Introduction

Colonoscopy is a promising diagnostic procedure for removal colorectal neoplasia, which can help prevent colorectal cancer. With an increasing trend towards colorectal cancer prevention, the number of colonoscopies and polypectomies is increasing, with approximately 11 million colonoscopy procedures performed annually in the United States alone. This has resulted in a significant burden on physicians and healthcare resources. Previous studies have demonstrated the utility of computer-aided diagnosis (CADx) systems in improving the characterization of colorectal polyps. However, the time-saving aspect of implementing AI-based CADx systems has not been thoroughly investigated. In this study, we aim to evaluate the potential of CADx systems to reduce the time required for polyp characterization.

Aims & Methods

This was a single center, retrospective study, was conducted at a university hospital between July 2021 and January 2022. We randomly extracted pathologically confirmed 100 polyps and one representative NBI image was prepared for each lesion. Three non-experts (a resident with no endoscopic experience, a gastroenterologist with one year of experience, and a gastroenterologist with two years of experience) were included. Randomly selected NBI images were shown separately to these doctors separately using laptop computer. They diagnosed each lesion diagnosed each lesion as neoplastic or non-neoplastic. For this study, we considered sessile serrated lesions (SSLs) as neoplasms. In the first examination, non-experts diagnosed without any assistance. After a four-week interval, a second examination was conducted with the assistance of a CADx system that provided a pathological prediction (neoplastic or non-neoplastic). We recorded the time required for diagnosing each polyp and compared it between the first and second examinations. The outcomes were, difference in diagnostic time and diagnostic accuracy of the endoscopists with or without CADx assistance. Wilcoxon signed rank test and McNemar test were used.

Results

Out of the 100 lesions, there were 72 adenomas/intramucosal carcinomas, 6 SSLs, 21 hyperplastic polyps, and 1 juvenile polyp. With the use of CADx, the mean (±SD) diagnostic time was significantly shorter than without CADx assistance (10.7±8.2 sec vs 5.3±3.6 sec, P value < 0.001). Regarding the diagnostic performance for differentiating neoplasms from non-neoplasms, diagnostic accuracy was significantly higher in with CADx assistance (75%　[95%CI 69.7-79.8] vs 92.3% [95%CI 88.7-95.1], P value < 0.001). Assuming the mean number of polyps per patient as 1.87 (Taku Sakamoto, et al. United European Gastroenterol J, 2019), time saving effect was estimated as 17 min / 100 colonoscopies.

Conclusion

Our study demonstrates that the utilization of the CADx can save diagnosing time and increase accuracy for non-experts. Therefore, we suggest that the CADx has the potential to decrease examination time by expediting the diagnosis of lesions.

Introduction

In the carcinogenic pathway of colorectal cancer, the first concept is the "adenoma-carcinoma sequence" and the second concept is the " serrated pathway ". In these concepts, adenomas and serrated lesions gradually progress to cancer. The third concept is the "de novo pathway," in which cancer develops without adenomas or serrated lesions and is also characterized by high malignancy and rapid progression. We have endoscopically characterized depressed colorectal cancers considered to be of the "de novo pathway" and validated the usefulness of pit pattern and EC classification.

Aims & Methods

A total of 54819 colorectal neoplasms were resected endoscopically or surgically in our institution from April 2001 to December 2024. They were subjects of this study. Advanced cancers were excluded. Of these, 1694 lesions were submucosally invasive (T1) carcinomas, 7330 lesions were classified as high-grade dysplasia and 39977 lesions as low-grade dysplasia. Morphologically, they were divided into 3 types: depressed, flat, and protruded type comprising 1.0 (%), 46.4 (%),and 52.6（%), respectively. Specifically, the distinctive features of depressed type neoplasms concerning their pit pattern and EC classification were examined. EC classification is a classification for an endocytoscopy that can evaluate pathology, such as nuclear atypia and glandular structure, by magnifying stained cells. EC1a is diagnosed as normal mucosa, EC1b is diagnosed as hyperplastic polyps, and if slit-like glandular ducts or spindle-shaped nuclei are present, it is classified as EC2 and diagnosed as an adenoma or intramucosal carcinoma. If irregularly shaped glandular ducts or nuclei with a high N/C ratio are found, it is classified as EC3a, and diagnosed as intramucosal carcinoma or SM lightly invasive carcinoma. If the glandular ducts are indistinct and the nucleus is irregularly shaped and darkly stained, it is classified as EC3b and diagnosed as SM highly invasive carcinoma.

