Introduction
Colonoscopy is generally considered the gold standard method for colorectal cancer
(CRC) screening, which can decrease the incidence and mortality of CRC by 40%–50%
[1 ]
[2 ]. However, colonoscopy is still associated with a high miss rate of colorectal neoplasia,
and the adenoma detection rate (ADR) varies among operators. It is increasingly recognized
that the effectiveness of the procedure relies on the quality of the colonoscopy.
Most current guidelines for surveillance intervals are based on high quality colonoscopy;
suboptimal quality is associated with a higher risk of missed adenomas and serrated
lesions, which are linked to postcolonoscopy CRC (PCCRC) [3 ].
Various quality indicators have been introduced for colonoscopy, including adequate
withdrawal time, adequate bowel preparation, and ADR [4 ]. Traditional quality control indicators must be assessed by endoscopists to ensure
clinical efficacy and increase accuracy. The introduction of artificial intelligence
(AI) allows for the development of new AI-assisted quality control indicators that
provide more effective evaluations [5 ]
[6 ]
[7 ]. For instance, effective withdrawal time has been proposed, using AI to automatically
eliminate unqualified intervals during the withdrawal phase, thereby enhancing the
efficacy of quality indicators [7 ]. Manual calculation of the effective withdrawal time is challenging, but AI can
do this task swiftly and conveniently, offering a stronger correlation with ADR than
the traditional indicator of standard withdrawal time.
To our knowledge, there are no studies on the accurate and automated assessment of
colorectal mucosal exposure. Based on an AI system, we propose a novel colonoscopy
quality indicator, namely the cumulative colorectal mucosal exposure area (CCMEA),
to evaluate mucosal exposure during the withdrawal observation period. We designed
a prospective observational study in which participants were divided into two groups
based on the identified CCMEA threshold, with comparisons made of the differences
between the two groups.
Methods
Establishment and threshold determination of the CCMEA system
The colorectal mucosal exposure area (CMEA) is defined as the proportion of clearly
exposed mucosa within a single colonoscopy frame relative to the entire field of view.
This definition ensures that any polyp in the field can be immediately recognized
by an endoscopist. Regions obscured by reflection, fecal material, occlusion, or excessive
darkness will not be annotated as well-exposed mucosa. [Fig. 1 ]
a shows the representative CMEA in single image frames. The CMEA was calculated as
follows: CMEA = mucosal exposure area/endoscopy view. The cumulative measure, the
CCMEA, was defined as the sum of the CMEAs of all qualified colonoscopy frames during
the complete withdrawal process.
Fig. 1 Images demonstrating the development of the cumulative colorectal mucosal exposure
area (CCMEA) system showing: a example segmentation of the colorectal mucosal exposure area (the cyan line is the
area of colorectal mucosal exposure labeled by the endoscopist; the blue line is the
model prediction); b the stages of the CCMEA system, with example qualified and unqualified images. DCNN,
deep convolutional neural network.
The CCMEA system consists of three deep convolutional neural network (DCNN) models
([Fig. 1 ]
b ). DCNN1 was employed to filter out unqualified images, including in vitro, blurred,
flushing, instrument, operation, or chromoendoscopy images. Its performance was comprehensively
validated in our previous study, where ResNet50 was employed as the backbone network,
achieving an accuracy of 98.9% [5 ]. DCNN2 segmented the clearly exposed mucosa in the endoscopy view of qualified images,
which were used as the molecules for calculation of the CMEA. During routine endoscopic
procedures, endoscopists conducted the withdrawal procedure using white-light endoscopy,
while chromoendoscopy was mainly used for diagnosis. Therefore, CCMEA calculations
were discontinued when chromoendoscopy was in use. DCNN3 was used to segment the endoscopy
view of the qualified image within the frame, which was used as the denominator of
CMEA calculation to rule out the bias caused by the inconsistency of image regions
between different endoscope manufacturers or types.
