OBJECTIVE: To evaluate whether a machine-learning algorithm (ie, the "NightSignal" algorithm) can be used for the detection of postoperative complications before symptom onset after cardiothoracic surgery.
Journal of imaging informatics in medicine
Mar 14, 2024
Radiology narrative reports often describe characteristics of a patient's disease, including its location, size, and shape. Motivated by the recent success of multimodal learning, we hypothesized that this descriptive text could guide medical image a...
Journal of imaging informatics in medicine
Mar 14, 2024
Deep learning techniques have recently yielded remarkable results across various fields. However, the quality of these results depends heavily on the quality and quantity of data used during the training phase. One common issue in multi-class and mul...
European journal of internal medicine
Mar 14, 2024
OBJECTIVES: Steatotic liver disease is the most frequent chronic liver disease worldwide. Ultrasonography (US) is commonly employed for the assessment and diagnosis. Few information is available on the possible use of artificial intelligence (AI) to ...
Systematic reviews are vital for guiding practice, research and policy, although they are often slow and labour-intensive. Large language models (LLMs) could speed up and automate systematic reviews, but their performance in such tasks has yet to be ...
Clinical oncology (Royal College of Radiologists (Great Britain))
Mar 14, 2024
AIMS: Interstitial high-dose-rate brachytherapy (HDR-BT) is an effective therapy modality for patients with localized prostate carcinoma. The objectives of the study were to optimise the therapy regime variables using two models: response surface met...
Biochimica et biophysica acta. General subjects
Mar 14, 2024
BACKGROUND: Abiotic stresses pose serious threat to the growth and yield of crop plants. Several studies suggest that in plants, transcription factors (TFs) are important regulators of gene expression, especially when it comes to coping with abiotic ...
BACKGROUND: Chronic kidney disease (CKD) requires accurate prediction of renal replacement therapy (RRT) initiation risk. This study developed deep learning algorithms (DLAs) to predict RRT risk in CKD patients by incorporating medical history and pr...
BACKGROUND: To compare image quality, metal artifacts, and diagnostic confidence of conventional computed tomography (CT) images of unilateral total hip arthroplasty patients (THA) with deep learning-based metal artifact reduction (DL-MAR) to convent...
OBJECTIVE: To develop and validate an artificial intelligence diagnostic model based on fundus images for predicting Carotid Intima-Media Thickness (CIMT) in individuals with Type 2 Diabetes Mellitus (T2DM).
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