(242 days)
Yes
The intended use, device description, and performance studies explicitly state that the device uses artificial intelligence (AI) for image analysis and recognition.
No
This device is an AI-assisted reading tool designed to aid reviewers in decreasing the time to review capsule endoscopy images and identify the digestive tract location of images. It does not provide therapy or treatment to patients.
Yes
The device aids in decreasing review time for capsule endoscopy images and identifying the digestive tract location, and marks images containing suspected abnormal lesions, all of which contribute to the diagnostic process by highlighting areas that may require further clinical assessment.
Yes
The device is explicitly described as "artificial intelligence software" and its function is to process images and provide analysis, without including any hardware components. It is intended to be used as an adjunct to an existing hardware system (NaviCam Small Bowel Capsule Endoscopy System) but is itself software only.
Based on the provided information, the NaviCam ProScan device is not an In Vitro Diagnostic (IVD).
Here's why:
- IVDs are used to examine specimens derived from the human body. The NaviCam ProScan processes images obtained from a capsule endoscopy system, which are visual representations of the inside of the digestive tract. While these images are of the human body, they are not biological specimens (like blood, urine, tissue, etc.) that are analyzed in vitro (outside the body).
- The device's intended use is to aid in the reading and review of images. It assists clinicians in identifying potential lesions and determining the location within the digestive tract. It does not perform any analysis of biological samples to diagnose a condition.
- The device is described as "artificial intelligence software" that processes images. This aligns with the definition of a medical image analysis software, not an IVD.
- The performance studies focus on metrics related to image analysis and reading time reduction, such as AUC for lesion detection and sensitivity/specificity for tract site recognition, and the impact on physician reading time and diagnostic yield when used as an adjunct. These are typical performance metrics for image analysis software, not IVDs which would focus on analytical and clinical performance related to the analysis of biological specimens.
In summary, the NaviCam ProScan is a software tool designed to assist clinicians in interpreting medical images obtained from a capsule endoscopy procedure. It does not analyze biological specimens and therefore does not fit the definition of an In Vitro Diagnostic device.
No.
The letter mentions "Control Plan Authorized" and describes "Special Controls" and "Postmarket Surveillance," but it does not state that the FDA has explicitly reviewed and approved or cleared a Predetermined Change Control Plan (PCCP) for this specific device. The provided text refers to general regulatory requirements and post-market data collection rather than specific PCCP approval.
Intended Use / Indications for Use
NaviCam ProScan is an artificial intelligence (AI) assisted reading tool designed to aid small bowel capsule endoscopy reviewers in decreasing the time to review capsule endoscopy images for adult patients in whom the capsule endoscopy images were obtained for suspected small bowel bleeding. The clinician is responsible for conducting their own assessment of the findings of the AI-assisted reading through review of the entire video, as clinically appropriate. ProScan also assists small bowel capsule endoscopy reviewers in identifying the digestive tract location (oral cavity and beyond, esophagus, stomach, small bowel) of the image in adults. This tool is not intended to replace clinical decision making.
Product codes
QZF
Device Description
The NaviCam ProScan is artificial intelligence software that has been trained to process capsule endoscopy images of the small bowel acquired by the NaviCam Small Bowel Capsule Endoscopy System to recognize the various sections of the digestive tract and to recognize and mark images containing suspected abnormal lesions.
NaviCam ProScan is intended to be used as an adjunct to the ESView software of the NaviCam Small Bowel Capsule Endoscopy System (both cleared in K221590) and is not intended to replace gastroenterologist assessment or histopathological sampling.
NaviCam ProScan does not make any modification or alteration to the original capsule endoscopy video. It only overlays graphical markers and includes an option to only display these identified images. The whole small bowel capsule endoscopy video and highlighted regions still must be independently assessed by the clinician and appropriate actions taken according to standard clinical practice.
The NaviCam ProScan software includes two main algorithms, as illustrated in Figure 1 below:
- Digestive tract site recognition, which includes an image analysis algorithm and site ● segmentation algorithm to determine: oral and beyond, esophagus, stomach, and small bowel. Tract site is displayed as a color code on the video timeline with descriptions on the indicators at the bottom of the software user interface.
