Dataset Information

Artificial Intelligence-Aided Endoscopy and Colorectal Cancer Screening.

ABSTRACT: Colorectal cancer (CRC) is the third most common cancer worldwide, with the highest incidence reported in high-income countries. However, because of the slow progression of neoplastic precursors, along with the opportunity for their endoscopic detection and resection, a well-designed endoscopic screening program is expected to strongly decrease colorectal cancer incidence and mortality. In this regard, quality of colonoscopy has been clearly related with the risk of post-colonoscopy colorectal cancer. Recently, the development of artificial intelligence (AI) applications in the medical field has been growing in interest. Through machine learning processes, and, more recently, deep learning, if a very high numbers of learning samples are available, AI systems may automatically extract specific features from endoscopic images/videos without human intervention, helping the endoscopists in different aspects of their daily practice. The aim of this review is to summarize the current knowledge on AI-aided endoscopy, and to outline its potential role in colorectal cancer prevention.

SUBMITTER: Spadaccini M

PROVIDER: S-EPMC10047293 | biostudies-literature | 2023 Mar

REPOSITORIES: biostudies-literature

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Artificial Intelligence-Aided Endoscopy and Colorectal Cancer Screening.

Spadaccini Marco M Massimi Davide D Mori Yuichi Y Alfarone Ludovico L Fugazza Alessandro A Maselli Roberta R Sharma Prateek P Facciorusso Antonio A Hassan Cesare C Repici Alessandro A

Diagnostics (Basel, Switzerland) 20230314 6

Colorectal cancer (CRC) is the third most common cancer worldwide, with the highest incidence reported in high-income countries. However, because of the slow progression of neoplastic precursors, along with the opportunity for their endoscopic detection and resection, a well-designed endoscopic screening program is expected to strongly decrease colorectal cancer incidence and mortality. In this regard, quality of colonoscopy has been clearly related with the risk of post-colonoscopy colorectal c ...[more]

PMID: 36980409

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Project description:Lung cancer is responsible for more fatalities than any other cancer worldwide, with 1.76 million associated deaths reported in 2018. The key issue in the fight against this disease is the detection and diagnosis of all pulmonary nodules at an early stage. Artificial intelligence (AI) algorithms play a vital role in the automated detection, segmentation, and computer-aided diagnosis of malignant lesions. Among the existing algorithms, radiomics and deep-learning-based types appear to show the most promise. Radiomics is a growing field related to the extraction of a set of features from an image, which allows for automated classification of medical images into a predefined group. The process comprises a series of consecutive steps including image acquisition and pre-processing, segmentation of the desired region of interest, calculation of defined features, feature engineering, and construction of the classification model. The features calculated in this process are mainly shape features, as well as first- and higher-order texture features. To date, more than 100 features have been defined, although this number varies depending on the application. The greatest challenge in radiomics is building a cross-validated model based on a selected set of calculated features known as the radiomic signature. Numerous radiomic signatures have successfully been developed; however, reproducibility and clinical validity of the results obtained constitutes a considerable challenge of modern radiomics. Deep learning algorithms are another rapidly evolving technique and are recognized as a valuable tool in the field of medical image analysis for the detection, characterization, and assessment of lesions. Such an approach involves the design of artificial neural network architecture while upholding the goal of high classification accuracy. This paper illuminates the evolution and current state of artificial intelligence methods in lung imaging and the detection and diagnosis of pulmonary nodules, with a particular emphasis on radiomics and deep learning methods.

Project description:Some research has already reported the diagnostic value of artificial intelligence (AI) in different endoscopy outcomes. However, the evidence is confusing and of varying quality. This review aimed to comprehensively evaluate the credibility of the evidence of AI's diagnostic accuracy in endoscopy. Before the study began, the protocol was registered on PROSPERO (CRD42023483073). First, 2 researchers searched PubMed, Web of Science, Embase, and Cochrane Library using comprehensive search terms. Then, researchers screened the articles and extracted information. We used A Measurement Tool to Assess Systematic Reviews 2 (AMSTAR2) to evaluate the quality of the articles. When there were multiple studies aiming at the same result, we chose the study with higher-quality evaluations for further analysis. To ensure the reliability of the conclusions, we recalculated each outcome. Finally, the Grading of Recommendations, Assessment, Development, and Evaluation (GRADE) was used to evaluate the credibility of the outcomes. A total of 21 studies were included for analysis. Through AMSTAR2, it was found that 8 research methodologies were of moderate quality, while other studies were regarded as having low or critically low quality. The sensitivity and specificity of 17 different outcomes were analyzed. There were 4 studies on esophagus, 4 studies on stomach, and 4 studies on colorectal regions. Two studies were associated with capsule endoscopy, two were related to laryngoscopy, and one was related to ultrasonic endoscopy. In terms of sensitivity, gastroesophageal reflux disease had the highest accuracy rate, reaching 97%, while the invasion depth of colon neoplasia, with 71%, had the lowest accuracy rate. On the other hand, the specificity of colorectal cancer was the highest, reaching 98%, while the gastrointestinal stromal tumor, with only 80%, had the lowest specificity. The GRADE evaluation suggested that the reliability of most outcomes was low or very low. AI proved valuabe in endoscopic diagnoses, especially in esophageal and colorectal diseases. These findings provide a theoretical basis for developing and evaluating AI-assisted systems, which are aimed at assisting endoscopists in carrying out examinations, leading to improved patient health outcomes. However, further high-quality research is needed in the future to fully validate AI's effectiveness.

Dataset Information

Artificial Intelligence-Aided Endoscopy and Colorectal Cancer Screening.

Publications

Artificial Intelligence-Aided Endoscopy and Colorectal Cancer Screening.

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