Journal of the European Academy of Dermatology and Venereology : JEADV
Dec 17, 2024
BACKGROUND: Ablative fractional photothermolysis serves as an excellent in vivo model for studying wound healing. The advent of non-invasive imaging devices, such as line-field confocal optical coherence tomography (LC-OCT), enhances this model by en...
BACKGROUND: Establishment of a quantitative, objective evaluation tool for facial palsy has been a challenging issue for clinicians and researchers, and artificial intelligence-driven video analysis can be considered a reasonable solution. The author...
MAIN OBJECTIVES: We aimed at comparing intratumoral and peritumoral deep learning, radiomics, and fusion models in predicting KRAS mutations in rectal cancer using endorectal ultrasound imaging.
International journal of medical informatics
Dec 17, 2024
OBJECTIVE: To evaluate the effectiveness and usability of an AI-assisted tool in providing real-time assistance to counselors during suicide prevention helpline conversations.
OBJECTIVE: Breast cancer is one of the most commonly occurring cancers in women. Thus, early detection and treatment of cancer lead to a better outcome for the patient. Ultrasound (US) imaging plays a crucial role in the early detection of breast can...
European journal of nuclear medicine and molecular imaging
Dec 17, 2024
PURPOSE: Prolonged scanning durations are one of the primary barriers to the widespread clinical adoption of dynamic Positron Emission Tomography (PET). In this paper, we developed a deep learning algorithm that capable of predicting dynamic images f...
Journal of gastroenterology and hepatology
Dec 17, 2024
BACKGROUND: Lugol chromoendoscopy has been shown to increase the sensitivity of detection of esophageal squamous cell carcinoma (ESCC). We aimed to develop a deep learning-based virtual lugol chromoendoscopy (V-LCE) method.
Otolaryngology--head and neck surgery : official journal of American Academy of Otolaryngology-Head and Neck Surgery
Dec 17, 2024
OBJECTIVE: This study aims to develop an interpretable machine learning (ML) predictive model to assess its efficacy in predicting postoperative recurrence in pediatric chronic rhinosinusitis (CRS).
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