- Small bowel lesion recognition, which includes the small bowel lesion image analysis ● algorithm with lesion region localization. Potential lesions are marked with a bounding box as illustrated in Figure 2 below, with the active video played at the top section of the figure, and ProScan-identified images in the lower section, which includes images with suspected lesions and individual images marking the transition in the digestive tract. The algorithm is functional only on those sections of the GI tract that were identified as "small bowel" by the digestive tract site recognition software function.
Mentions image processing
Yes
Mentions AI, DNN, or ML
Yes
Input Imaging Modality
capsule endoscopy images
Anatomical Site
small bowel, digestive tract (oral cavity and beyond, esophagus, stomach)
Indicated Patient Age Range
Adult patients
Intended User / Care Setting
small bowel capsule endoscopy reviewers
Description of the training set, sample size, data source, and annotation protocol
In the training phase, the NaviCam ProScan was trained to recognize abnormal small bowel images and to recognize the digestive tract site. A dataset of 2,642 patients that underwent small bowel capsule endoscopy with NaviCam Small Bowel Capsule Endoscopy System, obtained from 8 clinical institutions in China, was collected.
Of those 2,642 patients, the dataset was split so that 1,476 patients were used for training the lesion detection function. For tract site recognition, the 2,642 patient dataset was split so that 1,386 patients were used for training the tract site recognition function. Please note that patients used for training do not overlap with patients used for corresponding testing of that software function. However there may be overlap in selection of patients for training for lesion detection function and training/testing of the tract site recognition function, and overlap in selection of patients for training of the tract site recognition function and training/testing of the lesion detection function.
Lesion Detection Function
Pre-Annotation (Initial Labeling) Process:
Full videos were randomly assigned to three gastroenterologists for the annotation of small bowel images. The doctors annotate one or multiple positive lesion image segments and one or multiple negative image segments for each video. These positive lesion image segments and negative image segments constitute the full video of the small bowel for objects. All positive lesion image segments are independently numbered and randomly sampled. Similarly, all negative image segments are independently numbered and randomly sampled. These two parts, obtained through sampling, form the annotated image dataset of the small bowel.
Annotation (Truthing) Process:
The image dataset obtained from the pre-annotation process is annotated by the three gastroenterologists using annotation software in a back-to-back manner. The computer automatically determines consistency and merges the classification results while preserving differing opinions. When the cutoff value for consistency is less than 3, two arbitration experts independently review and modify the classification results, correcting any missed diagnoses, misdiagnoses, or misjudgments. If difficult questions arise, the arbitration experts engage in collective discussion and confirmation.
Tract Site Recognition Function
Pre-Annotation (Initial Labeling) Process:
Full video data is randomly assigned to three experts. The experts mark the boundary positions of each site (including Oral Cavity and beyond, Esophagus, Stomach, and Small Bowel) within each video. For example, the boundary position where the first image enters the esophagus, the first image enters the stomach, and the first image enters the small bowel. These four sets of images form a complete video. Each set of images is individually labeled and randomly sampled. The site annotation data is generated by randomly sampling all site data.
Annotation (Truthing) Process:
The image dataset obtained from the pre-annotation process is annotated by three gastroenterologists using annotation software in a blinded manner. They annotate the classification labels for the four sites (including Oral Cavity and beyond, Esophagus, Stomach, and Small Bowel). The computer automatically determines the consistency and merges the classification results while preserving differing opinions. When the consistency cutoff value is less than 3, two adjudication experts independently review and modify the classification results, correcting any missed diagnoses, misdiagnoses, or misjudgments. In case of difficult cases, collective discussions and confirmations are conducted by the adjudication experts.
Description of the test set, sample size, data source, and annotation protocol
Standalone Algorithm Testing
1. Lesion detection function
For testing, 218 patients from the dataset described above were selected. From that same dataset, normal and abnormal small bowel capsule endoscopy images were used to test the algorithm.
