A reference value of PTH was established in a large sample of healthy Chinese subjects according to 25OHD status, gender, and age. A high PTH level correlated significantly with an elevated bone turnover and a reduced BMD.

Most patients with PHPT in China still exhibit classic clinical and biochemical features. Both symptomatic and asymptomatic patients have increased over the last 12 years. Although the proportion of parathyroid carcinoma was small, a noticeable upward tendency was observed at our center.

Osteoporotic vertebral fracture (OVF) is a risk factor for morbidity and mortality in elderly population, and accurate diagnosis is important for improving treatment outcomes. OVF diagnosis suffers from high misdiagnosis and underdiagnosis rates, as well as high workload. Deep learning methods applied to plain radiographs, a simple, fast, and inexpensive examination, might solve this problem. We developed and validated a deep‐learning‐based vertebral fracture diagnostic system using area loss ratio, which assisted a multitasking network to perform skeletal position detection and segmentation and identify and grade vertebral fractures. As the training set and internal validation set, we used 11,397 plain radiographs from six community centers in Shanghai. For the external validation set, 1276 participants were recruited from the outpatient clinic of the Shanghai Sixth People's Hospital (1276 plain radiographs). Radiologists performed all X‐ray images and used the Genant semiquantitative tool for fracture diagnosis and grading as the ground truth data. Accuracy, sensitivity, specificity, positive predictive value, and negative predictive value were used to evaluate diagnostic performance. The AI_OVF_SH system demonstrated high accuracy and computational speed in skeletal position detection and segmentation. In the internal validation set, the accuracy, sensitivity, and specificity with the AI_OVF_SH model were 97.41%, 84.08%, and 97.25%, respectively, for all fractures. The sensitivity and specificity for moderate fractures were 88.55% and 99.74%, respectively, and for severe fractures, they were 92.30% and 99.92%. In the external validation set, the accuracy, sensitivity, and specificity for all fractures were 96.85%, 83.35%, and 94.70%, respectively. For moderate fractures, the sensitivity and specificity were 85.61% and 99.85%, respectively, and 93.46% and 99.92% for severe fractures. Therefore, the AI_OVF_SH system is an efficient tool to assist radiologists and clinicians to improve the diagnosing of vertebral fractures. © 2023 The Authors. Journal of Bone and Mineral Research published by Wiley Periodicals LLC on behalf of American Society for Bone and Mineral Research (ASBMR).

On-machine direct detection of profile errors is vital to improve accuracy and efficiency in profile grinding. However, achieving such detection processes is difficult because of harsh machining conditions. This study presents a novel machine-vision-based processing methodology for the profile grinding of contour surfaces instead of the traditional optical-enlargement-based profile grinding which is manual dependent and low efficient. Grinding errors were efficiently detected online through machine vision. A specific vision system was coordinately designed with the profile grinding system to ensure distortionless measurement of workpiece contour and overcome the interferences of machining environment during profile grinding. A machining error detection principle was proposed based on the online captured workpiece contour image. Real-time error identification and compensation algorithms were developed through the synthetic error measurement. Simulations and experiments were conducted successively. The results indicated that profile errors were considerably reduced and measurement efficiency was improved, validating the effectiveness of the proposed methodology for profile grinding of contour surface. The findings can also provide a reference for the direct measurement of machining errors in other machines.