Machine learning in orthopaedic surgery

Lalehzarian, Simon P.; Gowd, Anirudh K.; Liu, Joseph N.

doi:10.5312/wjo.v12.i9.685

Cited by 47 publications

(28 citation statements)

References 85 publications

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“…Deep learning in foot and ankle has been largely focused on fracture detection using CNN systems. However, machine learning has already demonstrated potential beyond this, as prior work has utilized machine learning for postoperative outcome prediction in shoulder arthroplasty, in addition to use for risk-assessment to predict mortality based on existing patient risk factors, amongst a multitude of other uses in trauma, spine, and oncology largely relating to fracture detection, measurements, and labeling [ 75 ]. Despite being in its infancy, machine learning strategies have already proven to improve patient outcomes in foot and ankle surgery.…”

Section: Discussionmentioning

confidence: 99%

Robotic Technology in Foot and Ankle Surgery: A Comprehensive Review

Stauffer

Kim

Grant

et al. 2023

Sensors

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Recent developments in robotic technologies in the field of orthopaedic surgery have largely been focused on higher volume arthroplasty procedures, with a paucity of attention paid to robotic potential for foot and ankle surgery. The aim of this paper is to summarize past and present developments foot and ankle robotics and describe outcomes associated with these interventions, with specific emphasis on the following topics: translational and preclinical utilization of robotics, deep learning and artificial intelligence modeling in foot and ankle, current applications for robotics in foot and ankle surgery, and therapeutic and orthotic-related utilizations of robotics related to the foot and ankle. Herein, we describe numerous recent robotic advancements across foot and ankle surgery, geared towards optimizing intra-operative performance, improving detection of foot and ankle pathology, understanding ankle kinematics, and rehabilitating post-surgically. Future research should work to incorporate robotics specifically into surgical procedures as other specialties within orthopaedics have done, and to further individualize machinery to patients, with the ultimate goal to improve perioperative and post-operative outcomes.

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Section: Discussionmentioning

confidence: 99%

Robotic Technology in Foot and Ankle Surgery: A Comprehensive Review

Stauffer

Kim

Grant

et al. 2023

Sensors

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“…AI is implemented in machines to perform tasks that require human knowledge in an automated manner ( 59 ). On the other hand, machine learning (ML) is a subset of AI and is defined as mathematical algorithms that enable a machine to make choices independently without any external human influence ( 60 ). Furthermore, deep learning, as a novel approach, is a subset of ML, but there are differences between ML and deep learning in data dependencies, hardware dependencies, feature engineering, problem-solving approach, execution time, and interpretability ( 61 ).…”

Section: Artificial Intelligence Based On Deep Learningmentioning

confidence: 99%

Emerging Applications of Deep Learning in Bone Tumors: Current Advances and Challenges

et al. 2022

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Deep learning is a subfield of state-of-the-art artificial intelligence (AI) technology, and multiple deep learning-based AI models have been applied to musculoskeletal diseases. Deep learning has shown the capability to assist clinical diagnosis and prognosis prediction in a spectrum of musculoskeletal disorders, including fracture detection, cartilage and spinal lesions identification, and osteoarthritis severity assessment. Meanwhile, deep learning has also been extensively explored in diverse tumors such as prostate, breast, and lung cancers. Recently, the application of deep learning emerges in bone tumors. A growing number of deep learning models have demonstrated good performance in detection, segmentation, classification, volume calculation, grading, and assessment of tumor necrosis rate in primary and metastatic bone tumors based on both radiological (such as X-ray, CT, MRI, SPECT) and pathological images, implicating a potential for diagnosis assistance and prognosis prediction of deep learning in bone tumors. In this review, we first summarized the workflows of deep learning methods in medical images and the current applications of deep learning-based AI for diagnosis and prognosis prediction in bone tumors. Moreover, the current challenges in the implementation of the deep learning method and future perspectives in this field were extensively discussed.

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“…In recent years, machine learning (ML) technologies have been broadly applied in the field of medicine [1,2]. With the adoption of these ML technologies, healthcare providers can make more informed decisions based on predicted clinical outcomes such as long-term recovery from stroke, the diagnostic accuracy of various diseases, and the efficiency of treatment [3][4][5]. Several well-known ML algorithms include random forests (RFs), decision trees (DTs), neural networks, linear or logistic regression (LR), and support vector machines (SVMs).…”

Section: Introductionmentioning

confidence: 99%

Using Machine Learning Algorithms to Predict Patient Portal Use Among Emergency Department Patients With Diabetes Mellitus

Zhou

Swoboda²,

et al. 2023

J Clin Med Res

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Background Different machine learning (ML) technologies have been applied in healthcare systems with diverse applications. We aimed to determine the model feasibility and accuracy of predicting patient portal use among diabetic patients by using six different ML algorithms. In addition, we also compared model performance accuracy with the use of only essential variables. Methods This was a single-center retrospective observational study. From March 1, 2019 to February 28, 2020, we included all diabetic patients from the study emergency department (ED). The primary outcome was the status of patient portal use. A total of 18 variables consisting of patient sociodemographic characteristics, ED and clinic information, and patient medical conditions were included to predict patient portal use. Six ML algorithms (logistic regression, random forest (RF), deep forest, decision tree, multilayer perception, and support vector machine) were used for such predictions. During the initial step, ML predictions were performed with all variables. Then, the essential variables were chosen via feature selection. Patient portal use predictions were repeated with only essential variables. The performance accuracies (overall accuracy, sensitivity, specificity, and area under receiver operating characteristic curve (AUC)) of patient portal predictions were compared. Results A total of 77,977 unique patients were placed in our final analysis. Among them, 23.4% (18,223) patients were diabetic mellitus (DM). Patient portal use was found in 26.9% of DM patients. Overall, the accuracy of predicting patient portal use was above 80% among five out of six ML algorithms. The RF outperformed the others when all variables were used for patient portal predictions (accuracy 0.9876, sensitivity 0.9454, specificity 0.9969, and AUC 0.9712). When only eight essential variables were chosen, RF still outperformed the others (accuracy 0.9876, sensitivity 0.9374, specificity 0.9932, and AUC 0.9769). Conclusion It is possible to predict patient portal use outcomes when different ML algorithms are used with fair performance accuracy. However, with similar prediction accuracies, the use of feature selection techniques can improve the interpretability of the model by addressing the most relevant features.

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Machine learning in orthopaedic surgery

Cited by 47 publications

References 85 publications

Robotic Technology in Foot and Ankle Surgery: A Comprehensive Review

Robotic Technology in Foot and Ankle Surgery: A Comprehensive Review

Emerging Applications of Deep Learning in Bone Tumors: Current Advances and Challenges

Using Machine Learning Algorithms to Predict Patient Portal Use Among Emergency Department Patients With Diabetes Mellitus

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