AI MSK clinical applications: orthopedic implants

Yi, Paul H.; Mutasa, Simukayi; Fritz, Jan

doi:10.1007/s00256-021-03879-5

Cited by 12 publications

(11 citation statements)

References 39 publications

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“…These results demonstrate that AI-based machine learning classifiers might be useful supportive diagnostic tools in challenging conditions like the differentiation of septic and aseptic THA failure. Indeed, AI tools applied to imaging after arthroplasty may improve reporting activity and decrease the mistakes rate by reducing cognitive load and fatigue for radiologists [15]. Unfortunately, a comparison with previously published data is not possible, since, to our knowledge, no other studies investigated the diagnostic performance of an AI-based machine learning classifier built upon MRI features in this setting.…”

Section: Discussionmentioning

confidence: 99%

“…Among them, characterization of prosthesis, identification of specific implant models, and assessment of prosthetic positioning and complications. Most works have been published about the use of AI-based analysis of radiographic images to automate postoperative evaluations of joint arthroplasty [14,15]. For instance, AI-based algorithms reported good accuracy in predicting the dislocation risk of THA (AUC 76.67) or in detecting THA loosening (accuracy 88.3%) [14].…”

Section: Introductionmentioning

confidence: 99%

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MRI-based artificial intelligence to predict infection following total hip arthroplasty failure

et al. 2023

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Purpose To investigate whether artificial intelligence (AI) can differentiate septic from non-septic total hip arthroplasty (THA) failure based on preoperative MRI features. Materials and methods We included 173 patients (98 females, age: 67 ± 12 years) subjected to first-time THA revision surgery after preoperative pelvis MRI. We divided the patients into a training/validation/internal testing cohort (n = 117) and a temporally independent external-testing cohort (n = 56). MRI features were used to train, validate and test a machine learning algorithm based on support vector machine (SVM) to predict THA infection on the training-internal validation cohort with a nested fivefold validation approach. Machine learning performance was evaluated on independent data from the external-testing cohort. Results MRI features were significantly more frequently observed in THA infection (P < 0.001), except bone destruction, periarticular soft-tissue mass, and fibrous membrane (P > 0.005). Considering all MRI features in the training/validation/internal-testing cohort, SVM classifier reached 92% sensitivity, 62% specificity, 79% PPV, 83% NPV, 82% accuracy, and 81% AUC in predicting THA infection, with bone edema, extracapsular edema, and synovitis having been the best predictors. After being tested on the external-testing cohort, the classifier showed 92% sensitivity, 79% specificity, 89% PPV, 83% NPV, 88% accuracy, and 89% AUC in predicting THA infection. SVM classifier showed 81% sensitivity, 76% specificity, 66% PPV, 88% NPV, 80% accuracy, and 74% AUC in predicting THA infection in the training/validation/internal-testing cohort based on the only presence of periprosthetic bone marrow edema on MRI, while it showed 68% sensitivity, 89% specificity, 93% PPV, 60% NPV, 75% accuracy, and 79% AUC in the external-testing cohort. Conclusion AI using SVM classifier showed promising results in predicting THA infection based on MRI features. This model might support radiologists in identifying THA infection.

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Section: Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

MRI-based artificial intelligence to predict infection following total hip arthroplasty failure

et al. 2023

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“…A theoretical AI pipeline for implant evaluation may include several steps, from body part identification to implant assessment [ 90 ], as follows.…”

