Impact of computer-aided characterization for diagnosis of colorectal lesions, including sessile serrated lesions: Multireader, multicase study.

Computer-aided characterization (CADx) may be used to implement optical biopsy strategies into colonoscopy practice; however, its impact on endoscopic diagnosis remains unknown. We aimed to evaluate the additional diagnostic value of CADx when used by endoscopists for assessing colorectal polyps. This was a single-center, multicase, multireader, image-reading study using randomly extracted images of pathologically confirmed polyps resected between July 2021 and January 2022. Approved CADx that could predict two-tier classification (neoplastic or nonneoplastic) by analyzing narrow-band images of the polyps was used to obtain a CADx diagnosis. Participating endoscopists determined if the polyps were neoplastic or not and noted their confidence level using a computer-based, image-reading test. The test was conducted twice with a 4-week interval: the first test was conducted without CADx prediction and the second test with CADx prediction. Diagnostic performances for neoplasms were calculated using the pathological diagnosis as reference and performances with and without CADx prediction were compared. Five hundred polyps were randomly extracted from 385 patients and diagnosed by 14 endoscopists (including seven experts). The sensitivity for neoplasia was significantly improved by referring to CADx (89.4% vs. 95.6%). CADx also had incremental effects on the negative predictive value (69.3% vs. 84.3%), overall accuracy (87.2% vs. 91.8%), and high-confidence diagnosis rate (77.4% vs. 85.8%). However, there was no significant difference in specificity (80.1% vs. 78.9%). Computer-aided characterization has added diagnostic value for differentiating colorectal neoplasms and may improve the high-confidence diagnosis rate.

Kato S ，Kudo SE ，Minegishi Y ，Miyata Y ，Maeda Y ，Kuroki T ，Takashina Y ，Mochizuki K ，Tamura E ，Abe M ，Sato Y ，Sakurai T ，Kouyama Y ，Tanaka K ，Ogawa Y ，Nakamura H ，Ichimasa K ，Ogata N ，Hisayuki T ，Hayashi T ，Wakamura K ，Miyachi H ，Baba T ，Ishida F ，Nemoto T ，Misawa M ，Collaborators ... -  《-》

Real-World Validation of a Computer-Aided Diagnosis System for Prediction of Polyp Histology in Colonoscopy: A Prospective Multicenter Study.

Computer-aided diagnosis (CADx) of polyp histology could support endoscopists in clinical decision-making. However, this has not been validated in a real-world setting. We performed a prospective, multicenter study comparing CADx and endoscopist predictions of polyp histology in real-time colonoscopy. Optical diagnosis based on visual inspection of polyps was made by experienced endoscopists. After this, the automated output from the CADx support tool was recorded. All imaged polyps were resected for histological assessment. Primary outcome was difference in diagnostic performance between CADx and endoscopist prediction of polyp histology. Subgroup analysis was performed for polyp size, bowel preparation, difficulty of location of the polyps, and endoscopist experience. A total of 661 eligible polyps were resected in 320 patients aged ≥40 years between March 2021 and July 2022. CADx had an overall accuracy of 71.6% (95% confidence interval [CI] 68.0-75.0), compared with 75.2% (95% CI 71.7-78.4) for endoscopists ( P = 0.023). The sensitivity of CADx for neoplastic polyps was 61.8% (95% CI 56.9-66.5), compared with 70.3% (95% CI 65.7-74.7) for endoscopists ( P < 0.001). The interobserver agreement between CADx and endoscopist predictions of polyp histology was moderate (83.1% agreement, κ 0.661). When there was concordance between CADx and endoscopist predictions, the accuracy increased to 78.1%. The overall diagnostic accuracy and sensitivity for neoplastic polyps was higher in experienced endoscopists compared with CADx predictions, with moderate interobserver agreement. Concordance in predictions increased this diagnostic accuracy. Further research is required to improve the performance of CADx and to establish its role in clinical practice.

Li JW ，Wu CCH ，Lee JWJ ，Liang R ，Soon GST ，Wang LM ，Koh XH ，Koh CJ ，Chew WD ，Lin KW ，Thian MY ，Matthew R ，Kim G ，Khor CJL ，Fock KM ，Ang TL ，So JBY ，Artificial Intelligence in Gastrointestinal Endoscopy Singapore (AIGES) Study Group ... -  《-》

Computer-aided diagnosis for optical diagnosis of diminutive colorectal polyps including sessile serrated lesions: a real-time comparison with screening endoscopists.

