Comprehensive review of deep learning in orthopaedics: Applications, challenges, trustworthiness, and fusion.

Deep learning (DL) in orthopaedics has gained significant attention in recent years. Previous studies have shown that DL can be applied to a wide variety of orthopaedic tasks, including fracture detection, bone tumour diagnosis, implant recognition, and evaluation of osteoarthritis severity. The utilisation of DL is expected to increase, owing to its ability to present accurate diagnoses more efficiently than traditional methods in many scenarios. This reduces the time and cost of diagnosis for patients and orthopaedic surgeons. To our knowledge, no exclusive study has comprehensively reviewed all aspects of DL currently used in orthopaedic practice. This review addresses this knowledge gap using articles from Science Direct, Scopus, IEEE Xplore, and Web of Science between 2017 and 2023. The authors begin with the motivation for using DL in orthopaedics, including its ability to enhance diagnosis and treatment planning. The review then covers various applications of DL in orthopaedics, including fracture detection, detection of supraspinatus tears using MRI, osteoarthritis, prediction of types of arthroplasty implants, bone age assessment, and detection of joint-specific soft tissue disease. We also examine the challenges for implementing DL in orthopaedics, including the scarcity of data to train DL and the lack of interpretability, as well as possible solutions to these common pitfalls. Our work highlights the requirements to achieve trustworthiness in the outcomes generated by DL, including the need for accuracy, explainability, and fairness in the DL models. We pay particular attention to fusion techniques as one of the ways to increase trustworthiness, which have also been used to address the common multimodality in orthopaedics. Finally, we have reviewed the approval requirements set forth by the US Food and Drug Administration to enable the use of DL applications. As such, we aim to have this review function as a guide for researchers to develop a reliable DL application for orthopaedic tasks from scratch for use in the market.

Does forearm referencing using a retroversion guide achieve the targeted retroversion of the humeral component in reverse shoulder arthroplasty?

BACKGROUND: Component positioning affects clinical outcomes of reverse shoulder arthroplasty, which necessitates an implantation technique that is reproducible, consistent, and reliable. This study aims to assess the accuracy and precision of positioning the humeral component in planned retroversion using a forearm referencing guide. METHODS: Computed tomography scans of 54 patients (27 males and 27 females) who underwent primary reverse shoulder arthroplasty for osteoarthritis or cuff tear arthropathy were evaluated. A standardized surgical technique was used to place the humeral stem in 15° of retroversion. Version was assessed intraoperatively visualizing the retroversion guide from above and referencing the forearm axis. Metal subtraction techniques from postoperative computed tomography images allowed for the generation of 3D models of the humerus and for evaluation of the humeral component position. Anatomical humeral plane and implant planes were defined and the retroversion 3D angle between identified planes was recorded for each patient. Accuracy and precision were assessed. A subgroup analysis evaluated differences between male and female patients. RESULTS: The humeral retroversion angle ranged from 0.9° to 22.8°. The majority (81%) of the measurements were less than 15°. Mean retroversion angle (±SD) was 9.9° ± 5.8° (95% CI 8.4°-11.5°) with a mean percent error with respect to 15° of -34% ± 38 (95% CI -23 to -44). In the male subgroup (n = 27, range 3.8°-22.5°), the mean retroversion angle was 11.9° ± 5.4° (95% CI 9.8°-14.1°) with a mean percent error with respect to 15° of -21% ± 36 (95% CI -6 to -35). In the female subgroup (n = 27, range 0.9°-22.8°), mean retroversion angle was 8.0° ± 5.5° (95% CI 5.8°-10.1°) and the mean percent error with respect to 15° was -47% ± 36 (95% CI -32 to -61). The differences between the 2 gender groups were statistically significant (P = .006). CONCLUSION: Referencing the forearm using an extramedullary forearm referencing system to position the humeral stem in a desired retroversion is neither accurate nor precise. There is a nonnegligible tendency to achieve a lower retroversion than planned, and the error is more marked in females.