Background: Fibro-Scope is an artificial intelligence/neural network system to determine the fibrosis stage in nonalcoholic steatohepatitis (NASH) using 12 parameters of the patient: age, sex, height, weight, waist circumference (WC), platelet count, and the levels of aspartate and alanine aminotransferase, gamma-glutamyltransferase, cholesterol, triglycerides, and type IV collagen 7S. However, measurement of WC is unstable and often missing from patient databases. Herein, we created Fibro-Scope V1.0.1 that has the same detection power as its predecessor, without the need to consider WC.
Methods: To build a new AI diagnostic system available for the global needs, data from 764 patients with NASH and bridging fibrosis (STELLAR-3) or compensated cirrhosis (STELLAR-4) that participated in two phase III trials were added to the Japanese data. Finally, the data of a total of 898 patients in the training and of 300 patients in the validation studies were analyzed, respectively.
Results: The discrimination of F0-2 from F3,4 through Fibro-Scope V1.0.1 was characterized by a 99.8% sensitivity, a 99.6% specificity, a 99.8% positive predictive value, and a 99.6% negative predictive value in a training study with gray zone analysis; similar effectiveness was also revealed in the analysis without a gray zone. In the validation studies with and without gray zone analysis, high sensitivity and specificity were also identified. Fibro-Scope V1.0.1 exerted a diagnostic accuracy for F3,4 advanced fibrosis that was comparable to that of the original Fibro-Scope and delivered high (> 92%) sensitivity and specificity.
Conclusion: Fibro-Scope V1.0.1 can accurately diagnose F3,4 fibrosis without the need of WC.
Keywords: Algorithm; Artificial intelligence; Diagnosis; Liver fibrosis; NASH.
© 2022. Asian Pacific Association for the Study of the Liver.