Robust automated prediction of the revised Vienna Classification in colonoscopy using deep learning: development and initial external validation

Externally validated and clinically useful machine learning algorithms to support patient-related decision-making in oncology: a scoping review

BACKGROUND

This scoping review systematically maps externally validated machine learning (ML)-based models in cancer patient care, quantifying their performance, and clinical utility, and examining relationships between models, cancer types, and clinical decisions. By synthesizing evidence, this study identifies, strengths, limitations, and areas requiring further research.

METHODS

The review followed the Joanna Briggs Institute's methodology, Preferred Reporting Items for Systematic Reviews and Meta-Analyses extension for Scoping Reviews guidelines, and the Population, Concept, and Context mnemonic. Searches were conducted across Embase, IEEE Xplore, PubMed, Scopus, and Web of Science (January 2014-September 2022), targeting English-language quantitative studies in Q1 journals (SciMago Journal and Country Ranking > 1) that used ML to evaluate clinical outcomes for human cancer patients with commonly available data. Eligible models required external validation, clinical utility assessment, and performance metric reporting. Studies involving genetics, synthetic patients, plants, or animals were excluded. Results were presented in tabular, graphical, and descriptive form.

RESULTS

From 4023 deduplicated abstracts and 636 full-text reviews, 56 studies (2018-2022) met the inclusion criteria, covering diverse cancer types and applications. Convolutional neural networks were most prevalent, demonstrating high performance, followed by gradient- and decision tree-based algorithms. Other algorithms, though underrepresented, showed promise. Lung and digestive system cancers were most frequently studied, focusing on diagnosis and outcome predictions. Most studies were retrospective and multi-institutional, primarily using image-based data, followed by text-based and hybrid approaches. Clinical utility assessments involved 499 clinicians and 12 tools, indicating improved clinician performance with AI assistance and superior performance to standard clinical systems.

DISCUSSION

Interest in ML-based clinical decision-making has grown in recent years alongside increased multi-institutional collaboration. However, small sample sizes likely impacted data quality and generalizability. Persistent challenges include limited international validation across ethnicities, inconsistent data sharing, disparities in validation metrics, and insufficient calibration reporting, hindering model comparison reliability.

CONCLUSION

Successful integration of ML in oncology decision-making requires standardized data and methodologies, larger sample sizes, greater transparency, and robust validation and clinical utility assessments.

OTHER

Financed by FCT-Fundação para a Ciência e a Tecnologia (Portugal, project LA/P/0063/2020, grant 2021.09040.BD) as part of CSS's Ph.D. This work was not registered.

Current status and future direction of cancer research using artificial intelligence for clinical application

The expectations for artificial intelligence (AI) technology have increased considerably in recent years, mainly due to the emergence of deep learning. At present, AI technology is being used for various purposes and has brought about change in society. In particular, the rapid development of generative AI technology, exemplified by ChatGPT, has amplified the societal impact of AI. The medical field is no exception, with a wide range of AI technologies being introduced for basic and applied research. Further, AI-equipped software as a medical device (AI-SaMD) is also being approved by regulatory bodies. Combined with the advent of big data, data-driven research utilizing AI is actively pursued. Nevertheless, while AI technology has great potential, it also presents many challenges that require careful consideration. In this review, we introduce the current status of AI-based cancer research, especially from the perspective of clinical application, and discuss the associated challenges and future directions, with the aim of helping to promote cancer research that utilizes effective AI technology.

Kruskal Szekeres generative adversarial network augmented deep autoencoder for colorectal cancer detection

Cancer involves abnormal cell growth, with types like intestinal and oesophageal cancer often diagnosed in advanced stages, making them hard to cure. Symptoms are like burning sensations in the stomach and swallowing difficulties are specified as colorectal cancer. Deep learning significantly impacts the medical image processing and diagnosis, offering potential improvements in accuracy and efficiency. The Kruskal Szekeres Generative Adversarial Network Augmented Deep Autoencoder (KSGANA-DA) is introduced for early colorectal cancer detection and it comprises two stages; Initial stage, data augmentation uses Affine Transform via Random Horizontal Rotation and Geometric Transform via Kruskal-Szekeres that coordinates to improve the training dataset diversity, boosting detection performance. The second stage, a Deep Autoencoder Anatomical Landmark-based Image Segmentation preserves edge pixel spatial locations, improving precision and recall for early boundary detection. Experiments validate KSGANA-DA performance and different existing methods are implemented into Python. The results of KSGANA-DA are to provide higher precision by 41%, recall by 7%, and lesser training time by 46% than compared to conventional methods.