For DCNN2 and DCNN3, we used 3597 and 104 colonoscopy images, respectively, from the
Endoscopy Center of Renmin Hospital of Wuhan University (RHWU) for image labeling
and model training. Four endoscopists with more than 3 years of colonoscopy experience
used an online image annotation tool, VGG Image Annotator (https://www.robots.ox.ac.uk/~vgg/software/via/via-2.0.11.html ), for labeling. The final review was conducted by a senior endoscopist (≥10 years’
experience). Both the DCNN2 and DCNN3 adopted UNet++ as their framework architecture
(details are available in Appendix 1s , see online-only Supplementary material).
The Dice coefficient and mean Intersection over Union (mIoU) were adopted to assess
the performance of the segmentation models. DCNN2 had a Dice coefficient of 0.93 and
mIoU of 0.87; DCNN3 had a Dice coefficient of 0.99 and mIoU of >0.99. [Fig. 2 ] shows the segmentation of the CMEA by DCNN2 in a single colonoscopy image.
Fig. 2 Visualization of the colorectal mucosal exposure area segmentation showing: a the original endoscopic images obtained by colonoscopy; b comparative results of area labeling, where the green area indicates the overlapping
area for endoscopist labeling and system prediction, the blue areas represent the
mucosal exposure range manually labeled by the endoscopist only, and the red areas
represent the mucosal region automatically predicted by the cumulative colorectal
mucosal exposure area (CCMEA) system only.
We retrospectively collected colonoscopy videos from 716 patients who underwent colonoscopy
at the Endoscopy Center of RHWU between March 2024 and August 2024 to validate the
system performance. We employed this dataset to explore the CCMEA threshold for qualified
colonoscopy. The eligibility criteria were patients aged >18 years with complete colonoscopy
videos available. Patients who did not undergo biopsy, were pregnant or breastfeeding,
had polyp syndromes, inflammatory bowel disease (IBD), a history of colorectal surgery,
CRC, or incomplete cecal intubation were excluded.
For the purpose of this study, a per-colonoscopy definition was established. All colonoscopy
videos in the threshold dataset were analyzed by the CCMEA system, and patients in
the dataset were grouped by CCMEA in intervals of 1000. The qualified CCMEA threshold
was determined based on the 25% ADR criterion for colonoscopy screening. As shown
in [Fig. 3 ], which presents the fitted curve between CCMEA groupings and ADR, a CCMEA of 2000
was selected as the threshold to ensure that ADR exceeded 25%, thereby enabling higher
quality endoscopic examinations. Within this study, a "qualified colonoscopy" therefore
refers to an examination with a CCMEA value ≥2000, a threshold that corresponds to
better visualization of the colorectal mucosa, as well as reliable adenoma detection.
An "unqualified colonoscopy" refers to an examination with a CCMEA value <2000, where
mucosal visualization may be insufficient to guarantee effective adenoma identification.
Fig. 3 Threshold curve for the cumulative colorectal mucosal exposure area (CCMEA) grouped
in increments of 1000. A CCMEA threshold of 2000 was chosen to guarantee an adenoma
detection rate (ADR) of >25%.
Study design and participants
A prospective multicenter observational study was conducted to objectively evaluate
the threshold of the CCMEA and assess the utility of the CCMEA system in clinical
practice. Between October 2024 and November 2024, consecutive patients aged >18 years
undergoing colonoscopy were recruited at three healthcare centers (RHWU, Wuhan Eighth
Hospital [WEH], and JingXing County Hospital [JXCH]). The inclusion criteria were
as follows: age ≥18 years; colonoscopy performed; ability to read, understand and
sign the informed consent; the investigators believed that the participant could understand
the process of this clinical study, and was willing and able to cooperate and complete
all research processes. The exclusion criteria were as follows: pregnant or breastfeeding;
drug or alcohol abuse or mental illness in the past 5 years; previous colorectal surgery;
multiple polyp syndrome; IBD; bowel stricture; CRC; bowel obstruction or perforation;
failure to reach the cecum during the procedure; contraindications to biopsy; polyposis
syndrome or IBD having been suspected during the procedure.
