An approach to automated measuring morphological parameters of proximal femora on three-dimensional models.

PURPOSE: Analyses of the morphology of proximal femora are essential for preoperative planning and designing customized femoral stems in total hip arthroplasty as well as intramedullary nailing fixation. Various studies reported measurements and analyses on the general geometry of proximal femora three-dimensionally. However, the modeling and measurements are time-consuming and unfriendly to surgeons. Thus, automated measurement and modeling of the femoral medullary canal are critical to promote the clinical application. METHODS: An approach to automated measuring morphological parameters of proximal femur was proposed, and a software allowing importing femur models and manually locating the related anatomic landmarks was developed in the current study. 3D modeling of the femoral medullary canal was created by the semispherical iterative searching algorithm, and 16 key anatomic parameters of the proximal femur were automatically calculated by the least-squares fitting algorithm. RESULTS: By experimenting on 196 femur STL models, the average execution time of single measurement was 0.88 (SD = 0.13) s, and the intra-class correlation coefficient of 10 anatomic parameters was between 0.880 and 0.996, showing high intra-rater and inter-rater reliability. CONCLUSIONS: The parameters of proximal femur can be easily measured, and the 3D modeling of the femoral medullary canal can be rapidly achieved. The approach will be easily applicable to clinical practice and has the potential to be applied in the design of customized femoral stems.

A review of medical image detection for cancers in digestive system based on artificial intelligence.

Introduction: At present, cancer imaging examination relies mainly on manual reading of doctors, which requests a high standard of doctors' professional skills, clinical experience, and concentration. However, the increasing amount of medical imaging data has brought more and more challenges to radiologists. The detection of digestive system cancer (DSC) based on artificial intelligence (AI) can provide a solution for automatic analysis of medical images and assist doctors to achieve high-precision intelligent diagnosis of cancers. Areas covered: The main goal of this paper is to introduce the main research methods of the AI based detection of DSC, and provide relevant reference for researchers. Meantime, it summarizes the main problems existing in these methods, and provides better guidance for future research. Expert commentary: The automatic classification, recognition, and segmentation of DSC can be better realized through the methods of machine learning and deep learning, which minimize the internal information of images that are difficult for humans to discover. In the diagnosis of DSC, the use of AI to assist imaging surgeons can achieve cancer detection rapidly and effectively and save doctors' diagnosis time. These can lay the foundation for better clinical diagnosis, treatment planning and accurate quantitative evaluation of DSC.