2. Tract Site Testing
For testing, 424 patients from the dataset described above were selected. The performance of the site recognition model at the image level was tested using four categories of digestive tract sites: Oral Cavity and beyond, Esophagus, Stomach, and Small Bowel.
Summary of Performance Studies
SUMMARY OF NONCLINICAL/BENCH STUDIES
PERFORMANCE TESTING - BENCH - STANDALONE PERFORMANCE
Study Type: Standalone algorithm testing (Lesion detection and Tract Site Recognition)
Sample Size:
- Lesion detection: 218 patients
- Tract Site Recognition: 424 patients
Key Results:
Lesion detection function
- Patient-level analysis:
- Sensitivity: 98% (95% CI: 93.95%-99.71%)
- Specificity: 37% (95%CI: 27.27%-48.02%)
- AUC: 0.911 (95% CI: 0.872 to 0.945)
- Image-level analysis:
- Sensitivity: 95.05% (95% CI: 94.28%-95.72%)
- Specificity: 97.54% (95% CI: 97.28%-97.78%)
- AUC: 0.993 (95% CI: 0.981 to 1.000)
Tract Site Testing
- Image-level analysis:
- Oral cavity and beyond: Sensitivity 99.47% (99.14%-99.68%), Specificity 99.50% (99.39%-99.58%)
- Esophagus: Sensitivity 98.92% (97.79%-99.50%), Specificity 99.10% (98.98%-99.22%)
- Stomach: Sensitivity 99.60% (99.49%-98.69%), Specificity 99.06% (98.80%-99.26%)
- Small Bowel: Sensitivity 99.26% (98.89%-99.51%), Specificity 98.36% (98.18%-98.52%)
SUMMARY OF CLINICAL INFORMATION
Retrospective Evaluation
Study Type: Retrospective evaluation of reading time
Sample Size: 87 patients (images from)
Key Results: The NaviCam SB System with ProScan feature on significantly reduced the physician's reading time compared to the reading time of the NaviCam SB System without the ProScan on (21.47+8.05 vs. 58.10±45.28, p
N/A
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DE NOVO CLASSIFICATION REQUEST FOR NAVICAM PROSCAN
REGULATORY INFORMATION
FDA identifies this generic type of device as:
Gastrointestinal capsule endoscopy analysis software device. A gastrointestinal capsule endoscopy analysis software device is used to analyze pre-recorded capsule endoscopy videos of the gastrointestinal tract that are suspected of containing lesions. This device uses software algorithms to identify images and areas of interest as outputs to aid the clinician in analyzing suspected lesions, for clinician review of device outputs. The device may include hardware to support interfacing with a capsule imaging system.
NEW REGULATION NUMBER: 21 CFR 876.1540
CLASSIFICATION: Class II
PRODUCT CODE: QZF
BACKGROUND
DEVICE NAME: NaviCam ProScan
SUBMISSION NUMBER: DEN230027
DATE DE NOVO RECEIVED: April 14, 2023
SPONSOR INFORMATION:
Ankon Technologies Co., Ltd B3-2, B3-3, D3-4 Biolake, No.666, Hi-Tech Road East Lake New Technology Development Zone Wuhan, 430075 Hubei, China
INDICATIONS FOR USE
The NaviCam ProScan is indicated as follows:
NaviCam ProScan is an artificial intelligence (AI) assisted reading tool designed to aid small bowel capsule endoscopy reviewers in decreasing the time to review capsule endoscopy images for adult patients in whom the capsule endoscopy images were obtained for suspected small bowel bleeding. The clinician is responsible for conducting their own assessment of the findings of the AI-assisted reading through review of the entire video, as clinically appropriate. ProScan also assists small bowel capsule endoscopy reviewers in identifying the digestive tract location (oral cavity and beyond,
1
esophagus, stomach, small bowel) of the image in adults. This tool is not intended to replace clinical decision making.
LIMITATIONS
The sale, distribution, and use of NaviCam ProScan are restricted to prescription use in accordance with 21 CFR 801.109.