Results

Most of the flat (91.5%) and protruded type (94.9%) lesions showed type IIIL or IV pit pattern, which are common findings in adenomas. On the other hand, most of the depressed type lesions (92.6%) classified as type IIIs, VI or VN pit patterns, are typically seen in carcinomas. As for endocytoscopy, most of flat and protruded type lesions were identified as EC2 corresponding to adenomas. In contrast, relatively high rates of depressed type lesions were categorized as EC3a (40.3%) and EC3b (52.4%), consistent with invasive carcinomas.
The proportion of T1 carcinomas in depressed type lesions was 58.7%, whereas in flat and protruded type lesions it was 2.5% and 2.5%, respectively. Additionally, 12.0% of depressed type lesions less than 5mm in diameter were T1 carcinomas, whereas flat and protruded type lesions were 0.01% and 0%, respectively.

Conclusion

This study revealed that depressed lesions were classified as type IIIs, VI or VN pit patterns on magnifying endoscopy and as type EC3a or EC3b on endocytoscopy. Lesions with these features or pit patterns tend to invade the submucosal layer even if they are small size. Despite their high potential for malignancy, depressed lesions are often ignored. However, it is critical to recognize their features and accurately diagnose lesions based on morphology, pit pattern, and EC classification.

Disclosure

Shin-ei Kudo and Masashi Misawa received speaking honoraria from Olympus Corporation and have an ownership interest of the products of Cybernet Systems. Tetsuo Nemoto received a research grant from Olympus Corporation for other studies. Other authors have nothing to declare.

Introduction

Recently, artificial intelligence-based computer-aided detection (CADe) for colonoscopy is garnering significant attention due to its effectiveness in increasing adenoma detection rate (ADR). However, theoretically, CADe system cannot decrease exposure error which polyps obscured by an object. In this regard, computer-aided quality improvement (CAQ) which expects to induce the meticulous mucosal inspection and leading to a reduction of blind spot area. In this study, we developed and evaluated CAQ for colonoscopy.

Aims & Methods

This was a single center, retrospective study from period April 1, 2022 to February 25, 2023. We randomly extracted colonoscopy videos and created 18,922 training images. Quadrant images were made from one image and three endoscopists annotated the following scores; i) appropriateness for mucosal inspection, ii) appropriate distance, and iii) presence of blind area in the field of view (e.g., fold or flexure). We conducted supervised machine learning using annotated images. The CAQ deployed multi-head deep convolutional neural network model which can output multiclass prediction from one image. Consequently, the CAQ system outputs aforementioned score from whole colonoscopy video. To validate its performance, we retrospectively analyzed colonoscopy procedures performed by seven endoscopists using the CAQ. We compared calculated CAQ model scores and expertise of endoscopists.

Results

29 videos were included as validation samples (8 videos were performed by non-expert endoscopists) As a result, there was a significant difference in the proportion of appropriate images during the examination between experts and non-experts (median 56% vs. 49%, p=0.005, Mann–Whitney U test) and there was also a significant difference in the evaluation score of the distance between the colon mucosa and the endoscope (median 4.2 vs. 3.2, p=0.0004, Mann–Whitney U test). In addition, there was a significant difference in the scores evaluating the presence of blind areas in the field of view (median 1.6 vs. 1.2, p=0.0007, Mann-Whitney U test).

Conclusion

In this study, developed CAQ system could quantify the skill of the endoscopists. Such CAQ would be expected to standardize the observation technique of colonoscopy.

