AIM: To investigate the feasibility of predicting dental implant loss risk with deep learning (DL) based on preoperative cone-beam computed tomography.
Postoperative patients are at risk of life-threatening complications such as hemodynamic decompensation or arrhythmia. Automated detection of patients with such risks via a real-time clinical decision support system may provide opportunities for earl...
OBJECTIVE: To evaluate the potential of deep learning models for categorization of dental caries in bitewing radiographs based on the International Caries Classification and Management System (ICCMS™) radiographic scoring system (RSS).
BACKGROUND: Discharge medical notes written by physicians contain important information about the health condition of patients. Many deep learning algorithms have been successfully applied to extract important information from unstructured medical no...
INTRODUCTION: We aim to apply deep learning to achieve fully automated detection and classification of the Cervical Vertebrae Maturation (CVM) stages. We propose an innovative custom-designed deep Convolutional Neural Network (CNN) with a built-in se...
Stroke is the second leading cause of death globally after ischemic heart disease, also a risk factor of cardioembolic stroke. Thus, we postulate that heartbeats encapsulate vital signals related to stroke. With the rapid advancement of deep neural n...
Predictive models for prognosis of small sample advanced schistosomiasis patients have not been well studied. We aimed to construct prognostic predictive models of small sample advanced schistosomiasis patients using two machine learning algorithms, ...
BMC medical informatics and decision making
Jun 26, 2022
BACKGROUND: A disease severity classification system is widely used to predict the survival of patients admitted to the intensive care unit with different diagnoses. In the present study, conventional severity classification systems were compared wit...
OBJECTIVE: This study aimed to develop and validate a machine learning (ML) model to predict the probability of a vaginal delivery (Partometer) using data iteratively obtained during labor from the electronic health record.
OBJECTIVES: To investigate the feasibility and efficacy of a deep-learning (DL)-based three-dimensional (3D) super-resolution (SR) MRI radiomics model for preoperative T-staging prediction in rectal cancer (RC).