Fatty liver classification via risk controlled neural networks trained on grouped ultrasound image data

• Published in: Scientific reports Vol. 14; no. 1; pp. 7345 - 13
• Main Authors: Yen, Tso-Jung, Yang, Chih-Ting, Lee, Yi-Ju, Chen, Chun-houh, Yang, Hsin-Chou
• Format: Journal Article
• Language: English
• Published: London Nature Publishing Group UK 28.03.2024; Nature Publishing Group; Nature Portfolio
• Subjects: 639/705; 639/705/1046; 639/705/531; 692/700; 692/700/1421; 692/700/1421/1860; Biobanks; Biopsy; Classification; Datasets; Diagnosis; Disease; Embedding; Experiments; Fatty liver; Fatty Liver - diagnostic imaging; Humanities and Social Sciences; Humans; Learning algorithms; Liver; Machine Learning; multidisciplinary; Neural networks; Neural Networks, Computer; Patients; Review boards; Risk management; Science; Science (multidisciplinary); Statistical analysis; Taiwan; Ultrasonic imaging; Ultrasonography - methods; Ultrasound; Taiwan
• ISSN: 2045-2322; 2045-2322
• DOI: 10.1038/s41598-024-57386-3

Ultrasound imaging is a widely used technique for fatty liver diagnosis as it is practically affordable and can be quickly deployed by using suitable devices. When it is applied to a patient, multiple images of the targeted tissues are produced. We propose a machine learning model for fatty liver diagnosis from multiple ultrasound images. The machine learning model extracts features of the ultrasound images by using a pre-trained image encoder. It further produces a summary embedding on these features by using a graph neural network. The summary embedding is used as input for a classifier on fatty liver diagnosis. We train the machine learning model on a ultrasound image dataset collected by Taiwan Biobank. We also carry out risk control on the machine learning model using conformal prediction. Under the risk control procedure, the classifier can improve the results with high probabilistic guarantees.