Introduction

Colonoscopy is the "gold-standard" technique for early diagnosis and resection of colorectal cancer precursor lesions. However, this technique has limitations, and can result in missed adenomas and polyps.
In recent years, artificial intelligence (AI) systems have been developed for the automatic detection of polyps. This study aims to investigate the impact of an AI system applied in real time on the detection rate, compared with conventional detection by the endoscopist.

Aims & Methods

A prospective observational study was carried out by seven gastroenterologists in the Digestive Endoscopy Unit of a tertiary hospital. Colonoscopies were performed on consecutive patients for polyp screening or surveillance. High-definition colonoscopes were used for the examinations and an AI system was applied in real time.
Each procedure consisted of two phases: (1) an initial assessment was performed by the endoscopist who withdrew the endoscope after reaching the cecum, identifying all visualized lesions without resecting them, and (2) the colonoscope was reintroduced up to the cecal pole with an AI device. The automatic detection of the AI device was assessed by means of a light and sound signal.
The primary objective was to compare the detection rate of adenomas and polyps. Secondary objectives were to compare the total number of polyps and serrated lesions detected, as well as detection of polyps by size, location and morphology, with and without the device.

Results

The sixty-six patients included in the analysis were examined simultaneously by colonoscopy, and the AI device was applied. The AI system identified a greater mean number of adenomas (2.85 vs 2.41, p<0.05) and polyps (3.68 vs 2.92, p<0.05), as well as a greater number of serrated lesions (0.83 vs 0.52, p <0.05), compared with the endoscopist. No significant differences were found in the adenoma detection rate (78.8% vs 75.7%; p > 0.05).
AI significantly increased the detection of small lesions (178 vs 135, p<0.001), and those of Paris type IIa flat morphology (165 vs 123, p<0.001). There were no differences in the detection of lesions larger than 5 mm or with other morphologies.

Detection of adenomas and polyps and characteristics
	Examination by endoscopist	Examination IA assisted	p value
Adenoma detection rate (ADR)	0.7575	0.7878	0.678
Polyp detection rate (PDR)	0.7878	0.8333	0.505
Total number of adenomas, n (mean)	159 (2.41)	188 (2.85)	< 0.05
Total number of polyps, n (mean)	193 (2.92)	243 (3.68)	< 0.05
Total number of serrated lesions, n (mean)	34 (0.52)	55 (0.83)	< 0.05
Polyp location Right colon, n (%) Transverse colon, n (%) Left colon, n (%)	70 (36.3) 52 (26.9) 71 (36.8)	98 (40.3) 64 (26.4) 81 (33.3)	< 0.001 0.012 0.018
Polyp morphology Paris 0-IIa, n (%) Paris 0-IIb, n (%) Paris 0-IIc, n (%) Paris 0-Ip, n (%) Paris 0-Is, n (%) LST, n (%)	123 (63.7) 8 (4.2) 1 (0.5) 17 (8.8) 41 (21.2) 3 (1.6)	165 (67.9) 13 (5.3) 1 (0.4) 17 (7) 42 (17.3) 5 (2.1)	< 0.001 0.096 1.000 1.000 0.564 0.157
Polyp size 0-5 mm, n (%) 6-9 mm, n (%) > 10 mm, n (%)	135 (70) 30 (15.5) 28 (14.5)	178 (73.2) 33 (13.6) 32 (13.2)	< 0.001 0.450 0.102

Conclusion

In a high-risk population, the application of an AI system allowed a significantly higher number of polyps and adenomatous lesions to be detected, at the expense of small lesions and those with flat morphology, compared with conventional evaluation by the endoscopist. There were no differences in the adenoma detection rate.

Introduction

Computer-aided detection (CADe) system increases the efficiency of adenoma detection during colonoscopy, which is expected to reduce the incidence of colorectal cancer as well as cancer treatment-associated medical costs. However, most lesions detected using CADe have been reported to be diminutive adenomas that may not affect patient prognosis. Additionally, CADe may impose an immediate economic burden because the system needs to be purchased. Consequently, the use of CADe for all colonoscopy procedures seems impractical. This study aimed to evaluate the usefulness of CADe in patients undergoing regular surveillance colonoscopy (SCS), to identify the characteristics of endoscopists and patients who may benefit significantly from CADe.

