Early identification of biliary atresia using subspace and the bootstrap methods

Kuniyoshi Hayashi (), Eri Hoshino (), Mitsuyoshi Suzuki (), Kotomi Sakai (), Masayuki Obatake () and Osamu Takahashi ()
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Kuniyoshi Hayashi: St. Luke’s International University
Eri Hoshino: St. Luke’s International University
Mitsuyoshi Suzuki: Juntendo University Faculty of Medicine
Kotomi Sakai: St. Luke’s International University
Masayuki Obatake: Kochi Medical School
Osamu Takahashi: St. Luke’s International University

Advances in Data Analysis and Classification, 2023, vol. 17, issue 1, No 9, 163-179

Abstract: Abstract In clinical medicine, physicians often rely on information derived from medical imaging systems, such as image data for diagnosis. To detect disease early, physicians extract essential information from data manually to distinguish accurately between positive and negative cases of disease. In recent years, deep learning (DL) has been used for this purpose, attracting the attention of prominent researchers because of its excellent performance. Consequently, DL and other artificial intelligence (AI) technologies are expected to develop further through integration with statistical and other approaches. Here, we examine biliary atresia (BA), a rare disease that affects primarily infants. Our study focuses on the identification of BA from image data (stool images of BA patients). Using AI and statistical approaches, we propose a machine learning classifier (model) for accurate diagnosis, efficient classification, and early detection of BA after exposure to limited training data. In an initial study, we used the subspace pattern recognition method for the development of a similar classifier. In this study, we propose the development of a filter based on the subspace method and a statistical approach. The filter enables the classifier to extract essential information from image data and discriminate efficiently between BA and non-BA patients.

Keywords: AUC; Convolution method; CLAFIC; Leave-one-out cross-validation; Pattern recognition; Validation study; 62; 62P10 (search for similar items in EconPapers)
Date: 2023
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DOI: 10.1007/s11634-022-00493-8

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