Procedure
Patients who met the inclusion and exclusion criteria and provided informed consent
were enrolled in the study. The basic demographic characteristics of the participants,
including sex, age, body mass index (BMI), and indication for colonoscopy, were recorded
before colonoscopy.
Patients underwent routine colonoscopy procedures. BL-7000 (Fujifilm, Kanagawa, Japan)
and CF-HQ290/CF-Q260AI (Olympus Optical, Tokyo, Japan) colonoscopes were used in this
study. Biopsies were taken for histological examination after polyps were detected.
The CCMEA system was employed to assess and record the CCMEA during the examination.
The researchers documented the results as they occurred in real-time. Colonoscopy
videos, endoscopy reports, and pathology reports were collected following the colonoscopy.
The pathological diagnosis was regarded as the gold standard.
Ethics statement
This study was approved by the Ethics Committee of RHWU (No. WDRY2024-K071). All participants
signed the informed consent form.
Outcome
The primary outcome of this study was the ADR, which was defined as the proportion
of patients in whom at least one pathologically confirmed adenoma was detected during
colonoscopy. Secondary outcomes were: the detection rates for adenomas of different
sizes (diminutive, ≤5 mm; small, >5 to <10 mm; large, ≥10 mm), the polyp detection
rate (PDR), the detection rates for polyps of different sizes, detection rates for
advanced adenomas (lesion diameter ≥10 mm, high grade dysplasia, or villous histology),
and mean numbers of adenomas or polyps per patient.
Sample size
This study primarily aimed to investigate the relationship between CCMEA and ADR.
There was no previous study regarding the distribution of CCMEA thresholds and ADR.
Therefore, based on prior experience with quality control indicators and historical
ADR data from the three participating healthcare centers (RHWU, WEH, and JXCH), we
assumed an ADR of 33% in the qualified group and 23% in the unqualified group, with
a 10 percentage point difference or greater between the two groups [5 ]
[6 ]
[8 ]
[9 ]
[10 ].
PASS 15.0 (NCSS Statistical Software, Kaysville, Utah, USA) was employed to calculate
the sample size. Based on a 10% dropout rate, 550 participants were determined as
being necessary to achieve 74% power at a significance level of α = 0.05 for differences
in ADR between the two groups.
Statistical analysis
The prospective datasets were divided into a qualified group (above the threshold)
and an unqualified group (below the threshold) based on the determined CCMEA threshold
for analysis. Continuous variables are presented as mean and SD and categorical variables
as frequencies and percentages. For baseline comparisons, continuous variables were
analyzed using two-tailed t tests or Mann–Whitney U tests, while categorical variables were analyzed using the chi-squared test or Fisher's
exact test.
For comparisons of categorical outcomes, generalized linear mixed models (GLMMs) with
a logit link were used, adjusting for sex (a baseline variable with significant differences)
and colonoscopy indication (the latter considered clinically meaningful), with healthcare
center included as a random effect. Adjusted odds ratios (aORs) with 95%CIs were reported
for comparisons between the two groups. For continuous outcomes, GLMMs with a Poisson
distribution and a log link were applied, adjusting for the same covariates and random
effects. Adjusted incidence rate ratios (aIRRs) with 95%CIs were calculated to compare
the two groups.
Given the number of statistical tests conducted, Bonferroni correction was applied
to account for multiple comparisons. A total of 11 statistical tests (one for the
primary end point and 10 for secondary outcomes) were subject to Bonferroni correction,
resulting in an adjusted significance level of 0.0045 (0.05/11). All P values for the primary and secondary outcomes were interpreted at this adjusted threshold.