The device is not intended to be used as a stand-alone diagnostic device or replace clinical decision making.
ProScan should only be used with NaviCam Small Bowel Capsule Endoscopy System.
In the clinical study of the device, performance (sensitivity and specificity) of the device in the absence of clinician input was not evaluated. Therefore, the AI standalone performance in the clinical study of NaviCam ProScan has not been established. The clinician is responsible for making the final clinical diagnosis.
Negative or normal result, as determined by ProScan alone, does not exclude the presence of small bowel disease (false negative). Similarly, positive or abnormal result, as determined by ProScan alone, does not automatically confirm the presence of small bowel disease (false positive). The clinician should always carefully review the entire video. If symptoms persist, further evaluation should be performed.
ProScan is not intended to characterize lesions in a manner that would potentially replace biopsy sampling or other characterization tools.
PLEASE REFER TO THE LABELING FOR A COMPLETE LIST OF WARNINGS, PRECAUTIONS AND CONTRAINDICATIONS.
DEVICE DESCRIPTION
The NaviCam ProScan is artificial intelligence software that has been trained to process capsule endoscopy images of the small bowel acquired by the NaviCam Small Bowel Capsule Endoscopy System to recognize the various sections of the digestive tract and to recognize and mark images containing suspected abnormal lesions.
NaviCam ProScan is intended to be used as an adjunct to the ESView software of the NaviCam Small Bowel Capsule Endoscopy System (both cleared in K221590) and is not intended to replace gastroenterologist assessment or histopathological sampling.
NaviCam ProScan does not make any modification or alteration to the original capsule endoscopy video. It only overlays graphical markers and includes an option to only display these identified images. The whole small bowel capsule endoscopy video and highlighted regions still must be independently assessed by the clinician and appropriate actions taken according to standard clinical practice.
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The NaviCam ProScan software includes two main algorithms, as illustrated in Figure 1 below:
- Digestive tract site recognition, which includes an image analysis algorithm and site ● segmentation algorithm to determine: oral and beyond, esophagus, stomach, and small bowel. Tract site is displayed as a color code on the video timeline with descriptions on the indicators at the bottom of the software user interface.
- Small bowel lesion recognition, which includes the small bowel lesion image analysis ● algorithm with lesion region localization. Potential lesions are marked with a bounding box as illustrated in Figure 2 below, with the active video played at the top section of the figure, and ProScan-identified images in the lower section, which includes images with suspected lesions and individual images marking the transition in the digestive tract. The algorithm is functional only on those sections of the GI tract that were identified as "small bowel" by the digestive tract site recognition software function.
Image /page/2/Figure/3 description: The image shows a flowchart of an image analysis system for digestive tract and small bowel lesions. The process starts with image data, which is then preprocessed. The preprocessed data is then fed into two parallel branches: one for digestive tract site analysis and another for small bowel lesion analysis. The digestive tract site analysis branch includes an image analysis algorithm followed by a digestive tract segmentation algorithm, while the small bowel lesion analysis branch includes an image analysis algorithm followed by region of lesion location. Finally, the results from both branches are combined to produce output results.
Figure 1: Working Principle of NaviCam ProScan
Image /page/2/Picture/5 description: The image shows a screenshot of a video player interface, likely displaying a medical video. The interface includes standard video controls such as play, pause, rewind, and fast forward, along with a timeline indicating the current playback position. The video content appears to be endoscopic imagery, possibly showing the inside of a patient's digestive tract, with some areas highlighted by blue rectangles. The interface also displays timecodes, such as "00:24:40" and "00:21:29", indicating the current and total duration of the video.
Figure 2: Lesion Recognition
Both software algorithms are based on convolutional networks using different deep learning models.