Section: Orthopedic Implants and Implant-related Complicationmentioning

confidence: 99%

AI applications in musculoskeletal imaging: a narrative review

Gitto,

Serpi,

Albano

et al. 2024

Eur Radiol Exp

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This narrative review focuses on clinical applications of artificial intelligence (AI) in musculoskeletal imaging. A range of musculoskeletal disorders are discussed using a clinical-based approach, including trauma, bone age estimation, osteoarthritis, bone and soft-tissue tumors, and orthopedic implant-related pathology. Several AI algorithms have been applied to fracture detection and classification, which are potentially helpful tools for radiologists and clinicians. In bone age assessment, AI methods have been applied to assist radiologists by automatizing workflow, thus reducing workload and inter-observer variability. AI may potentially aid radiologists in identifying and grading abnormal findings of osteoarthritis as well as predicting the onset or progression of this disease. Either alone or combined with radiomics, AI algorithms may potentially improve diagnosis and outcome prediction of bone and soft-tissue tumors. Finally, information regarding appropriate positioning of orthopedic implants and related complications may be obtained using AI algorithms. In conclusion, rather than replacing radiologists, the use of AI should instead help them to optimize workflow, augment diagnostic performance, and keep up with ever-increasing workload.Relevance statement This narrative review provides an overview of AI applications in musculoskeletal imaging. As the number of AI technologies continues to increase, it will be crucial for radiologists to play a role in their selection and application as well as to fully understand their potential value in clinical practice.Key points• AI may potentially assist musculoskeletal radiologists in several interpretative tasks.• AI applications to trauma, age estimation, osteoarthritis, tumors, and orthopedic implants are discussed.• AI should help radiologists to optimize workflow and augment diagnostic performance. Graphical Abstract

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“…Most DL and DCNN architectures in musculoskeletal radiology have been applied to radiographs for fracture detection, osteoarthritis grading, bone age assessment, quantification tasks, and characterization of orthopedic implants. [42][43][44][45][46][47] Although fewer studies have been performed on MRI and CTof the musculoskeletal system, 48 an increasing number of studies describe DL and DCNN methods for MRI-or CT-based image reconstruction, tissue segmentation, and musculoskeletal disease detection. 42,49…”

Section: Artificial Intelligence-based Machine Learning For Musculosk...mentioning

confidence: 99%

Radiomics and Deep Learning for Disease Detection in Musculoskeletal Radiology

Kijowski

Fritz

2022

Invest Radiol

Self Cite

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Radiomics and machine learning-based methods offer exciting opportunities for improving diagnostic performance and efficiency in musculoskeletal radiology for various tasks, including acute injuries, chronic conditions, spinal abnormalities, and neoplasms. While early radiomics-based methods were often limited to a smaller number of higher-order image feature extractions, applying machine learning-based analytic models, multifactorial correlations, and classifiers now permits big data processing and testing thousands of features to identify relevant markers. A growing number of novel deep learning-based methods describe magnetic resonance imaging-and computed tomography-based algorithms for diagnosing anterior cruciate ligament tears, meniscus tears, articular cartilage defects, rotator cuff tears, fractures, metastatic skeletal disease, and soft tissue tumors. Initial radiomics and deep learning techniques have focused on binary detection tasks, such as determining the presence or absence of a single abnormality and differentiation of benign versus malignant. Newer-generation algorithms aim to include practically relevant multiclass characterization of detected abnormalities, such as typing and malignancy grading of neoplasms. So-called delta-radiomics assess tumor features before and after treatment, with temporal changes of radiomics features serving as surrogate markers for tumor responses to treatment. New approaches also predict treatment success rates, surgical resection completeness, and recurrence risk. Practice-relevant goals for the next generation of algorithms include diagnostic whole-organ and advanced classification capabilities. Important research objectives to fill current knowledge gaps include well-designed research studies to understand how diagnostic performances and suggested efficiency gains of isolated research settings translate into routine daily clinical practice. This article summarizes current radiomics-and machine learning-based magnetic resonance imaging and computed tomography approaches for musculoskeletal disease detection and offers a perspective on future goals and objectives.

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AI MSK clinical applications: orthopedic implants

Cited by 12 publications

References 39 publications

MRI-based artificial intelligence to predict infection following total hip arthroplasty failure

MRI-based artificial intelligence to predict infection following total hip arthroplasty failure

AI applications in musculoskeletal imaging: a narrative review

Radiomics and Deep Learning for Disease Detection in Musculoskeletal Radiology

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