Aiding Diagnosis and Classifying of Early Stage Osteonecrosis of the Femoral Head with Convolutional Neural Network Based on Magnetic Resonance Imaging.

Introduction: The Steinberg classification system is commonly used by orthopedic surgeons to stage the severity of patients with osteonecrosis of the femoral head (ONFH), and it includes mild, moderate, and severe grading of each stage based on the area of the femoral head affected. However, clinicians mostly grade approximately by visual assessment or not at all. To accurately distinguish the mild, moderate, or severe grade of early stage ONFH, we propose a convolutional neural network (CNN) based on magnetic resonance imaging (MRI) of the hip joint of patients to accurately grade and aid diagnosis of ONFH.

Inference of Correlations Among Testlet Effects: A Latent Variable Selection Method.

In psychological and educational measurement, a testlet-based test is a common and popular format, especially in some large-scale assessments. In modeling testlet effects, a standard bifactor model, as a common strategy, assumes different testlet effects and the main effect to be fully independently distributed. However, it is difficult to establish perfectly independent clusters as this assumption.

The potential role of HPV oncoproteins in the PD-L1/PD-1 pathway in cervical cancer: new perspectives on cervical cancer immunotherapy.

Cervical cancer (CC) is a common malignant tumour of the female reproductive system that is highly harmful to women's health. The efficacy of traditional surgery, radiotherapy and chemotherapy is limited, especially for recurrent and metastatic CC. With continuous progress in diagnostic and treatment technology, immunotherapy has become a new approach for treating CC and has become a new therapy for recurrent and metastatic CC.

Development of a urine-based metabolomics approach for multi-cancer screening and tumor origin prediction.

Background: Cancer remains a leading cause of mortality worldwide. A non-invasive screening solution was required for early diagnosis of cancer. Multi-cancer early detection (MCED) tests have been considered to address the challenge by simultaneously identifying multiple types of cancer within a single test using minimally invasive blood samples.

Leveraging anatomical constraints with uncertainty for pneumothorax segmentation.

Background: Pneumothorax is a medical emergency caused by the abnormal accumulation of air in the pleural space-the potential space between the lungs and chest wall. On 2D chest radiographs, pneumothorax occurs within the thoracic cavity and outside of the mediastinum, and we refer to this area as "lung + space." While deep learning (DL) has increasingly been utilized to segment pneumothorax lesions in chest radiographs, many existing DL models employ an end-to-end approach.