Performance of a novel computer-aided diagnosis system in the characterization of colorectal polyps, and its role in meeting Preservation and Incorporation of Valuable Endoscopic Innovations standards set by the American Society of Gastrointestinal Endoscopy.

Background and aims: There has been an increasing role of artificial intelligence (AI) in the characterization of colorectal polyps. Recently, a novel AI algorithm for the characterization of polyps was developed by NEC Corporation (Japan). The aim of our study is to perform an external validation of this algorithm. Methods: The study was a video-based evaluation of the computer-aided diagnosis (CADx) system. Patients undergoing colonoscopy were recruited to record videos of colonic polyps. The frozen polyp images extracted from these videos were used for real-time histological prediction by the endoscopists and by the CADx system, and the results were compared. Results: A total of 115 polyp images were extracted from 66 patients. Sensitivity, negative predictive value and accuracy for diminutive polyps on white light imaging (WLI) and image-enhanced endoscopy (IEE) when assessed by CADx was 90.9% [95% confidence interval (CI) 77.3-100] and 95.8% [95% CI 87.5-100], 80% [95% CI 44.4-97.5] and 90.9% [95% CI 58.7-99.8], 84.8% [95% CI 72.7-97] and 84.6% [95%CI 71.8-94.9], respectively, compared to 48.1% [95%CI 37.7-59.1] and 72% [95% CI 62.5-81], 37.5% [95% CI 28.8-46.8] and 55% [95% CI 44.7-65.0], 53.7% [95% CI 44.2-63.2] and 66.7% [95% CI 59.7-73.3] when assessed by endoscopists. Concordance between histology and CADx-based post-polypectomy surveillance intervals was 93.02% on WLI and 96% on IEE. Conclusion: AI-based optical diagnosis is promising and has the potential to be better than the performance of general endoscopists. We believe that AI can help make real-time optical diagnoses of polyps meeting the Preservation and Incorporation of Valuable endoscopic Innovations standards set by the American Society of Gastrointestinal Endoscopy.

Automated histological classification of whole-slide images of gastric biopsy specimens.

BACKGROUND: Automated image analysis has been developed currently in the field of surgical pathology. The aim of the present study was to evaluate the classification accuracy of the e-Pathologist image analysis software. METHODS: A total of 3062 gastric biopsy specimens were consecutively obtained and stained. The specimen slides were anonymized and digitized. At least two experienced gastrointestinal pathologists evaluated each slide for pathological diagnosis. We compared the three-tier (positive for carcinoma or suspicion of carcinoma; caution for adenoma or suspicion of a neoplastic lesion; or negative for a neoplastic lesion) or two-tier (negative or non-negative) classification results of human pathologists and of the e-Pathologist. RESULTS: Of 3062 cases, 33.4% showed an abnormal finding. For the three-tier classification, the overall concordance rate was 55.6% (1702/3062). The kappa coefficient was 0.28 (95% CI, 0.26-0.30; fair agreement). For the negative biopsy specimens, the concordance rate was 90.6% (1033/1140), but for the positive biopsy specimens, the concordance rate was less than 50%. For the two-tier classification, the sensitivity, specificity, positive predictive value, and negative predictive value were 89.5% (95% CI, 87.5-91.4%), 50.7% (95% CI, 48.5-52.9%), 47.7% (95% CI, 45.4-49.9%), and 90.6% (95% CI, 88.8-92.2%), respectively. CONCLUSIONS: Although there are limitations and requirements for applying automated histopathological classification of gastric biopsy specimens in the clinical setting, the results of the present study are promising.

Age-related decrease of the retinal vasculature area identified with a novel computer-aided analysis system.

Fundus photographs enable non-invasive analysis of the status of the microcirculation by directly observing the retinal vasculature. Retinal microvascular abnormalities are important clinical markers of hypertension and arteriosclerosis, but retinal microvascular changes can be observed in older individuals without hypertension. In this study, our goal is to elucidate the effects of aging on fundus vessels in the retinal photograph. We analyzed retinal vessels of 161 healthy volunteers (49.5 ± 18.7 years, range 18-87) using in-house computer-aided measurement system to measure areas and diameters of all retinal vessels across the entire area of a retinal photograph. The vessels were segmented according to color, and then their area, size, length and thickness were measured by image processing. We also analyzed the correlation between total blood vessel area, age and mean arterial blood pressure (MABP). The decrease in total blood vessel area was dependent on both age and MABP. Moreover, decrease in blood vessel area was also correlated with age for the normotensive group. Furthermore, the slope of the regression line for retinal vessel area with MABP was significantly higher in participants aged ≤ 60 years than in those aged over 60 years. Changes in retinal vessel area with aging were observed in both arterioles and venules. In conclusion, we found the significant decrease in retinal vessel area that is correlated well with calendar age. Therefore, we need to carefully apply traditional classifications of fundus examination for hypertensive retinopathy in older individuals.