Evidence map›Paper›PMID 36542263›Full record

ArticleHepatology international2023

Fibro-Scope V1.0.1: an artificial intelligence/neural network system for staging of nonalcoholic steatohepatitis.

Kanji Yamaguchi, Toshihide Shima, Yasuhide Mitsumoto, Yuya Seko, Atsushi Umemura, Yoshito Itoh, Atsushi Nakajima, Shuichi Kaneko, Kenichi Harada, Timothy Watkins and 1 more

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Article in Hepatology international, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.

0numbers the graph read from it
0cells of the map it votes in
2citing papers in PubMed
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1 · What the graph read from it

What it found

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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

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3 · Its place in the literature

Who cites it

2 citing papers in PubMed.

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4 · The record

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5 · Who and what money

Authors and funding

11 authors.

Kanji YamaguchiMolecular Gastroenterology and Hepatology, Graduate School of Medical Science, Kyoto Prefectural University of Medicine, Kyoto, Japan.
Toshihide ShimaDepartment of Gastroenterology and Hepatology, Saiseikai Suita Hospital, 1-2 Kawazono-cho Suita, Osaka, 564-0013, Japan.
Yasuhide MitsumotoDepartment of Gastroenterology and Hepatology, Saiseikai Suita Hospital, 1-2 Kawazono-cho Suita, Osaka, 564-0013, Japan.
Yuya SekoMolecular Gastroenterology and Hepatology, Graduate School of Medical Science, Kyoto Prefectural University of Medicine, Kyoto, Japan.
Atsushi UmemuraDepartment of Pharmacology, Kyoto Prefectural University of Medicine, Kyoto, Japan.
Yoshito ItohMolecular Gastroenterology and Hepatology, Graduate School of Medical Science, Kyoto Prefectural University of Medicine, Kyoto, Japan.
Atsushi NakajimaDepartment of Gastroenterology, Yokohama City University Graduate School of Medicine, Yokohama, Japan.
Shuichi KanekoDepartment of Gastroenterology, Graduate School of Medicine, Kanazawa University, Kanazawa, Japan.
Kenichi HaradaDepartment of Human Pathology, Graduate School of Medicine, Kanazawa University, Kanazawa, Japan.
Timothy WatkinsClinical Development, Gilead Sciences, Inc., Foster City, USA.
Takeshi OkanoueDepartment of Gastroenterology and Hepatology, Saiseikai Suita Hospital, 1-2 Kawazono-cho Suita, Osaka, 564-0013, Japan. okanoue@suita.saiseikai.or.jp.

Funding

Japan Agency for Medical Research and Development JP19ae0101075Japan Agency for Medical Research and Development JP20gm0010006
6 · The paper itself

Abstract

backgroundFibro-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.

methodsTo 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.

resultsThe 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.

conclusionFibro-Scope V1.0.1 can accurately diagnose F3,4 fibrosis without the need of WC.

Indexed as

Non-alcoholic Fatty Liver DiseaseArtificial IntelligenceFibrosisHumansLiverLiver CirrhosisNeural Networks, ComputerAlgorithmArtificial intelligenceDiagnosisLiver fibrosisNASH

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