The current standard-of-care (SOC) practice for defining the clinical target volume (CTV) for radiation therapy (RT) in patients with glioblastoma still employs an isotropic 1–2 cm expansion of the T2-hyperintensity lesion, without considering the heterogeneous…
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We developed a rapid scanning optical microscope, termed “BlurryScope”, that leverages continuous image acquisition and deep learning to provide a cost-effective and compact solution for automated inspection and analysis of tissue sections. This device…
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Blepharospasm is a focal dystonia characterized by involuntary eyelid contractions that impair vision and social function. The subtle clinical signs of blepharospasm make early and accurate diagnosis difficult, delaying timely intervention. In this study,…
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For patients with locally advanced cervical cancer (LACC), precise survival prediction models could guide personalized treatment. We developed and validated CerviPro, a deep learning-based multimodal prognostic model, to predict disease-free survival (DFS) in 1018…
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Artificial intelligence (AI) is increasingly applied to clinical trial risk assessment, aiming to improve safety and efficiency. This scoping review analyzed 142 studies published between 2013 and 2024, focusing on safety (n = 55), efficacy (n = 46),…
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Existing prostate cancer monitoring methods, reliant on prostate-specific antigen (PSA) measurements in blood tests often fail to detect tumor growth. We develop a computational framework to reconstruct tumor growth from the PSA integrating physics-based…
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Three-dimensional magnetic resonance vessel wall imaging (3D MR-VWI) is critical for characterizing cerebrovascular pathologies, yet its clinical adoption is hindered by labor-intensive postprocessing. We developed VWI Assistant, a multi-sequence integrated deep learning platform trained…
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Glaucoma is a progressive disease that can lead to permanent vision loss, making progression prediction vital for guiding effective treatment. Deep learning aids progression prediction but may yield unequal outcomes across demographic groups. We…
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This meta-analysis evaluated diagnostic performance of deep learning (DL) algorithms using whole slide images (WSIs) for detecting microsatellite instability-high (MSI-H) in colorectal cancer (CRC). PubMed, Embase, and Web of Science were searched until January…
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Early identification of cerebral small vessel disease related cognitive impairment (CSVD-CI) is crucial for timely clinical intervention. We developed a Transformer-based deep learning model using white matter hyperintensity (WMH) radiomics features from T2-fluid-attenuated inversion…
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