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SUMMARY OF NONCLINICAL/BENCH STUDIES
SOFTWARE/CYBERSECURITY
NaviCam ProScan was identified as having a moderate level of concern as defined in the FDA guidance document "Guidance for the Content of Premarket Submissions for Software Contained in Medical Devices." The software documentation included:
-
- Software/Firmware Description
-
- Device Hazard Analysis
-
- Software Requirement Specifications
-
- Architecture Design Chart
-
- Software Design Specifications
-
- Traceability
-
- Software Development Environment Description
-
- Verification and Validation Documentation
-
- Revision Level History
-
- Unresolved Anomalies
Risk analysis was provided for the software with a description of the hazards, their causes and severity as well as acceptable methods for control of the identified risks. NaviCam ProScan provided a description, with test protocols including pass/fail criteria and report of results, of acceptable verification and validation activities at the unit, integration and system level. These evaluations include performance, functional, UI, security, installation, compatibility, and regression testing associated with the software implementation into the primary ESView application. All testing met design specifications and passed successfully. This testing is not part of the AI performance evaluation.
Regarding the cybersecurity, documentation included all the recommended information from the FDA guidance document "Content of Premarket Submissions for Management of Cybersecurity in Medical Devices." This includes a threat model, cybersecurity mitigation information, a malware-free shipping plan, an upgrade plan, and other information for safeguarding the algorithms.
PERFORMANCE TESTING - BENCH - STANDALONE PERFORMANCE
The optimization, training, and validation of the NaviCam ProScan was performed in several phases, as summarized below.
Algorithm Training
In the training phase, the NaviCam ProScan was trained to recognize abnormal small bowel images and to recognize the digestive tract site. A dataset of 2,642 patients that underwent small bowel capsule endoscopy with NaviCam Small Bowel Capsule Endoscopy System, obtained from 8 clinical institutions in China, was collected.
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Of those 2,642 patients, the dataset was split so that 1,476 patients were used for training the lesion detection function, and 218 patients were used in internal testing for the lesion detection function (see Standalone Algorithm Testing section, below), with full demographics information shown in Table 1 below. For tract site recognition, the 2,642 patient dataset was split so that 1,386 patients were used for training the tract site recognition function, and 424 patients were used in internal testing of the tract site recognition function (see Standalone Algorithm Testing section, below), with full demographics information shown in Table 2 below. Please note that patients used for training do not overlap with patients used for corresponding testing of that software function. However there may be overlap in selection of patients for training for lesion detection function and training/testing of the tract site recognition function, and overlap in selection of patients for training of the tract site recognition function and training/testing of the lesion detection function.
| | Training
(No. of subjects = 1,476) | Testing
(No. of subjects = 218) |
|----------------------------------------------|---------------------------------------|------------------------------------|
| Age (years): | | |
| Mean Age (SD) | 47.7 (9.8) | 48.4 (9.8) |
| Age range | 1291 | 1591 |
| Sex: | | |
| Male | 977 | 151 |
| Female | 478 | 62 |
| Unknown | 21 | 5 |
| Race/Ethnicity: | | |
| White or Caucasian | 0 | 0 |
| Black or African American | 0 | 0 |
| Hispanic or Latino | 0 | 0 |
| Asian | 1476 | 218 |
| Native Hawaiian or other
Pacific Islander | 0 | 0 |
| | Training
(No. of subjects =
1,386) | Testing (No. of
subjects =424) |
|---------------------------|------------------------------------------|-----------------------------------|
| Age (years): | | |
| Mean Age (SD) | 48.1(12.6) | 47.3(11.6) |
| Age range | 1597 | 1184 |
| Sex: | | |
| Male | 898 | 342 |
| Female | 488 | 82 |
| Unknown | 0 | 0 |
| Race/Ethnicity: | | |
| White or Caucasian | 0 | 0 |
| Black or African American | 0 | 0 |
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| Hispanic or Latino | 0 | 0 |
|---|---|---|
| Asian | 1386 | 424 |
| Native Hawaiian or other | ||
| Pacific Islander | 0 | 0 |
Images for the dataset were labelled as follows:
Lesion Detection Function
Pre-Annotation (Initial Labeling) Process:
Full videos were randomly assigned to three gastroenterologists for the annotation of small bowel images. The doctors annotate one or multiple positive lesion image segments and one or multiple negative image segments for each video. These positive lesion image segments and negative image segments constitute the full video of the small bowel for objects. All positive lesion image segments are independently numbered and randomly sampled. Similarly, all negative image segments are independently numbered and randomly sampled. These two parts, obtained through sampling, form the annotated image dataset of the small bowel.
