Jongjun Won, Grace Yoojin Lee, Sungyang Jo, Jihyun Lee, Sangjin Lee, Jae Seung Kim, Changhwan Sung, Jungsu S Oh, Kyum-Yil Kwon, Soo Bin Park, Joonsang Lee, Jieun Yum, Sun Ju Chung, Namkug Kim
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引用次数: 0
Abstract
Accurate diagnosis and precise estimation of disease progression states are crucial for developing effective treatment plans for patients with parkinsonism. Although various deep learning-based computer-aided diagnostic models have demonstrated benefits, they have been relatively underexplored in parkinsonism owing to limited data and lack of external validation. We introduce the hierarchical wavelet diffusion autoencoder (HWDAE), a generative self-supervised model trained with 1,934 dopamine transporter positron emission tomography (DAT PET) images. HWDAE learns relevant disease traits during generative training, prior to supervision with human labels, as evidenced by its ability to synthesize realistic images representing different disease states of Parkinson's disease. The pretrained HWDAE is subsequently adapted for two differential diagnostic tasks and one disease progression estimation task, tested on images from two medical centers. Our training approach introduces a paradigm for deep learning research utilizing PET and expands the potential of DAT PET as a biomarker for Parkinson's disease.
Cell Reports MedicineBiochemistry, Genetics and Molecular Biology-Biochemistry, Genetics and Molecular Biology (all)
CiteScore
15.00
自引率
1.40%
发文量
231
审稿时长
40 days
期刊介绍:
Cell Reports Medicine is an esteemed open-access journal by Cell Press that publishes groundbreaking research in translational and clinical biomedical sciences, influencing human health and medicine.
Our journal ensures wide visibility and accessibility, reaching scientists and clinicians across various medical disciplines. We publish original research that spans from intriguing human biology concepts to all aspects of clinical work. We encourage submissions that introduce innovative ideas, forging new paths in clinical research and practice. We also welcome studies that provide vital information, enhancing our understanding of current standards of care in diagnosis, treatment, and prognosis. This encompasses translational studies, clinical trials (including long-term follow-ups), genomics, biomarker discovery, and technological advancements that contribute to diagnostics, treatment, and healthcare. Additionally, studies based on vertebrate model organisms are within the scope of the journal, as long as they directly relate to human health and disease.