Artificial intelligence in colonoscopy: from detection to diagnosis

This study reviews the recent progress of artificial intelligence for colonoscopy from detection to diagnosis. The source of data was 27 original studies in PubMed. The search terms were "colonoscopy" (title) and "deep learning" (abstract). The eligibility criteria were: (1) the dependent variable of gastrointestinal disease; (2) the interventions of deep learning for classification, detection and/or segmentation for colonoscopy; (3) the outcomes of accuracy, sensitivity, specificity, area under the curve (AUC), precision, F1, intersection of union (IOU), Dice and/or inference frames per second (FPS); (3) the publication year of 2021 or later; (4) the publication language of English. Based on the results of this study, different deep learning methods would be appropriate for different tasks for colonoscopy, e.g., Efficientnet with neural architecture search (AUC 99.8%) in the case of classification, You Only Look Once with the instance tracking head (F1 96.3%) in the case of detection, and Unet with dense-dilation-residual blocks (Dice 97.3%) in the case of segmentation. Their performance measures reported varied within 74.0-95.0% for accuracy, 60.0-93.0% for sensitivity, 60.0-100.0% for specificity, 71.0-99.8% for the AUC, 70.1-93.3% for precision, 81.0-96.3% for F1, 57.2-89.5% for the IOU, 75.1-97.3% for Dice and 66-182 for FPS. In conclusion, artificial intelligence provides an effective, non-invasive decision support system for colonoscopy from detection to diagnosis.

Deep learning-based lesion detection and severity grading of small-bowel Crohn's disease ulcers on double-balloon endoscopy images

BACKGROUND AND AIMS

Double-balloon endoscopy (DBE) is widely used in diagnosing small-bowel Crohn's disease (CD). However, CD misdiagnosis frequently occurs if inexperienced endoscopists cannot accurately detect the lesions. The CD evaluation may also be inaccurate owing to the subjectivity of endoscopists. This study aimed to use artificial intelligence (AI) to accurately detect and objectively assess small-bowel CD for more refined disease management.

METHODS

We collected 28,155 small-bowel DBE images from 628 patients from January 2018 to December 2022. Four expert gastroenterologists labeled the images, and at least 2 endoscopists made the final decision with agreement. A state-of-the-art deep learning model, EfficientNet-b5, was trained to detect CD lesions and evaluate CD ulcers. The detection included lesions of ulcer, noninflammatory stenosis, and inflammatory stenosis. Ulcer grading included ulcerated surface, ulcer size, and ulcer depth. A comparison of AI model performance with endoscopists was performed.

RESULTS

The EfficientNet-b5 achieved high accuracies of 96.3% (95% confidence interval [CI], 95.7%-96.7%), 95.7% (95% CI, 95.1%-96.2%), and 96.7% (95% CI, 96.2%-97.2%) for the detection of ulcers, noninflammatory stenosis, and inflammatory stenosis, respectively. In ulcer grading, the EfficientNet-b5 exhibited average accuracies of 87.3% (95% CI, 84.6%-89.6%) for grading the ulcerated surface, 87.8% (95% CI, 85.0%-90.2%) for grading the size of ulcers, and 85.2% (95% CI, 83.2%-87.0%) for ulcer depth assessment.

CONCLUSIONS

The EfficientNet-b5 achieved high accuracy in detecting CD lesions and grading CD ulcers. The AI model can provide expert-level accuracy and objective evaluation of small-bowel CD to optimize the clinical treatment plans.

First report from the International Evaluation of Endoscopic classification Japan NBI Expert Team: International multicenter web trial

OBJECTIVES

Narrow-band imaging (NBI) contributes to real-time optical diagnosis and classification of colorectal lesions. The Japan NBI Expert Team (JNET) was introduced in 2011. The aim of this study was to explore the diagnostic accuracy of JNET when applied by European and Japanese endoscopists not familiar with this classification.

METHODS

This study was conducted by 36 European Society of Gastrointestinal Endoscopy (ESGE) and 49 Japan Gastroenterological Endoscopy Society (JGES) non-JNET endoscopists using still images of 150 lesions. For each lesion, nonmagnified white-light, nonmagnified NBI, and magnified NBI images were presented. In the magnified NBI, the evaluation area was designated by region of interest (ROI). The endoscopists scored histological prediction for each lesion.

RESULTS

In ESGE members, the sensitivity, specificity, and accuracy were respectively 73.3%, 94.7%, and 93.0% for JNET Type 1; 53.0%, 64.9%, and 62.1% for Type 2A; 43.9%, 67.7%, and 55.1% for Type 2B; and 38.1%, 93.7%, and 85.1% for Type 3. When Type 2B and 3 were considered as one category of cancer, the sensitivity, specificity, and accuracy for differentiating high-grade dysplasia and cancer from the others were 59.9%, 72.5%, and 63.8%, respectively. These trends were the same for JGES endoscopists.