Annotation (Truthing) Process:
The image dataset obtained from the pre-annotation process is annotated by the three gastroenterologists using annotation software in a back-to-back manner. The computer automatically determines consistency and merges the classification results while preserving differing opinions. When the cutoff value for consistency is less than 3, two arbitration experts independently review and modify the classification results, correcting any missed diagnoses, misdiagnoses, or misjudgments. If difficult questions arise, the arbitration experts engage in collective discussion and confirmation.
Tract Site Recognition Function
Pre-Annotation (Initial Labeling) Process:
Full video data is randomly assigned to three experts. The experts mark the boundary positions of each site (including Oral Cavity and beyond, Esophagus, Stomach, and Small Bowel) within each video. For example, the boundary position where the first image enters the esophagus, the first image enters the stomach, and the first image enters the small bowel. These four sets of images form a complete video. Each set of images is individually labeled and randomly sampled. The site annotation data is generated by randomly sampling all site data.
Annotation (Truthing) Process:
The image dataset obtained from the pre-annotation process is annotated by three gastroenterologists using annotation software in a blinded manner. They annotate the classification labels for the four sites (including Oral Cavity and beyond, Esophagus, Stomach, and Small Bowel). The computer automatically determines the consistency and merges the classification results while preserving differing opinions. When the consistency cutoff value is less than 3, two adjudication experts independently review and modify the classification results, correcting any missed diagnoses, misdiagnoses, or misjudgments. In case of difficult cases, collective discussions and confirmations are conducted by the adjudication experts.
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Standalone Algorithm Testing
1. Lesion detection function
Following the training phase, the performance of the NaviCam ProScan to correctly recognize abnormal small bowel images was tested. For this purpose, 218 patients from the dataset described above were selected for testing, with results provided in Table 3 below. From that same dataset, normal and abnormal small bowel capsule endoscopy images were used to test the algorithm, with results provided in Table 4 below.
A. Patient-level analysis
| Expert Reading | |||
|---|---|---|---|
| Normal | Abnormal | ||
| ProScan | Normal | 33 | 2 |
| Prediction | Abnormal | 56 | 127 |
Patient-level sensitivity and specificity were determined to be 98% (95% CI: 93.95%-99.71%) and 37% (95%CI: 27.27%-48.02%), respectively. Subgroup analyses for patient-level sensitivity and specificity did not identify major differences when analyzed by gender, age, or lesion type.
Image /page/6/Figure/7 description: The image is a plot of the Receiver Operating Characteristic (ROC) curve. The ROC curve plots the true positive rate against the false positive rate. The area under the ROC curve (AUC) is 0.911 with a 95% confidence interval of 0.872 to 0.945. The plot also shows a dashed line representing the line of no discrimination, where the true positive rate equals the false positive rate. The title of the figure is 'Figure 3: Patient-Level ROC and AUC Analysis'.
The patient-level ROC analysis, shown in Figure 3 above, is based on the assumption of worst-case device failure analysis, where the AI algorithm identifies a case as positive if it detects at least one positive image.
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B. Image-level analysis
| Expert Reading | |||
|---|---|---|---|
| Normal | Abnormal | ||
| ProScan | |||
| Prediction | Normal | 14743 | 179 |
| Abnormal | 372 | 3439 |
| Table 4: Image-level Analysis Results |
|---|
Image level analysis refers to the analysis of the dataset consisting of normal (no lesion present) and abnormal (lesion present) images. This dataset may include multiple images of the same lesion from different angles. Image level sensitivity and specificity were determined to be 95.05% (95% CI: 94.28%-95.72%) and 97.54% (95% CI: 97.28%-97.78%), respectively. ROC and AUC analysis is shown below in Figure 4.