BACKGROUND : We aimed to compare the accuracy of the optical diagnosis of diminutive colorectal polyps, including sessile serrated lesions (SSLs), between a computer-aided diagnosis (CADx) system and endoscopists during real-time colonoscopy. METHODS : We developed the POLyp Artificial Recognition (POLAR) system, which was capable of performing real-time characterization of diminutive colorectal polyps. For pretraining, the Microsoft-COCO dataset with over 300 000 nonpolyp object images was used. For training, eight hospitals prospectively collected 2637 annotated images from 1339 polyps (i. e. publicly available online POLAR database). For clinical validation, POLAR was tested during colonoscopy in patients with a positive fecal immunochemical test (FIT), and compared with the performance of 20 endoscopists from eight hospitals. Endoscopists were blinded to the POLAR output. Primary outcome was the comparison of accuracy of the optical diagnosis of diminutive colorectal polyps between POLAR and endoscopists (neoplastic [adenomas and SSLs] versus non-neoplastic [hyperplastic polyps]). Histopathology served as the reference standard. RESULTS : During clinical validation, 423 diminutive polyps detected in 194 FIT-positive individuals were included for analysis (300 adenomas, 41 SSLs, 82 hyperplastic polyps). POLAR distinguished neoplastic from non-neoplastic lesions with 79 % accuracy, 89 % sensitivity, and 38 % specificity. The endoscopists achieved 83 % accuracy, 92 % sensitivity, and 44 % specificity. The optical diagnosis accuracy between POLAR and endoscopists was not significantly different (P = 0.10). The proportion of polyps in which POLAR was able to provide an optical diagnosis was 98 % (i. e. success rate). CONCLUSIONS : We developed a CADx system that differentiated neoplastic from non-neoplastic diminutive polyps during endoscopy, with an accuracy comparable to that of screening endoscopists and near-perfect success rate.

Houwen BBSL ，Hazewinkel Y ，Giotis I ，Vleugels JLA ，Mostafavi NS ，van Putten P ，Fockens P ，Dekker E ，POLAR Study Group ... -  《-》

Artificial Intelligence Allows Leaving-In-Situ Colorectal Polyps.

Artificial Intelligence (AI) could support cost-saving strategies for colonoscopy because of its accuracy in the optical diagnosis of colorectal polyps. However, AI must meet predefined criteria to be implemented in clinical settings. An approved computer-aided diagnosis (CADx) module for differentiating between adenoma and nonadenoma in unmagnified white-light colonoscopy was used in a consecutive series of colonoscopies. For each polyp, CADx output and subsequent endoscopist diagnosis with advanced imaging were matched against the histology gold standard. The primary outcome was the negative predictive value (NPV) of CADx for adenomatous histology for ≤5-mm rectosigmoid lesions. We also calculated the NPV for AI-assisted endoscopist predictions, and agreement between CADx and histology-based postpolypectomy surveillance intervals according to European and American guidelines. Overall, 544 polyps were removed in 162 patients, of which 295 (54.2%) were ≤5-mm rectosigmoid histologically verified lesions. CADx diagnosis was feasible in 291 of 295 (98.6%), and the NPV for ≤5-mm rectosigmoid lesions was 97.6% (95% CI, 94.1%-99.1%). There were 242 of 295 (82%) lesions that were amenable for a leave-in-situ strategy. Based on CADx output, 212 of 544 (39%) would be amenable to a resect-and-discard strategy, resulting in a 95.6% (95% CI, 90.8%-98.0%) and 95.9% (95% CI, 89.8%-98.4%) agreement between CADx- and histology-based surveillance intervals according to European and American guidelines, respectively. A similar NPV (97.6%; 95% CI, 94.8%-99.1%) for ≤5-mm rectosigmoids was achieved by AI-assisted endoscopists assessing polyps with electronic chromoendoscopy, with a CADx-concordant diagnosis in 97.2% of cases. In this study, CADx without advanced imaging exceeded the benchmarks required for optical diagnosis of colorectal polyps. CADx could help implement cost-saving strategies in colonoscopy by reducing the burden of polypectomy and/or pathology. gov registration number: NCT04884581.

Hassan C ，Balsamo G ，Lorenzetti R ，Zullo A ，Antonelli G ... -  《-》

Computer-aided diagnosis system for optical diagnosis of colorectal polyps under white light imaging.

This study presents a novel computer-aided diagnosis (CADx) designed for optically diagnosing colorectal polyps using white light imaging (WLI).We aimed to evaluate the effectiveness of the CADx and its auxiliary role among endoscopists with different levels of expertise. We collected 2,324 neoplastic and 3,735 nonneoplastic polyp WLI images for model training, and 838 colorectal polyp images from 740 patients for model validation. We compared the diagnostic accuracy of the CADx with that of 15 endoscopists under WLI and narrow band imaging (NBI). The auxiliary benefits of CADx for endoscopists of different experience levels and for identifying different types of colorectal polyps was also evaluated. The CADx demonstrated an optical diagnostic accuracy of 84.49%, showing considerable superiority over all endoscopists, irrespective of whether WLI or NBI was used (P < 0.001). Assistance from the CADx significantly improved the diagnostic accuracy of the endoscopists from 68.84% to 77.49% (P = 0.001), with the most significant impact observed among novice endoscopists. Notably, novices using CADx-assisted WLI outperform junior and expert endoscopists without such assistance. The CADx demonstrated a crucial role in substantially enhancing the precision of optical diagnosis for colorectal polyps under WLI and showed the greatest auxiliary benefits for novice endoscopists.

Cheng Y ，Li L ，Bi Y ，Su S ，Zhang B ，Feng X ，Wang N ，Zhang W ，Yao Y ，Ru N ，Xiang J ，Sun L ，Hu K ，Wen F ，Wang Z ，Bai L ，Wang X ，Wang R ，Lv X ，Wang P ，Meng F ，Xiao W ，Linghu E ，Chai N ... -  《-》