European heart journal. Imaging methods and practice · 2024
Deep learning model to diagnose cardiac amyloidosis from haematoxylin/eosin-stained myocardial tissue.
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Tohyama Takeshi, Iwasaki Takeshi, Ikeda Masataka, Katsuki Masato, Watanabe Tatsuya, Misumi Kayo, Shinohara Keisuke, Fujino Takeo, Hashimoto Toru, Matsushima Shouji, Ide Tomomi, Kishimoto Junji, Todaka Koji, Oda Yoshinao, Abe Kohtaro
Аннотация
AIMS: Amyloid deposition in myocardial tissue is a definitive feature for diagnosing cardiac amyloidosis, though less invasive imaging modalities such as bone tracer cardiac scintigraphy and cardiac magnetic resonance imaging have been established as first steps for its diagnosis. This study aimed to develop a deep learning model to support the diagnosis of cardiac amyloidosis from haematoxylin/eosin (HE)-stained myocardial tissue.
METHODS AND RESULTS: This single-centre retrospective observational study enrolled 166 patients who underwent myocardial biopsies between 2008 and 2022, including 76 patients diagnosed with cardiac amyloidosis and 90 with other diagnoses. A deep learning model was developed to output the probabilities of cardiac amyloidosis for all the small patches cutout from each myocardial specimen. The developed model highlighted the area in the stained images as highly suspicious, corresponding to where Dylon staining marked amyloid deposition, and discriminated the patches in the evaluation dataset with an area under the curve of 0.965. Provided that the diagnostic criterion for cardiac amyloidosis was defined as a median probability of cardiac amyloidosis >50% in all patches, the model achieved perfect performance in discriminating patients with cardiac amyloidosis from those without it, with an area under the curve of 1.0.
CONCLUSION: A deep learning model was developed to diagnose cardiac amyloidosis from HE-stained myocardial tissue accurately. Although further prospective validation of this model using HE-stained myocardial tissues from multiple centres is needed, it may help minimize the risk of missing cardiac amyloidosis and maximize the utility of histological diagnosis in clinical practice.
METHODS AND RESULTS: This single-centre retrospective observational study enrolled 166 patients who underwent myocardial biopsies between 2008 and 2022, including 76 patients diagnosed with cardiac amyloidosis and 90 with other diagnoses. A deep learning model was developed to output the probabilities of cardiac amyloidosis for all the small patches cutout from each myocardial specimen. The developed model highlighted the area in the stained images as highly suspicious, corresponding to where Dylon staining marked amyloid deposition, and discriminated the patches in the evaluation dataset with an area under the curve of 0.965. Provided that the diagnostic criterion for cardiac amyloidosis was defined as a median probability of cardiac amyloidosis >50% in all patches, the model achieved perfect performance in discriminating patients with cardiac amyloidosis from those without it, with an area under the curve of 1.0.
CONCLUSION: A deep learning model was developed to diagnose cardiac amyloidosis from HE-stained myocardial tissue accurately. Although further prospective validation of this model using HE-stained myocardial tissues from multiple centres is needed, it may help minimize the risk of missing cardiac amyloidosis and maximize the utility of histological diagnosis in clinical practice.