چاپ صفحه |  صفحه نخست سامانه |  نویسندگان |  اطلاعات تفضیلی |  دانلود مقاله | دانشگاه علوم پزشکی تبریز |
| نویسنده | نفر چندم مقاله |
|---|---|
| شهاب مهدی پور | سوم |
| سمیه امیرسعادت | اول |
| عنوان | متن |
|---|---|
| خلاصه مقاله | with AI algorithms. As shown by the findings, this study assesses the inter-rater reliability of AI-assisted PALM measures for PD and LLD.30 people participated in the study, 15 of whom were healthy and 15 of whom had limb abnormalities. AI algorithms were used to analyze the data for accuracy and repeatability after PD and LLD were measured using the PALM technique.The Intraclass Correlation Coefficient (ICC 3,3) was used to evaluate the inter-rater reliability. The findings, which are shown in Table 1, show that both raters’ PD assessments are highly accurate (Rater 1: ICC 0.90; Rater 2: ICC 0.92), while LLD measurements showed moderate reliability (Rater 1: ICC 0.76; Rater 2: ICC 0.78). Table 1. Inter-rater reliability of AI-assisted PALM measurements Additionally, Figure 1 depicts the skeletal model used for the study and outlines the key anatomical landmarks utilized for measurement. Figure 1. Anatomical landmarks for PD and LLD measurement. Discussion The findings show that AI-assisted PALM evaluations produce reliable PD outcomes, which makes them a practical tool for clinical practice. However, LLD readings are less consistent, suggesting that the technique has to be further refined. Future studies should look into integrating more robust AI frameworks to increase measurement consistency and accuracy. Applying AI-assisted diagnostic methods, like the PALM system, is a significant advancement in orthopedics. Better patient outcomes may result from these technologies’ capacity to boost efficiency and accuracy. In clinical contexts, AI-assisted diagnostic methods may greatly increase accuracy and efficiency. By automating data processing, they free up physicians’ time to concentrate on patient care rather than diagnoses. Because AI systems continuously improve their algorithms and learn from large datasets, they can also identify orthopedic diseases with greater accuracy. Patients may benefit from improved therapy results as a result. The use of AI-assisted diagnostics in orthopedics is not without its difficulties, though. Ensuring access to high-quality datasets is essential, as are ethical issues and strong data governance. It can be difficult to incorporate AI technology into present medical practices, but these challenges can be addressed with careful training and a collaborative culture between doctors and AI developers.Orthopedic AI-assisted diagnostics has a promising future. Conclusion :The use of AI-assisted diagnostic techniques, like the PALM system, is revolutionizing orthopedic care by increasing efficiency and precision. These technologies can improve patient outcomes, but challenges like data governance, clinical integration, and clinician adoption need to be addressed. A collaborative approach with ethical considerations and continuous education is crucial for harnessing the full potential of AI in orthopedics. The journey towards AI-enhanced care relies on innovation and patient-centered care.PALM-System, Diagnosis, AI-assisted for Orthopedics |
| کلمات کلیدی | Diagnostics Techniques , Orthopedics |
| نام فایل | تاریخ درج فایل | اندازه فایل | دانلود |
|---|---|---|---|
| 1403.pdf | 1403/12/08 | 2116440 | دانلود |
| IMG-20250226-WA0000.jpg | 1403/12/08 | 66608 | دانلود |