Image /page/7/Figure/4 description: This image shows a Receiver Operating Characteristic (ROC) curve. The ROC curve plots the true positive rate against the false positive rate. The area under the ROC curve (AUC) is 0.993 with a 95% confidence interval of 0.981 to 1.000. The ROC curve shows a point where the false positive rate is 0.025 and the true positive rate is 0.951.
Figure 4: Image-Level ROC and AUC Analysis
Conclusions from Lesion Detection Testing
Patient-level sensitivity was high, at 98%, demonstrating that the lesion detection function resulted in few false negatives for patients. Patient level specificity was 37%, suggesting that clinicians will need to carefully review ProScan findings to identify false positive predictions. Lesion-level (or object-level) sensitivity and specificity are unknown. Patients may have multiple true positive lesions. The lack of lesion-level data introduces uncertainty regarding the ability of the device to detect individual lesions. At the image-level, results demonstrate sensitivity of 95.05% and specificity of 97.54%.
2. Tract Site Testing
Following the training phase, the performance of the NaviCam ProScan to recognize the tract site (oral cavity and beyond, esophagus, stomach, or small bowel) was tested. For this purpose, 424 patients from the dataset described above were selected for testing. The performance of the site recognition model at the image level was tested using four categories of digestive tract sites: Oral Cavity and beyond, Esophagus, Stomach, and
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Small Bowel. The digestive tract images captured by capsule endoscopy exhibit a sequential relationship. Based on this sequential relationship and the results of image site classification, all digestive tract images were divided into four groups. Subsequently, the boundaries between different digestive tract sites, namely esophagus, stomach, or small bowel, were determined based on these groups, with results shown in Table 5 below.
| Expert Reading | |||||
|---|---|---|---|---|---|
| Oral Cavity | |||||
| and beyond | Esophagus | Stomach | Small | ||
| bowel | |||||
| ProScan Prediction | Oral Cavity | ||||
| and beyond | 3216 | 25 | 39 | 50 | |
| Esophagus | 11 | 704 | 5 | 24 | |
| Stomach | 5 | 10 | 17215 | 306 | |
| Small Bowel | 2 | 1 | 48 | 2986 |
Table 5: Tract Recognition - Image-level Analysis Results
Sensitivity and specificity were calculated for each category of anatomical site recognition (including Oral Cavity and beyond, Esophagus, Stomach, and Small Bowel) using a binary classification approach, with results shown in Table 6 below. The current category being evaluated is considered as positive, while the other categories are considered as negative. The Youden index is used to determine the optimal threshold, and sensitivity and specificity values are calculated accordingly. The prediction result of ProScan based on the confusion matrix are obtained by selecting the maximum probability value among the four anatomical site classifications for each image, and these predictions are used for statistical analysis and the final threshold setting for the device.
Table 6: Sensitivity and Specificity for Tract Site Recognition
| Sensitivity (95% CI) | Specificity (95% CI) | |
|---|---|---|
| Oral cavity and beyond | 99.47% (99.14%-99.68%) | 99.50% (99.39%-99.58%) |
| Esophagus | 98.92% (97.79%-99.50%) | 99.10% (98.98%-99.22%) |
| Stomach | 99.60% (99.49%-98.69%) | 99.06% (98.80%-99.26%) |
| Small Bowel | 99.26% (98.89%-99.51%) | 98.36% (98.18%-98.52%) |
Conclusions from Tract Site Recognition Testing
Based on the actual test results, the site recognition sensitivity and specificity values for each site (including Oral Cavity and beyond, Esophagus, Stomach, and Small Bowel) are all above 98%.
SUMMARY OF CLINICAL INFORMATION
Retrospective Evaluation
In a retrospective evaluation, two independent reviewers in China read capsule endoscopy images from 87 patients. The image review time of NaviCam reading with and without the ProScan feature on was assessed. The NaviCam SB System with ProScan feature on significantly
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reduced the physician's reading time compared to the reading time of the NaviCam SB System without the ProScan on (21.47+8.05 vs. 58.10±45.28, p