Statistical analyses were conducted using SPSS (version 26.0, IBM, New York, USA)
and R (version 4.3.3, R Foundation for Statistical Computing, Vienna, Austria).
Results
A total of 551 consecutive patients undergoing colonoscopy were screened between 1
October 2024 and 30 November 2024. There were 41 patients who were excluded before
or during the procedure (suspected IBD [n = 6], inadequate bowel preparation [n =
15], failed cecal intubation [n = 7], no biopsies [n = 8], and cancerous obstruction
[n = 5]). Finally, 510 eligible patients were included in the analysis (169 from RHWU,
151 from JXCH, and 190 from WEH) ([Fig. 4 ]). Patient information was collected prospectively, without there being any missing
values. The baseline characteristics of the patients (238 men [46.7%]; average age
58.5) are shown in Table 1s .
Fig. 4 Flowchart of patient inclusion and exclusion in the prospective study. CCMEA, cumulative
colorectal mucosal exposure area.
Based on a CCMEA of 2000 as the threshold, the patients were divided into the qualified
group (n = 270) and the unqualified group (n = 240). On their baseline information,
there were significant differences between the two groups in terms of sex and healthcare
center ([Table 1 ]). The detection rates of all types of colorectal lesions were higher in the qualified
group than in the unqualified group. After adjusting for confounding factors, the
detection rates of all types of lesions in the qualified group remained significantly
different from those in the unqualified group ([Table 2 ]).
Table 1 Comparison of the baseline information for the qualified and unqualified group in
the prospective validation dataset.
Characteristics
Qualified group
(n = 270)
Unqualified group
(n = 240)
P value
RHWU, Renmin Hospital of Wuhan University; WEH, Wuhan Eighth Hospital; JXCH, JingXing
County Hospital.
Age, mean (SD), years
59.2 (10.6)
57.9 (12.0)
0.22
Sex, male, n (%)
146 (54.1)
92 (38.3)
<0.001
Body mass index, mean (SD), kg/m2
24.0 (3.4)
23.4 (3.9)
0.06
Indication for colonoscopy, n (%)
0.40
62 (23.0)
49 (20.4)
160 (59.3)
156 (65.0)
48 (17.8)
35 (14.6)
Endoscope manufacturer, n (%)
0.45
176 (65.2)
164 (68.3)
94 (34.8)
76 (31.7)
Boston score, mean (SD)
7.89 (1.18)
7.94 (1.15)
0.64
Healthcare center, n (%)
<0.001
106 (39.3)
63 (26.3)
116 (43.0)
74 (30.8)
48 (17.8)
103 (42.9)
Table 2 Comparison of the primary and secondary outcomes for the qualified and unqualified
groups in the prospective validation dataset.
Outcome
Qualified group
(n = 270)
Unqualified group
(n = 240)
Absolute difference
(95%CI)
Adjusted ratios1 (95%CI)
P value1
GLMM, generalized linear mixed model; IRR, incident rate ratio; OR, odds ratio.
1 Adjusted P values, adjusted ORs, and adjusted IRRs were derived from GLMMs.
2 Categorical variables were analyzed using GLMMs with a logit link, adjusting for
sex and colonoscopy indication, with healthcare center as a random effect. P values
<0.0045 (0.05/11) were considered significant as Bonferroni correction was adopted
for multiple tests.
3 Continuous variables were analyzed using GLMMs with a Poisson distribution and a
log link, adjusting for sex and colonoscopy indication, with healthcare center as
a random effect. P values <0.0045 (0.05/11) were considered significant as Bonferroni
correction was adopted for multiple tests.
