Artificial Intelligence and Machine Learning in Orthopedic Surgery: A Systematic Review Protocol

Maffulli, Nicola; Rodriguez, Hugo C.; Stone, Ian W.; Nam, Andrew; Song, Albert; Gupta, Manu; Alvarado, Rebecca; Ramon, David; Gupta, Ashim

doi:10.21203/rs.3.rs-46174/v1

Cited by 4 publications

(4 citation statements)

References 14 publications

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“…Given recent advances in ML technology and its potential to transform clinical practice, it is important to understand its applications to vascular surgical conditions. Systematic reviews have been conducted on ML/AI in neurosurgery 23 , plastic surgery 24 , and orthopedic surgery 25 . However, there has been no synthesis or evaluation of ML studies in vascular surgery using standardized tools such as PROBAST and TRIPOD.…”

Section: Introductionmentioning

confidence: 99%

Machine learning in vascular surgery: a systematic review and critical appraisal

Feridooni

Cuen-Ojeda

et al. 2022

npj Digit. Med.

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Machine learning (ML) is a rapidly advancing field with increasing utility in health care. We conducted a systematic review and critical appraisal of ML applications in vascular surgery. MEDLINE, Embase, and Cochrane CENTRAL were searched from inception to March 1, 2021. Study screening, data extraction, and quality assessment were performed by two independent reviewers, with a third author resolving discrepancies. All original studies reporting ML applications in vascular surgery were included. Publication trends, disease conditions, methodologies, and outcomes were summarized. Critical appraisal was conducted using the PROBAST risk-of-bias and TRIPOD reporting adherence tools. We included 212 studies from a pool of 2235 unique articles. ML techniques were used for diagnosis, prognosis, and image segmentation in carotid stenosis, aortic aneurysm/dissection, peripheral artery disease, diabetic foot ulcer, venous disease, and renal artery stenosis. The number of publications on ML in vascular surgery increased from 1 (1991–1996) to 118 (2016–2021). Most studies were retrospective and single center, with no randomized controlled trials. The median area under the receiver operating characteristic curve (AUROC) was 0.88 (range 0.61–1.00), with 79.5% [62/78] studies reporting AUROC ≥ 0.80. Out of 22 studies comparing ML techniques to existing prediction tools, clinicians, or traditional regression models, 20 performed better and 2 performed similarly. Overall, 94.8% (201/212) studies had high risk-of-bias and adherence to reporting standards was poor with a rate of 41.4%. Despite improvements over time, study quality and reporting remain inadequate. Future studies should consider standardized tools such as PROBAST and TRIPOD to improve study quality and clinical applicability.

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Section: Introductionmentioning

confidence: 99%

Machine learning in vascular surgery: a systematic review and critical appraisal

Feridooni

Cuen-Ojeda

et al. 2022

npj Digit. Med.

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“…As AI can quickly process large amounts of patient information, it has incredible potential in diagnosing and classifying patients' diseases [26]. Especially the usefulness of AI is being studied in the trauma prediction, which has a wide range of individual differences in the number and severity of injuries due to the involvement of many external and internal factors [27].…”

Section: Efforts For Ai Deep Learning and High Diagnostic Accuracy Fo...mentioning

confidence: 99%

Artificial intelligence and machine learning on diagnosis and classification of hip fracture: systematic review

