Deep Learning–Based Automated Imaging Classification of ADPKD
Abstrak
Introduction: The Mayo imaging classification model (MICM) requires a prestep qualitative assessment to determine whether a patient is in class 1 (typical) or class 2 (atypical), where patients assigned to class 2 are excluded from the MICM application. Methods: We developed a deep learning–based method to automatically classify class 1 and 2 from magnetic resonance (MR) images and provide classification confidence utilizing abdominal T2-weighted MR images from 486 subjects, where transfer learning was applied. In addition, the explainable artificial intelligence (XAI) method was illustrated to enhance the explainability of the automated classification results. For performance evaluations, confusion matrices were generated, and receiver operating characteristic curves were drawn to measure the area under the curve. Results: The proposed method showed excellent performance for the classification of class 1 (97.7%) and 2 (100%), where the combined test accuracy was 98.01%. The precision and recall for predicting class 1 were 1.00 and 0.98, respectively, with F1-score of 0.99; whereas those for predicting class 2 were 0.87 and 1.00, respectively, with F1-score of 0.93. The weighted averages of precision and recall were 0.98 and 0.98, respectively, showing the classification confidence scores whereas the XAI method well-highlighted contributing regions for the classification. Conclusion: The proposed automated method can classify class 1 and 2 cases as accurately as the level of a human expert. This method may be a useful tool to facilitate clinical trials investigating different types of kidney morphology and for clinical management of patients with autosomal dominant polycystic kidney disease (ADPKD).
Topik & Kata Kunci
Penulis (53)
Youngwoo Kim
Seonah Bu
Cheng Tao
Kyongtae T. Bae
Theodore Steinman
Jesse Wei
Peter Czarnecki
Ivan Pedrosa
William Braun
Saul Nurko
Erick Remer
Arlene Chapman
Diego Martin
Frederic Rahbari-Oskoui
Pardeep Mittal
Vicente Torres
Marie C. Hogan
Ziad El-Zoghby
Peter Harris
James Glockner
Bernard King, Jr.
Ronald Perrone
Neil Halin
Dana Miskulin
Robert Schrier
Godela Brosnahan
Berenice Gitomer
Cass Kelleher
Amirali Masoumi
Nayana Patel
Franz Winklhofer
Jared Grantham
Alan Yu
Connie Wang
Louis Wetzel
Charity G. Moore
James E. Bost
Kyongtae Bae
Kaleab Z. Abebe
J. Philip Miller
Paul A. Thompson
Josephine Briggs
Michael Flessner
Catherine M. Meyers
Robert Star
James Shayman
William Henrich
Tom Greene
Mary Leonard
Peter McCullough
Sharon Moe
Michael Rocco
David Wendler
Akses Cepat
- Tahun Terbit
- 2024
- Sumber Database
- DOAJ
- DOI
- 10.1016/j.ekir.2024.04.002
- Akses
- Open Access ✓