Detection rates
Percentage points
Adjusted OR
Adenoma detection rate, n (%)2
145 (53.7)
34 (14.2)
39.5 (31.8–46.5)
7.8 (5.0–12.8)
<0.001
Adenoma size, n (%), mm2
114 (42.2)
24 (10.0)
32.2 (25.0–39.0)
6.9 (4.1–11.5)
<0.001
55 (20.4)
9 (3.8)
16.6 (11.2–22.1)
5.8 (2.7–12.3)
<0.001
14 (5.2)
1 (0.4)
4.8 (2.0–8.1)
12.2 (1.6–91.6)
0.02
Advanced adenoma rate, n (%)2
16 (5.9)
2 (0.8)
5.1 (2.0–8.6)
7.7 (1.7–34.2)
0.007
Polyp detection rate, n (%)2
242 (89.6)
96 (40.0)
49.6 (42.1–56.4)
13.1 (7.8–21.8)
<0.001
Polyp size, n (%), mm2
222 (82.2)
83 (34.6)
47.6 (39.7–54.7)
10.0 (6.3–15.8)
<0.001
77 (28.5)
21 (8.8)
19.8 (13.2–26.2)
4.5 (2.6–7.9)
<0.001
19 (7.0)
3 (1.3)
5.8 (2.4–9.6)
5.8 (1.7–20.2)
0.006
Detected numbers
Lesion numbers
Adjusted IRR
Number of adenomas, mean (SD)3
1.0 (1.3)
0.2 (0.5)
0.8 (0.7–1.0)
5.9 (4.2–8.4)
<0.001
Number of polyps, mean (SD)3
5.8 (6.4)
1.3 (3.8)
4.5 (3.5–5.4)
4.0 (3.5–4.5)
<0.001
There was a 39.5 percentage point difference in ADR, with the qualified group showing
a higher rate than the unqualified group (53.7% vs. 14.2%; aOR 8.0, 95%CI 5.0–12.8;
P < 0.001). Similarly, the qualified group outperformed the unqualified group for adenoma
detection across the various sizes, and particularly for lesions ≤5 mm (42.2% vs.
10.0%; aOR 6.9, 95%CI 4.1–11.5; P < 0.001). Furthermore, the detection rate of advanced adenomas in the qualified group
was higher than that in the unqualified group (5.9% vs. 0.8%; aOR 7.7, 95%CI 1.7–34.2;
P = 0.007).
In terms of polyp detection, the qualified group had a significantly higher PDR than
the unqualified group (89.6% vs. 40.0%; aOR 13.1, 95%CI 7.8–21.8; P < 0.001). This difference was again particularly notable for lesions ≤5 mm (82.2% vs.
34.6%; aOR 10.0, 95%CI 6.3–15.8; P < 0.001). Additionally, the qualified group had significantly higher mean numbers of
both adenomas (1.0 vs. 0.2; aIRR 5.9, 95%CI 4.3–8.4; P < 0.001) and polyps detected (5.8 vs. 1.3; aIRR 4.0, 95%CI 3.5–4.5; P < 0.001).
Discussion
In this study, we proposed a novel indicator, the CCMEA, to comprehensively evaluate
the quality of colonoscopy. Specifically, we employed deep learning models to develop
an AI system capable of automatically calculating the CCMEA. The system assesses whether
endoscopists have conducted the procedure adequately and comprehensively.
Missed diagnosis of colorectal neoplasia is the most common cause of PCCRC [11 ]
[12 ]; 57.8% of PCCRC cases are attributed to neoplastic lesions missed during colonoscopy
[13 ]. Therefore, the most important goal of colonoscopy is to maximize the detection
of lesions, especially adenomas. Detection of lesions largely relies on the thorough
examination of the entire mucosa by endoscopists; however, variations in endoscopists'
technical proficiency can lead to significant differences in colonoscopy quality.
Conventional colonoscopy quality indicators, such as the withdrawal time, lesion detection,
and the Boston Bowel Preparation Scale (BBPS) score, rely on manual scoring and have
various limitations.
