Systematic review of artificial intelligence tack in preventive orthopaedics: is the land coming soon?

Корнеев, А. А.; Lipina, Marina; Лычагин, А.В.; Timashev, P. S.; Kon, Elizaveta; Telyshev, D. V.; Goncharuk, Yu.R.; Vyazankin, Ivan; Elizarov, Mikhail; Murdalov, Emirkhan E.; Pogosyan, David A.; Zhidkov, Sergei; Bindeeva, Anastasia; Liang, Xing‐Jie; Lasovskiy, Vladimir; Grinin, Victor; Anosov, Alexey; Kalinsky, Eugene

doi:10.1007/s00264-022-05628-2

Cited by 6 publications

(2 citation statements)

References 69 publications

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“…This new tool QUADAS-AI 14 is an AI-specific extension of QUADAS-2 and QUADAS-C, includes four domains in the risk of bias and three domains in applicability concerns, which is more comprehensive and suitable for AI associated studies. Some studies [ 45 – 47 ] related to AI models have also employed this new tool.…”

Section: Discussionmentioning

confidence: 99%

Image-based AI diagnostic performance for fatty liver: a systematic review and meta-analysis

Zhao,

Lan,

Yin

et al. 2023

BMC Med Imaging

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Background The gold standard to diagnose fatty liver is pathology. Recently, image-based artificial intelligence (AI) has been found to have high diagnostic performance. We systematically reviewed studies of image-based AI in the diagnosis of fatty liver. Methods We searched the Cochrane Library, Pubmed, Embase and assessed the quality of included studies by QUADAS-AI. The pooled sensitivity, specificity, negative likelihood ratio (NLR), positive likelihood ratio (PLR), and diagnostic odds ratio (DOR) were calculated using a random effects model. Summary receiver operating characteristic curves (SROC) were generated to identify the diagnostic accuracy of AI models. Results 15 studies were selected in our meta-analysis. Pooled sensitivity and specificity were 92% (95% CI: 90–93%) and 94% (95% CI: 93–96%), PLR and NLR were 12.67 (95% CI: 7.65–20.98) and 0.09 (95% CI: 0.06–0.13), DOR was 182.36 (95% CI: 94.85-350.61). After subgroup analysis by AI algorithm (conventional machine learning/deep learning), region, reference (US, MRI or pathology), imaging techniques (MRI or US) and transfer learning, the model also demonstrated acceptable diagnostic efficacy. Conclusion AI has satisfactory performance in the diagnosis of fatty liver by medical imaging. The integration of AI into imaging devices may produce effective diagnostic tools, but more high-quality studies are needed for further evaluation.

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Section: Discussionmentioning

confidence: 99%

Image-based AI diagnostic performance for fatty liver: a systematic review and meta-analysis

Zhao,

Lan,

Yin

et al. 2023

BMC Med Imaging

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“…In addition, very few guiding studies currently pinpoint how predictors of surgical complications, inflammatory correlates, pain attributes and experiences, cognitive, behavioral and muscle influences, including the role of comorbid health conditions and hip joint shape among other factors determine the disease course with any certainty [35]. In addition, even among diagnostically themed publications that are most favored, along with surgical themes of study, Korneev, et al, identified two problems that block the full integration of AI into the routine of an orthopedic physician, including those who are involved in hip osteoarthritis care [36]. The first of these is related to the insufficient amount, variety and quality of data for training and validation and testing of AI models in almost any sphere.…”

Section: Discussionmentioning

confidence: 99%

Artificial Intelligence and Its Potential Application in Advancing Hip Osteoarthritis Care

Marks¹

2023

JOSR

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Background: Hip joint osteoarthritis remains an incurable disabling health condition. Aim: To examine what trends exist in the realm of Artificial Intelligence [AI] applications to hip osteoarthritis. Methods: An in-depth literature review focusing on hip osteoarthritis and selected artificial intelligence association’s themes was conducted. Results: Artificial intelligence is being widely studied and applied in the realms of hip osteoarthritis diagnoses and surgical factors and approaches, but less so in the clinical, and deterministic spheres. Conclusion: Future expanded research efforts that integrate the features of the whole joint and person-environment factors and their association with favorable and unfavorable hip osteoarthritis outcomes are needed and are likely to prove promising and save immense human and service costs. Keywords: Arthroplasty; Artificial Intelligence; Deep Learning, Diagnosis; Hip Osteoarthritis; Machine Learning; Outcomes; Pathology; Prevention; Surgery

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Artificial intelligence and robots like us (surgeons) for people like you (patients): toward a new human–robot-surgery shared experience. What is the moral and legal status of robots and surgeons in the operating room?

Hernigou¹,

Lustig

Caton

2023

International Orthopaedics (SICOT)

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Systematic review of artificial intelligence tack in preventive orthopaedics: is the land coming soon?

Cited by 6 publications

References 69 publications

Image-based AI diagnostic performance for fatty liver: a systematic review and meta-analysis

Image-based AI diagnostic performance for fatty liver: a systematic review and meta-analysis

Artificial Intelligence and Its Potential Application in Advancing Hip Osteoarthritis Care

Artificial intelligence and robots like us (surgeons) for people like you (patients): toward a new human–robot-surgery shared experience. What is the moral and legal status of robots and surgeons in the operating room?

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