Musculoskeletal trauma and artificial intelligence: current trends and projections

Laur, Olga; Wang, Benjamin

doi:10.1007/s00256-021-03824-6

Cited by 14 publications

(8 citation statements)

References 55 publications

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“…Given that CNN training is repetitive, there is a relationship between the database size and the algorithm’s performance. DL algorithms require less time and computational power to analyze new data than other AI techniques ( 2 ).…”

Section: What Is Artificial Intelligence?mentioning

confidence: 99%

“…In a systematic review with grade III of evidence, Hinterwimmer et al analyzed ML algorithms for outcome prediction in TKA ( 17 ). The studies presented in such a review showed fair to good outcomes (AUC median 0.76/range 0.57–0.98), while heterogeneous prediction models were analyzed: complications ( 6 ), costs ( 4 ), functional result ( 3 ), revision ( 2 ), satisfaction after surgery ( 2 ), surgical procedure ( 1 ), and biomechanical properties ( 1 ) were studied. The modified Coleman Methodology Score median was 65 (range 40–80) points.…”

Section: Virtual Elements Of Artificial Intelligence (Computer Scienc...mentioning

confidence: 99%

“…AI is ubiquitous in today’s society, as personal assistants (Alexa, Siri), viewing recommendation algorithms for video-on-demand platforms (Netflix), image and video processing applications (FaceApp), and self-driving cars (Tesla) ( 1 ). AI has enormous potential for improving health care; within a few years, it is feasible that AI will change the way daily clinical practice is conducted ( 2 ).…”

Section: Introductionmentioning

confidence: 99%

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The current role of the virtual elements of artificial intelligence in total knee arthroplasty

Rodríguez-Merchán

2022

EFORT Open Reviews

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The current applications of the virtual elements of artificial intelligence (AI), machine learning (ML), and deep learning (DL) in total knee arthroplasty (TKA) are diverse. ML can predict the length of stay (LOS) and costs before primary TKA, the risk of transfusion after primary TKA, postoperative dissatisfaction after TKA, the size of TKA components, and poorest outcomes. The prediction of distinct results with ML models applying specific data is already possible; nevertheless, the prediction of more complex results is still imprecise. Remote patient monitoring systems offer the ability to more completely assess the individuals experiencing TKA in terms of mobility and rehabilitation compliance. DL can accurately identify the presence of TKA, distinguish between specific arthroplasty designs, and identify and classify knee osteoarthritis as accurately as an orthopedic surgeon. DL allows for the detection of prosthetic loosening from radiographs. Regarding the architectures associated with DL, artificial neural networks (ANNs) and convolutional neural networks (CNNs), ANNs can predict LOS, inpatient charges, and discharge disposition prior to primary TKA and CNNs allow for differentiation between different implant types with near-perfect accuracy.

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Section: What Is Artificial Intelligence?mentioning

confidence: 99%

Section: Virtual Elements Of Artificial Intelligence (Computer Scienc...mentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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The current role of the virtual elements of artificial intelligence in total knee arthroplasty

Rodríguez-Merchán

2022

EFORT Open Reviews

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“…В последнее время ниша в области ведения больных с травмой активно стала заполняться различными ИИ-решениями. Так, например, с целью более точного описания повреждений на различных видах снимков уже разработаны модели, показывающие превосходство в сравнении с врачами [86]. Помимо этого, имеются две модели, которые позволяют с высокой точностью заподозрить развитие острой травматической коагулопатии [87].…”

Section: системы поддержки принятия клинических решенийunclassified

Polytrauma: Definition of the Problem and Management Strategy (Review)

Prokazyuk

Жанаспаев

Aubakirova

et al. 2022

Obŝaâ reanimatologiâ

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Polytrauma is a highly relevant problem from both scientific and clinical perspectives due to its high mortality rate (>20% in young and middle-aged individuals and >45% in the elderly). The lack of consensus in the definition of polytrauma complicates data collection and comparison of available datasets. In addition, selection of the most appropriate management strategy determining the quality of medical care and magnitude of invested resources can be challenging.Aim of the review. To revisit the current definition of polytrauma and define the perspective directions for the diagnosis and management of patients with polytrauma.Material and methods. Based on the data of 93 selected publications, we studied the mortality trends in the trauma and main causes of lethal outcomes, analyzed the polytrauma severity scales and determined their potential flaws, examined the guidelines for choosing the orthosurgical strategy according to the severity of the patient’s condition.Results. The pattern of mortality trends in trauma directly depends on the adequacy of severity assessment and the quality of medical care. The Berlin definition of polytrauma in combination with a mCGS/PTGS scale most accurately classifies polytrauma into four severity groups. For the «stable» patients, the use of primary definitive osteosynthesis with internal fixation (early total care, or ETC) is the gold standard of treatment. For the «borderline» and «unstable» groups, no definitive unified strategy has been adopted. Meanwhile, in «critical» patients, priority is given to general stabilization followed by delayed major surgery (damage control orthopaedics, or DCO), which increases survival.Conclusion. The use of artificial intelligence and machine learning, which have been employed for more specific goals (predicting mortality and several common complications), seems reasonable for planning the management strategy in the «controversial» groups. The use of a clinical decision support system based on a unified patient registry could improve the quality of care for polytrauma, even by less experienced physicians.

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“…In the “Global estimates for the need of rehabilitation…” study, MSK disorders contributed most to the need for rehabilitation, with lower back pain being the most prevalent condition in more than half of the countries analyzed [ 24 ]. In addition, MSK trauma is among the most common reasons for unscheduled visits to the emergency department [ 25 , 26 ]. Missed or delayed diagnosis can lead to an increase in morbidity and mortality.…”

Section: Introductionmentioning

confidence: 99%

Artificial Intelligence in Orthopedic Radiography Analysis: A Narrative Review

et al. 2022

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Artificial intelligence (AI) in medicine is a rapidly growing field. In orthopedics, the clinical implementations of AI have not yet reached their full potential. Deep learning algorithms have shown promising results in computed radiographs for fracture detection, classification of OA, bone age, as well as automated measurements of the lower extremities. Studies investigating the performance of AI compared to trained human readers often show equal or better results, although human validation is indispensable at the current standards. The objective of this narrative review is to give an overview of AI in medicine and summarize the current applications of AI in orthopedic radiography imaging. Due to the different AI software and study design, it is difficult to find a clear structure in this field. To produce more homogeneous studies, open-source access to AI software codes and a consensus on study design should be aimed for.

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Musculoskeletal trauma and artificial intelligence: current trends and projections

Cited by 14 publications

References 55 publications

The current role of the virtual elements of artificial intelligence in total knee arthroplasty

The current role of the virtual elements of artificial intelligence in total knee arthroplasty

Polytrauma: Definition of the Problem and Management Strategy (Review)

Artificial Intelligence in Orthopedic Radiography Analysis: A Narrative Review

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