 ± 1.85 | 1 | ||||
| 1.35 (1.13, 1.61) | 0.001 | ||||
| Withdrawal time | |||||
| (Negative Procedures) | SoC | ||||
| CADe | 178 | ||||
| 138 | 8 [7, 10] | ||||
| 8 [7, 11] | 1 | ||||
| 1.02 (0.96, 1.09) | 0.44 | ||||
| Withdrawal time | |||||
| (All procedures) | SoC | ||||
| CADe | 424 | ||||
| 417 | 10 [8, 13] | ||||
| 11 [8, 15] | 1 | ||||
| 1.10 (1.05, 1.15) | 0.003 | ||||
| 6 - 9 mm | SoC | ||||
| CADe | $0.25 \pm 0.62$ | ||||
| $0.28 \pm 0.71$ | 1 | ||||
| 1.16 (0.85, 1.60) | 0.35 | ||||
| ≥ 10 mm | SoC | ||||
| CADe | $0.05 \pm 0.24$ | ||||
| $0.12 +0.44$ | 1 | ||||
| 2.36 (1.33, 4.17) | 0.003 | ||||
| Histology (*) | Adenocarcinoma | SoC | |||
| CADe | $0.00 \pm 0.00$ | ||||
| $0.007 \pm 0.08$ | - | - | |||
| Adenoma | SoC | ||||
| CADe | $0.62 \pm 1.19$ | ||||
| $0.82 \pm 1.40$ | 1 | ||||
| 1.32 (1.05, 1.66) | 0.02 | ||||
| SSA | SoC | ||||
| CADe | $0.03 \pm 0.16$ | ||||
| $0.08 \pm 0.45$ | 1 | ||||
| 3.30 (1.41, 7.57) | 0.006 | ||||
| TSA | SoC | ||||
| CADe | $0.02 \pm 0.14$ | ||||
| $0.01 \pm 0.12$ | 1 | ||||
| 0.53 (0.11, 2.44) | 0.41 |
20
| | Hyperplastic | SoC
CADe | $0.40 \pm 0.80$
$0.50 \pm 1.01$ | 1
$1.24 (0.95, 1.63)$ | 0.12 |
|--|----------------|-------------|--------------------------------------|--------------------------|------|
| | Inflammatory | SoC
CADe | $0.005 \pm 0.07$
$0.010 \pm 0.10$ | - | - |
| | Colonic mucosa | SoC
CADe | $0.13 \pm 0.45$
$0.19 \pm 0.50$ | 1
$1.43 (0.96, 2.13)$ | 0.08 |
(*) Histopathology subgroups do not include polyps which were lost/unrecovered, non-diagnostic material, or samples described as 'other'. There were too few adenocarcinomas and inflammatory polyps to perform a statistical analysis. (#) Lesion size endpoints include all polyps as per the PPC definition, whereas histology endpoints also include inflammatory polyps and colonic mucosa.
Group differences are ratio of values in CADe relative to SoC. For all comparisons, there were 424 patients in the SoC arm and 417 in the CADe arm.
Efficacy
CADDIE had significant superiority over standard colonoscopy in the detection of adenomas, with a significant increase in APC of 33%. Moreover, the CADDIE device significantly increased the detection of both diminutive (≤5 mm) and large (≥10 mm) sized adenomas/adenocarcinomas, 29% and 93% more adenomas detected respectively.
Safetv
The PPA for CADDIE was non-inferior to SoC, as the lower bound of the confidence interval for the difference between groups was above the prespecified -15% margin. This suggests that despite an increase in the number of polyps resected, there was no increase in unnecessary biopsies/extractions using CADDIE. In support of this, while CADDIE resulted in resection of more adenomas and sessile serrated adenomas (clinically relevant biopsies), there was no significant increase in resections of non-adenomatous polyps or colonic mucosa (unnecessary biopsies). There were also no adverse events during the study that related to the use of CADDIE.
Overall, the clinical study demonstrated that the performance of CADDIE achieved benchmark expectations and a safety and effectiveness profile comparable to the predicate device. The results of each trial demonstrated the superiority of the CADe systems in the detection of adenomas/adenocarcinomas and non-inferiority in the PPA.
Substantial Equivalence Conclusion
CADDIE has similar indications for use, technological characteristics, and principles of operation as the predicate GI Genius. There are minor technological differences that are addressed using performance testing, showing that there are no new issues with safety or effectiveness when used as labelled. Minor technological differences between the CADDIE system and the predicate device do not raise different issues of safety or effectiveness. The performance data demonstrates that CADDIE is as safe and effective as the predicate device. As a result, the CADDIE device can be considered substantially equivalent.