AI-based pipelines for the automated recognition of hepatocellular carcinoma and the semantic segmentation of virtually stained liver biopsies

Scodellaro, Riccardo; Inverso, Donato; Panzeri, Davide; Bouzin, Margaux; D’Alfonso, Laura; Collini, Maddalena; Chirico, Giuseppe; Sironi, Laura

doi:10.1016/j.bpj.2021.11.2061

Cited by 1 publication

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“…With the rapid development of artificial intelligence (AI) in the field of medical imaging, AI models based on CT and MR images have been widely applied for automatic segmentation, lesion detection, characterization, risk stratification, treatment response prediction, and automated classification of liver nodules in hepatocellular carcinoma [75]. Riccardo et al [76] proposed a novel AI-based pipeline that utilizes convolutional neural networks to provide virtual Hematoxylin and Eosin-stained images, and employs AI algorithms based on color and texture content to automatically identify regions with different progression features of HCC, such as steatosis, fibrosis, and cirrhosis. Compared to manual segmentation performed by histopathologists, the AI approach achieved an accuracy rate of over 90%.…”

Section: Hepatocellular Carcinomamentioning

confidence: 99%

Advancements in AI Research for Digestive System Diseases

Feng,

Li,

Tan

et al. 2023

JBM

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With the "boom" of AI, researchers have made significant progress in assisting clinical disease diagnosis, prediction, and treatment. This article provides an overview of models built using both traditional machine learning methods and deep learning methods, as well as research progress on robotics in digestive system diseases, aiming to provide references for further studies. An application has been developed by domestic and foreign scholars that allows users to upload images of stool samples, which are then analyzed using big data to provide a score for bowel preparation, thereby improving the quality of bowel preparation. In some gastrointestinal diseases, such as Hp infection, Barrett's esophagus and esophageal cancer, chronic atrophic gastritis and gastric cancer, IBD, etc., artificial intelligence possesses diagnostic capabilities comparable to those of professional endoscopists, and some applications can achieve real-time diagnosis. In the field of liver, gallbladder, and pancreatic diseases, artificial intelligence can assist in preoperative diagnosis using imaging or pathology, and robotic remote operations can be performed during surgery, predicting postoperative risk levels, and more. Different scholars have compared and analyzed various algorithm networks for different diseases to find the best-performing models. On this basis, methods such as the MCA attention mechanism, feature selection, gradient descent, and ensemble models can be introduced to further improve the diagnostic performance of the models. In the future, AI can not only help patients self-manage single or multiple diseases, monitor and manage their own diseases in a standardized and reasonable manner, but also predict and treat digestive system diseases at the genetic level.

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Section: Hepatocellular Carcinomamentioning

confidence: 99%