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Artificial Intelligence Revolutionizes Leg Axis Measurements in Medical Imaging

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In a groundbreaking study, researchers have demonstrated the potential of artificial intelligence (AI) to revolutionize the medical field, particularly in the analysis of leg axis parameters. The study, which conducted a systematic review and meta-analysis, aimed to determine the reliability and applicability of AI-based leg axis measurements compared to traditional human raters. This could have significant implications for market access, as the efficiency and accuracy of AI can streamline medical procedures and reduce costs.

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Systematic Review and Meta-Analysis

The study’s protocol was meticulously registered with the International Prospective Register of Systematic Reviews (PROSPERO). Comprehensive searches of databases such as PubMed, Epistemonikos, and Web of Science were carried out up to February 24, 2024. A stepwise screening process was employed to review titles and abstracts, ensuring a robust selection of relevant studies. Following data extraction and quality assessment, a frequentist meta-analysis was conducted using a common effect/random effects model with inverse variance and the Sidik-Jonkman heterogeneity estimator.

Key Findings

Incorporating a total of 13 studies that involved 3192 patients, the meta-analysis revealed compelling results. All included studies compared AI-based leg axis measurements on long-leg radiographs (LLR) with those performed by human raters. Parameters such as the hip knee ankle angle (HKA), mechanical lateral distal femoral angle (mLDFA), mechanical medial proximal tibial angle (mMPTA), and joint-line convergence angle (JLCA) exhibited excellent agreement between AI and human assessments. Notably, the AI system demonstrated a time efficiency advantage, being approximately 3 minutes faster in reading standing long-leg anteroposterior radiographs (LLRs) than human raters.

The findings underscore the potential for AI to enhance market access by providing a reliable and quicker alternative to human-based measurements. This efficiency could lead to better resource allocation and reduced waiting times in clinical settings, ultimately benefiting patients and healthcare providers alike.

Concrete Inferences from the Study

The study’s outcomes suggest several critical implications for the integration of AI in medical imaging:

  • AI-based assessments can significantly reduce the time required for leg axis measurement, improving workflow efficiency.
  • The high accuracy of AI systems ensures that they can be trusted to provide measurements comparable to those of experienced human raters.
  • The AI technology’s robustness, unaffected by implants or pathological conditions, suggests a broad applicability in various clinical scenarios.

In conclusion, the study provides compelling evidence that AI-based assessment of leg axis parameters is both efficient and accurate. The integration of AI in this domain is not hindered by the presence of implants or pathological conditions, making it a reliable and time-saving tool for clinicians. This advancement holds promise for enhancing market access and optimizing clinical workflows, paving the way for more advanced and efficient medical practices.

Original Article:

Knee Surg Sports Traumatol Arthrosc. 2024 Jul 21. doi: 10.1002/ksa.12362. Online ahead of print.

ABSTRACT

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PURPOSE: The aim of this study was to conduct a systematic review and meta-analysis on the reliability and applicability of artificial intelligence (AI)-based analysis of leg axis parameters. We hypothesized that AI-based leg axis measurements would be less time-consuming and as accurate as those performed by human raters.

METHODS: The study protocol was registered with the International Prospective Register of Systematic Reviews (PROSPERO). PubMed, Epistemonikos, and Web of Science were searched up to 24 February 2024, using a BOOLEAN search strategy. Titles and abstracts of identified records were screened through a stepwise process. Data extraction and quality assessment of the included papers were followed by a frequentist meta-analysis employing a common effect/random effects model with inverse variance and the Sidik-Jonkman heterogeneity estimator.

RESULTS: A total of 13 studies encompassing 3192 patients were included in this meta-analysis. All studies compared AI-based leg axis measurements on long-leg radiographs (LLR) with those performed by human raters. The parameters hip knee ankle angle (HKA), mechanical lateral distal femoral angle (mLDFA), mechanical medial proximal tibial angle (mMPTA), and joint-line convergence angle (JLCA) showed excellent agreement between AI and human raters. The AI system was approximately 3 min faster in reading standing long-leg anteroposterior radiographs (LLRs) compared with human raters.

CONCLUSION: AI-based assessment of leg axis parameters is an efficient, accurate, and time-saving procedure. The quality of AI-based assessment of the investigated parameters does not appear to be affected by the presence of implants or pathological conditions.

LEVEL OF EVIDENCE: Level I.

PMID:39033340 | DOI:10.1002/ksa.12362


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