Cha

Kim

Park³

et al. 2022

J Orthop Surg Res

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Background In the emergency room, clinicians spend a lot of time and are exposed to mental stress. In addition, fracture classification is important for determining the surgical method and restoring the patient's mobility. Recently, with the help of computers using artificial intelligence (AI) or machine learning (ML), diagnosis and classification of hip fractures can be performed easily and quickly. The purpose of this systematic review is to search for studies that diagnose and classify for hip fracture using AI or ML, organize the results of each study, analyze the usefulness of this technology and its future use value. Methods PubMed Central, OVID Medline, Cochrane Collaboration Library, Web of Science, EMBASE, and AHRQ databases were searched to identify relevant studies published up to June 2022 with English language restriction. The following search terms were used [All Fields] AND (", "[MeSH Terms] OR (""[All Fields] AND "bone"[All Fields]) OR "bone fractures"[All Fields] OR "fracture"[All Fields]). The following information was extracted from the included articles: authors, publication year, study period, type of image, type of fracture, number of patient or used images, fracture classification, reference diagnosis of fracture diagnosis and classification, and augments of each studies. In addition, AI name, CNN architecture type, ROI or important region labeling, data input proportion in training/validation/test, and diagnosis accuracy/AUC, classification accuracy/AUC of each studies were also extracted. Results In 14 finally included studies, the accuracy of diagnosis for hip fracture by AI was 79.3–98%, and the accuracy of fracture diagnosis in AI aided humans was 90.5–97.1. The accuracy of human fracture diagnosis was 77.5–93.5. AUC of fracture diagnosis by AI was 0.905–0.99. The accuracy of fracture classification by AI was 86–98.5 and AUC was 0.873–1.0. The forest plot represented that the mean AI diagnosis accuracy was 0.92, the mean AI diagnosis AUC was 0.969, the mean AI classification accuracy was 0.914, and the mean AI classification AUC was 0.933. Among the included studies, the architecture based on the GoogLeNet architectural model or the DenseNet architectural model was the most common with three each. Among the data input proportions, the study with the lowest training rate was 57%, and the study with the highest training rate was 95%. In 14 studies, 5 studies used Grad-CAM for highlight important regions. Conclusion We expected that our study may be helpful in making judgments about the use of AI in the diagnosis and classification of hip fractures. It is clear that AI is a tool that can help medical staff reduce the time and effort required for hip fracture diagnosis with high accuracy. Further studies are needed to determine what effect this causes in actual clinical situations.

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“…Big data has motivated many researchers and clinicians to apply ML and predict various health outcomes to improve patient treatment and quality of care [ 26 , 27 ]. Proportionally, the role of ML surged in the orthopedic field as well [ 20 , 28 , 29 ]. For example, Ramkumar et al [ 30 ] developed an ML using a naïve Bayesian model to forecast LOS and payments for total hip arthroplasty (THA).…”

Section: Introductionmentioning

confidence: 99%

Predicting Length of Stay using machine learning for total joint replacements performed at a rural community hospital

et al. 2022

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Background Predicting patient’s Length of Stay (LOS) before total joint replacement (TJR) surgery is vital for hospitals to optimally manage costs and resources. Many hospitals including in rural areas use publicly available models such as National Surgical Quality Improvement Program (NSQIP) calculator which, unfortunately, performs suboptimally when predicting LOS for TJR procedures. Objective The objective of this research was to develop a Machine Learning (ML) model to predict LOS for TJR procedures performed at a Perioperative Surgical Home implemented rural community hospital for better accuracy and interpretation than the NSQIP calculator. Methods A total of 158 TJR patients were collected and analyzed from a rural community hospital located in Montana. A random forest (RF) model was used to predict patient’s LOS. For interpretation, permuted feature importance and partial dependence plot methods were used to identify the important variables and their relationship with the LOS. Results The root mean square error for the RF model (0.7) was lower than the NSQIP calculator (1.21). The five most important variables for predicting LOS were BMI, Duke Activity Status-Index, diabetes, patient’s household income, and patient’s age. Conclusion This pilot study is the first of its kind to develop an ML model to predict LOS for TJR procedures that were performed at a small-scale rural community hospital. This pilot study contributes an approach for rural hospitals, making them more independent by developing their own predictions instead of relying on public models.

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Artificial Intelligence and Machine Learning in Orthopedic Surgery: A Systematic Review Protocol

Cited by 4 publications

References 14 publications

Machine learning in vascular surgery: a systematic review and critical appraisal

Machine learning in vascular surgery: a systematic review and critical appraisal

Artificial intelligence and machine learning on diagnosis and classification of hip fracture: systematic review

Predicting Length of Stay using machine learning for total joint replacements performed at a rural community hospital

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