Aims & Methods

Patients who underwent regular SCS with CADe at Hiroshima University Hospital from January to March 2023 and had undergone colonoscopy at least twice in the previous three years using a conventional detection method were enrolled. Forty-eight endoscopists who had collectively performed ≥800 colonoscopies performed the examinations. Endoscopic resection was performed for lesions ≥6 mm in size, flat and depressed neoplastic lesions ≤5 mm in size, and lesions with suspected high-grade dysplasia or T1a carcinoma, according to Japanese guidelines. Diminutive polypoid adenomas ≤5 mm in size were followed up without resection. Lesions pathologically identified as adenomas >6 mm or carcinomas were defined as index lesions (ILs). Lesions pathologically identified as adenomas ≥10 mm in size, tubulovillous adenomas, and high-grade dysplasia were defined as advanced adenomas (AAs). The clinicopathological findings of the lesions detected by CADe were evaluated, and the outcomes were compared between the semi-clean colon (diminutive polypoid adenomas ≤5 mm in size followed up without resection) and clean colon (no adenomatous polyps) groups.

Results

Overall, 78 patients were included. CADe identified 46 adenomas that were not detected by previous colonoscopy in 28 patients, including three ILs but no AAs. The mean withdrawal time was 824 ± 353 s, and the mean tumor diameter was 3.3 mm (range, 2–8 mm). The most common gross type was 0-Is (70%), followed by 0-Isp (17%) and 0-IIa (13%). Most tumors were located in the ascending and sigmoid colons (28%), followed by the transverse colon (26%), cecum (7%), descending colon (7%), and rectum (4%). The ILs were 0-Isp Isp of 6 mm diameter in the ascending colon, 0-Is of 7 mm diameter in the transverse colon, and 0-Isp Isp of 6 mm diameter in the descending colon, all of which were adenomas with lesions on the folds. Overall, 34.1% and 38.2% of the patients in the semi-clean and clean colon groups, respectively, had newly detected adenomas. No new neoplastic lesions were detected using CADe in patients without a history of endoscopic resection. When comparing the clinical backgrounds of patients with and without new ILs detected in CADe SCS, a significant difference was found in the IL detection rate at the previous SCS between the groups (67% vs. 12%, p = 0.0319).

Conclusion

CADe may help detect adenoma in SCS for patients with semi-clean colons and those with clean colons and a history of endoscopic resection. Its effectiveness is considered more for patients with ILs at the previous SCS.

Introduction

Optical diagnosis of colorectal polyps is challenging. Artificial intelligence based systems allow the detection and histological characterization of polyps.

Aims & Methods

The aim of this study is to compare the diagnostic accuracy of Polydeep, a CADe/x system with expert endoscopists for the optical diagnosis of colorectal polyps.
We designed a diagnostic test study based on the evaluation of 2364 polyps still images with confirmed histological diagnosis (69.12% adenomas, 14.8% Traditional Serrated Adenoma, TSA, or Serrated Sessile Lesion, SSL, 16.7% hyperplastic). The lesions was classificated as neoplastic (adenomatous, TSA, SSL) and no neoplastic (hyperplastic lesions). The images were shown to Polydeep and to the expert endoscopists to obtain the predicted histology. The endoscopists classified the lesions as adenoma, TSA, SSL or hyperplastic lesions, while PolyDeep classified the lesions as neoplastic and non-neoplastic. We calculated sensitivity, specificity, predictive values, likelihood ratios, diagnostic odds ratio and the Area Under the Curve (AUC). We compared sensitivity and specificity using the McNemar test and the AUC using the homogeneity areas test.

Results

PolyDeep classified 77.62% of evaluated lesions as neoplastic and the endoscopists classified 87.1% of the lesions as neoplastic (adenomas: 75.76%, TSA: 3.86%, SSL: 7.55%). The diagnostic accuracy results as well as the comparison between PolyDeep and the endoscopists for the neoplastic category is shown in the attached table.