To address these limitations, we proposed a new quality control indicator, the CCMEA,
which can be calculated automatically. This automated, objective, and accurate assessment
system of the colorectal mucosal exposure area helps evaluate whether endoscopists
have performed the operation adequately and comprehensively. By setting the threshold
for the CCMEA on the basis of the 25% ADR standard for colonoscopy, a threshold of
2000 was proposed for the CCMEA as an indicator of a qualified colonoscopy. We found
that ADR was significantly higher in patients whose CCMEA was above the threshold.
CCMEA was strongly correlated with ADR, and was better than the traditional colonoscopy
quality index of standard withdrawal time. The CCMEA is expected to become a new intelligent-era
quality control indicator for colonoscopy.
Compared with previous indicators, the CCMEA provides a more refined and practical
evaluation of colonoscopy mucosal exposure. Liu et al. developed an AI-based system
to assess the quality of fold exposure (FEQ) during colonoscopy, using the classification
model to differentiate lumen and wall views, and calculating the visible folds proportion
for the assessment of withdrawal performance during the withdrawal process [14 ]. This study showed that the system's evaluation of FEQ was strongly correlated with
ADR and withdrawal time. Compared with the FEQ study, the CCMEA, as a new evaluation
indicator, expanded the assessment scope of mucosal observation. By segmenting effective
and ineffective mucosal exposure areas frame by frame, it directly quantified the
proportion of mucosal exposure during colonoscopy. Compared with the classification
model, the segmentation model can provide more refined mucosal exposure information
and directly focuses on the visible area of the entire mucosa. The evaluation index
is more intuitive, which is conducive to the understanding and application of endoscopists,
especially novice endoscopists, and can reduce the rate of missed diagnosis of lesions
and provide more accurate evaluation tools. By accumulating real-time the effective
exposure area across frames, the CCMEA reflects full withdrawal-phase mucosal coverage
and enables instant feedback, aligning better with the clinical needs of endoscopists,
and may have greater practicality in operational guidance.
Poor bowel preparation, inadequate examination of mucosal folds, incomplete dilation
of the colorectal cavity, and short withdrawal time can lead to blind areas during
colonoscopy [15 ]
[16 ]. Among these, the withdrawal time has been identified as an important quality indicator
of mucosal observation quality [17 ], predicting the detection of adenomas [18 ]; however, some studies have reported that the time of device withdrawal was not
necessarily associated with a significant increase in lesion detection rate [19 ], and that there is no relationship with the risk of PCCRC [20 ]. Therefore, withdrawal time remains a controversial indicator for quality control
[21 ]. Calculation of the CCMEA is associated with many factors, such as withdrawal time,
withdrawal speed, endoscopist's ability to stably observe the mucosa, and bowel preparation.
Compared with withdrawal time, which is only associated with the withdrawal speed,
the CCMEA can more comprehensively evaluate the quality of mucosal examination, partly
representing the withdrawal time, bowel preparation, and mucosal exposure. Our study
showed that the CCMEA was superior to standard withdrawal time in terms of detecting
adenomas and polyps (Fig. 1s ). Therefore, the CCMEA may be a better indicator of quality than the traditional
standard withdrawal time.
During colonoscopy, our software can collect and analyze relevant data in a real-time
manner using intelligent algorithms. A CCMEA below the qualified threshold is an alarm
sign. The index and analysis results are presented to endoscopists through visual
representation of colorectal mucosal area and dashboard indicators, accompanied by
operational suggestions. Additionally, a professional quality control platform is
used for feedback. In terms of hardware, the CPU is an Intel Core i7–9700K running
at 3.60 GHz. The GPU is an NVIDIA 3070 with 8 GB of video random access memory (VRAM).
In terms of software, we used the NVIDIA TensorRT framework. We adopted the half-precision
floating-point (FP16) quantization technique to optimize the DCNN model. The above
configuration is sufficient to construct a clinically applicable CCMEA system with
real-time feedback capabilities.