CONCLUSION

The diagnostic accuracy of the JNET classification was similar between ESGE and JGES and considered to be sufficient for JNET Type 1. On the other hand, the accuracy for Types 2 and 3 is not sufficient; however, JNET 2B lesions should be resected en bloc due to the risk of cancers and JNET 3 can be treated by surgery due to its high specificity.

Cost-effectiveness analysis of computer-aided detection systems for colonoscopy in Japan

OBJECTIVES

The usefulness of computer-aided detection systems (CADe) for colonoscopy has been increasingly reported. In many countries, however, data on the cost-effectiveness of their use are lacking; consequently, CADe for colonoscopy has not been covered by health insurance. We aimed to evaluate the cost-effectiveness of colonoscopy using CADe in Japan.

METHODS

We conducted a simulation model analysis using Japanese data to examine the cost-effectiveness of colonoscopy with and without CADe for a population aged 40-74 years who received colorectal cancer (CRC) screening with a fecal immunochemical test (FIT). The rates of receiving FIT screening and colonoscopy following a positive FIT were set as 40% and 70%, respectively. The sensitivities of FIT for advanced adenomas and CRC Dukes' A-D were 26.5% and 52.8-78.3%, respectively. CADe colonoscopy was judged to be cost-effective when its incremental cost-effectiveness ratio (ICER) was below JPY 5,000,000 per quality-adjusted life-years (QALYs) gained.

RESULTS

Compared to conventional colonoscopy, CADe colonoscopy showed a higher QALY (20.4098 vs. 20.4088) and lower CRC incidence (2373 vs. 2415 per 100,000) and mortality (561 vs. 569 per 100,000). When the CADe cost was set at JPY 1000-6000, the ICER per QALY gained for CADe colonoscopy was lower than JPY 5,000,000 (JPY 796,328-4,971,274). The CADe cost threshold at which the ICER for CADe colonoscopy exceeded JPY 5,000,000 was JPY 6040.

CONCLUSIONS

Computer-aided detection systems for colonoscopy has the potential to be cost-effective when the CADe cost is up to JPY 6000. These results suggest that the insurance reimbursement of CADe for colonoscopy is reasonable.

The Role of Artificial Intelligence in Colorectal Cancer Screening: Lesion Detection and Lesion Characterization

Colorectal cancer remains a leading cause of cancer-related morbidity and mortality worldwide, despite the widespread uptake of population surveillance strategies. This is in part due to the persistent development of 'interval colorectal cancers', where patients develop colorectal cancer despite appropriate surveillance intervals, implying pre-malignant polyps were not resected at a prior colonoscopy. Multiple techniques have been developed to improve the sensitivity and accuracy of lesion detection and characterisation in an effort to improve the efficacy of colorectal cancer screening, thereby reducing the incidence of interval colorectal cancers. This article presents a comprehensive review of the transformative role of artificial intelligence (AI), which has recently emerged as one such solution for improving the quality of screening and surveillance colonoscopy. Firstly, AI-driven algorithms demonstrate remarkable potential in addressing the challenge of overlooked polyps, particularly polyp subtypes infamous for escaping human detection because of their inconspicuous appearance. Secondly, AI empowers gastroenterologists without exhaustive training in advanced mucosal imaging to characterise polyps with accuracy similar to that of expert interventionalists, reducing the dependence on pathologic evaluation and guiding appropriate resection techniques or referrals for more complex resections. AI in colonoscopy holds the potential to advance the detection and characterisation of polyps, addressing current limitations and improving patient outcomes. The integration of AI technologies into routine colonoscopy represents a promising step towards more effective colorectal cancer screening and prevention.

Analysis of super-enhancer using machine learning and its application to medical biology

The analysis of super-enhancers (SEs) has recently attracted attention in elucidating the molecular mechanisms of cancer and other diseases. SEs are genomic structures that strongly induce gene expression and have been reported to contribute to the overexpression of oncogenes. Because the analysis of SEs and integrated analysis with other data are performed using large amounts of genome-wide data, artificial intelligence technology, with machine learning at its core, has recently begun to be utilized. In promoting precision medicine, it is important to consider information from SEs in addition to genomic data; therefore, machine learning technology is expected to be introduced appropriately in terms of building a robust analysis platform with a high generalization performance. In this review, we explain the history and principles of SE, and the results of SE analysis using state-of-the-art machine learning and integrated analysis with other data are presented to provide a comprehensive understanding of the current status of SE analysis in the field of medical biology. Additionally, we compared the accuracy between existing machine learning methods on the benchmark dataset and attempted to explore the kind of data preprocessing and integration work needed to make the existing algorithms work on the benchmark dataset. Furthermore, we discuss the issues and future directions of current SE analysis.