	Endoscopists	PolyDeep	P-value
Sensitivity	91.56%	90.34%	<0.001
Specificity	35.23%	35.31%	0.355
PPV	88.21%	88.72%
NPV	44.07%	39.37%
LR +	1.41	1.39
LR -	0.24	0.27
OR	5.89	5.11
AUC	0.634	0.628	0.91

Table 1: The table show the comparison of diagnostic test between endoscopists and PolyDeep, furthermore the AUC and p-values of McNemar test and the comparison between both ROC curves. Positive Predictive Value (PPV), Negative predictive Value (NPV), positive Likelihood Ratio (LR+), negative Likelihood Ratio (LR-), Odds Ratio (OR), Area Under the Curve (AUC).

Conclusion

Polydeep, a CADe/x system, is as accurate as expert endoscopists for the optical diagnosis of colorectal polyps.

Introduction

Recently, artificial intelligence-based computer-aided detection (CADe) for colonoscopy has garnered attention due to its effectiveness in increasing the adenoma detection rate (ADR). However, theoretically, CADe systems cannot reduce exposure errors for polyps obscured by objects. In this regard, computer-aided quality improvement (CAQ) which expects to induce the meticulous mucosal inspection and leading to a reduction of blind spot area. In this study, we developed and evaluated CAQ for colonoscopy.

Aims & Methods

This was a single-center, retrospective study conducted from April 1, 2022 to October 31, 2023. We randomly extracted videos of the entire colonoscopies and created 18,922 training images. Quadrant images were made from one image and three endoscopists annotated the following scores; i) (overall) quality of image(not evaluable 0 or evaluable 1), ii) appropriate distance between the colon mucosa and the endoscope(Scored on a scale of 0 to 8), iii) the amount of residue in the colon,(Scored on a scale of 0 to 8) and ⅳ) presence of blind area in the field of view (e.g., fold or flexure) (Scored on a scale of 0 to 4). We conducted supervised machine learning using annotated images. The CAQ deployed multi-head deep convolutional neural network model which can output multiclass prediction from one image. Consequently, the CAQ system outputs aforementioned score from colonoscopy video. To validate its performance, we randomly selected videos from the cecum to the hepatic flexure performed by 19 endoscopists and calculated CAQ model scores. We compared CAQ scores between endoscopists with low and high ADR (ADR cutoff is 35%) and with and without the history of detecting early depressed-type colorectal cancer by endoscopists.

Results

62 videos were included as validation samples. Among the 19 endoscopists, 11 had a low ADR and 8 had a high ADR. The results showed a significant difference in the rate of evaluable images between endoscopists with low and high ADR (median 70% vs. 77%, p = 0.003, t-test). There was also a significant difference in the evaluation score of the distance (median 3.8 vs. 4.2, p = 0.003, t-test). A significant difference was also obtained in the score for the amount of residue in the colon(median 5.3 vs. 5.7, p = 0.01, t-test). However, there was no significant difference in the scores evaluating the presence of blind areas(median 1.01 vs. 1.10, p = 0.13, t-test)
Among the 19 endoscopists, 5 endoscopists had a history of detecting early depressed-type colorectal cancer, while 14 did not. The results showed a significant difference was observed in the rate of evaluable images between endoscopists with and without a history of detecting early depressed-type colorectal cancer(median 80% vs. 71%, p < 0.001, t-test) . Additionally, there was a difference in the evaluation scores for the distance (median 4.5 vs. 3.8, p < 0.001, t-test). There was also a difference in the scores assessing the presence or absence of blind spots (median 1.2 vs. 1.0, p = 0.006, t-test). Moreover, a significant difference was found in the evaluation scores for the amount of residue in the colon(median 5.9 vs. 5.3, p < 0.001, t-test) .

Conclusion

The CAQ system developed in this study was able to quantify the skill of endoscopists. Such CAQ is expected to standardize the observation technique of colonoscopy and may lead to elucidation of detection techniques for significant lesions such as early depressed-type colorectal cancer.