Our study has certain limitations. First, CCMEA thresholds were retrospectively established
using data from a single-center and applied to a multicenter prospective cohort, with
all of the participating centers located only within China and endoscopic equipment
limited to specific manufacturers (Olympus and Fujifilm). Fig. 2s shows the corresponding CCMEA vs. ADR curve, whose consistent trends across datasets
support the CCMEA's potential as a quantifiable indicator for enhancing colonoscopy
quality in diverse clinical scenarios. While this design strengthened generalizability
within the included settings, there are differences in terms of diet, lifestyle, and
ADR/PDR, not only across regions but also across countries, alongside potential variations
in performance across diverse endoscopic systems globally. Notably, after Bonferroni
correction, some secondary outcomes did not reach statistical significance, which
is likely associated with the low rates of these outcomes, as such low detection rates
may have reduced statistical power to detect potential associations. Moving forward,
addressing these limitations will require large-scale international multicenter studies
spanning diverse countries, ethnicities, healthcare settings, and endoscopic devices
to confirm the CCMEA's robustness as a universal quality indicator.
Second, this study was an observational study, and further randomized controlled trials
should be conducted to clarify the auxiliary effect of the CCMEA system on the colonoscopy
operation of endoscopists, especially novice endoscopists.
Third, the applicability of the CCMEA in colonoscopies using mucosal exposure-enhancing
devices needs careful consideration. These devices improve adenoma detection by enhancing
mucosal visualization, which may alter the relationship between colonoscopy movement
patterns and the ADR [22 ]. The Endocuff device, for example, increased ADR in randomized controlled trials
[23 ]. Given this, the CCMEA, originally developed based on standard colonoscopy movement
dynamics, may underestimate or overestimate procedural quality in device-assisted
procedures, and adjustments to its calculation, such as accounting for device-specific
visualization enhancements, might be necessary to maintain validity. Because the availability
and routine use of these devices varies across different regions and healthcare settings,
the present study did not include distal attachment device-assisted colonoscopy, which
was necessary to determine the effect of the CCMEA in standard colonoscopy; however,
this limitation underscores the need for device-specific validation of the CCMEA.
Future studies should directly evaluate its performance in colonoscopies using mucosal
exposure-enhancing devices, explore whether modifications to the metric (e.g. recalibrating
movement–visibility correlations) are required, and develop targeted assessment methods
tailored to such devices.
Fourth, this study excluded participants with a BBPS score <6, limiting the generalizability
of our findings across populations with poor bowel preparation. Calculation of the
CCMEA is highly dependent on the quality of endoscopic images. When bowel preparation
is inadequate, in accordance with guidelines, repeat bowel preparation is needed for
a qualified colonoscopy, rather than direct calculation of the CCMEA, which may be
meaningless. While this exclusion was necessary to ensure the validity of the CCMEA
in high quality examinations, it could introduce a selection bias and limit the applicability
of the CCMEA to patients with adequate bowel preparation (BBPS ≥6). Therefore, it
is necessary to conduct studies specifically focusing on the validity of the CCMEA
in patients with insufficient bowel preparation. We are conducting a study to develop
a precise evaluation system for assessing bowel preparation quality, especially for
those with BBPS <6, which is expected to explore the application value of the CCMEA
in colonoscopy with poor bowel preparation and BBPS <6, and expand its utility across
diverse clinical scenarios in the future.
Finally, the CCMEA does not account for the mucosa hidden behind folds or requiring
retroflexion. From the technical point of view, the classification model should be
used to distinguish whether the fold is extended or not, and different coefficients
should be used for the CCMEA; however, it is difficult to evaluate hidden mucosa,
which is not visible to the naked eye.
In conclusion, we have proposed an AI-based CCMEA system for a more accurate assessment
of the exposed area of colorectal mucosa. The system can evaluate the clear exposure
area of the colorectal mucosa in a real-time manner during colonoscopy, which is expected
to be an important tool for colonoscopy quality control.
