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Artificial Intelligence promising for diagnosing hip fracture from hip radiographs: JAMA

Written By : Jacinthlyn Sylvia |Medically Reviewed By : Dr. Kamal Kant Kohli Published On 2023-03-21T20:00:34+05:30  |  Updated On 21 March 2023 8:31 PM IST
Artificial Intelligence promising for diagnosing hip fracture from hip radiographs: JAMA
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A recent systematic review and meta-analysis by Johnathan Lex and team suggests that there is potential for Artificial intelligence (AI) in the diagnositic process of hip radiographs. The findings of this study were published in the Journal of American Medical Association.

Strong models made possible by artificial intelligence can be used to construct clinical diagnostic and prognostic tools for hip fractures; however, the effectiveness and potential consequences of these recently established algorithms are yet unclear. In order to compare the efficacy of AI algorithms built to detect hip fractures on radiography and determine postoperative clinical outcomes after hip fracture surgery to existing procedures, this study was carried out.

Using the databases for Embase, MEDLINE, and the Cochrane Library, a thorough evaluation of the literature was conducted for all publications released up through January 23, 2023. To find any more pertinent papers, a manual reference search was also done on the listed articles. Included were studies using machine learning (ML) models to diagnose hip fractures from radiographs of the hip or pelvis or to forecast any postoperative patient outcome following hip fracture surgery.

The key findings of this study were:

A total of 39 papers that satisfied all requirements and were included in the study employed AI models in 18 (46.2%) of them to identify hip fractures on plain radiographs and in 21 (53.8%) of them to forecast patient outcomes after hip fracture surgery.

For training, validating, and testing ML models specific to diagnostic and postoperative outcome prediction, respectively, a total of 39 598 plain radiographs and 714 939 hip fractures were employed.

The most likely results were mortality and length of hospital stay.

In comparison to doctors, the OR for diagnostic mistake of ML models for hip fracture radiographs on pooled data analysis was 0.79.

The average (SD) sensitivity, specificity, and F1 score for the ML models were 89.3% (8.5%), 87.5% (9.9%), and 0.90, respectively (0.06).

In conclusion, when it came to identifying hip fractures, artificial intelligence performed on par with experienced radiologists and surgeons. The existing applications of AI for outcome prediction, however, do not appear to offer a significant advantage over multivariable predictive statistics.

Reference:

Lex, J. R., Di Michele, J., Koucheki, R., Pincus, D., Whyne, C., & Ravi, B. (2023). Artificial Intelligence for Hip Fracture Detection and Outcome Prediction. In JAMA Network Open (Vol. 6, Issue 3, p. e233391). American Medical Association (AMA). https://doi.org/10.1001/jamanetworkopen.2023.3391

Artificial intelligenceHip FractureJAMA Network Open
Source : JAMA Network Open
Jacinthlyn Sylvia
Jacinthlyn Sylvia

    Neuroscience Masters graduate

    Jacinthlyn Sylvia, a Neuroscience Master's graduate from Chennai has worked extensively in deciphering the neurobiology of cognition and motor control in aging. She also has spread-out exposure to Neurosurgery from her Bachelor’s. She is currently involved in active Neuro-Oncology research. She is an upcoming neuroscientist with a fiery passion for writing. Her news cover at Medical Dialogues feature recent discoveries and updates from the healthcare and biomedical research fields. She can be reached at editorial@medicaldialogues.in

    Dr. Kamal Kant Kohli
    Dr. Kamal Kant Kohli

    Dr Kamal Kant Kohli-MBBS, DTCD- a chest specialist with more than 30 years of practice and a flair for writing clinical articles, Dr Kamal Kant Kohli joined Medical Dialogues as a Chief Editor of Medical News. Besides writing articles, as an editor, he proofreads and verifies all the medical content published on Medical Dialogues including those coming from journals, studies,medical conferences,guidelines etc. Email: drkohli@medicaldialogues.in. Contact no. 011-43720751

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