Role of Artificial Intelligence (AI) in enhancing Musculoskeletal Imaging

Artificial Intelligence 0r AI is a hot topic for medical researchers due to its diverse application. A study published in the American Journal of Roentgenology outlined AI’s role in evaluating the suitability of imaging instructions to help predict fracture risk in Patients.

It concluded that the use of AI has the potential to greatly enhance every component of the imaging value chain and AI can increase the value that musculoskeletal imagers provide to their patients and to referring clinicians by improving image quality, patient centricity, imaging efficiency, and diagnostic accuracy.

The review paper mentioned seven methods in which AI provides improved musculoskeletal imaging

1. Scheduling- Predictive analysis with AI regression models using electronic medical record data has been used to predict imaging no-shows successfully. ML algorithms have also been used to predict missed appointments in other clinical settings, ranging from diabetes clinics to urban academic centers.


2. Imaging Appropriateness and Protocoling- ML can provide a more comprehensive evidence-based resource to help select the best imaging examination. ML algorithms can incorporate various sources of information from a patient’s medical records, including symptoms, laboratory values, physical examination findings, and prior imaging results, to recommend an appropriate patient-specific imaging examination tailored to the clinical question that needs to be answered.


3. Image Acquisition and Reconstruction- AI may increase the speed of Image Acquisition. Decreasing imaging acquisition time has been a major ongoing field of research since the invention of MRI. Early studies have shown promising results for knee MRI scans that are accelerated by machine learning, for example. In addition, machine learning offers an exciting new tool for reducing radiation dose in CT, according to the researchers. A recent study reported that more than 90% of readers indicated that the quality of low-dose CT images produced in part by an artificial neural network was equal or greater than CT images acquired using standard doses, the authors noted.


4. Decreasing CT Radiation Dose- AI provides an exciting new tool for reducing the radiation dose in CT.


5. Image Presentation- AI has the potential to revolutionize the way in which a PACS displays information for the radiologist, by using smarter tools that process a variety of available data. One PACS vendor uses ML algorithms to learn how radiologists prefer to view examinations, collect contextual data, present layouts for future similar studies, and adapt after any corrections.


6. Image Interpretation- In MSK radiology alone, ML algorithms have been applied to various conditions, including diagnosis of fractures, osteoarthritis, bone age, and bone strength.


7. Quantitative Image Analysis- ML can improve quantitative analysis by allowing automatic segmentation of the areas of interest, depending on the region of the body and clinical question.

For further reference, click on the link

https://doi.org/10.2214/AJR.19.21117