Do Artificial Intelligence Supports Medical Imaging?

Artificial intelligence and machine learning have captivated the healthcare industry as these innovative analytics strategies become more accurate and applicable to a variety of tasks.

AI is increasingly helping to uncover hidden insights into clinical decision-making, connect patients with resources for self-management, and extract meaning from previously inaccessible, unstructured data assets.

Medical imaging data is one of the richest sources of information about patients, and often one of the most complexes. Let us see the role of AI on medical imaging.

Identifying the Abnormalities in Cardiovascular

Measuring the various structures of the heart can reveal an individual’s risk for cardiovascular diseases or identify problems that may need to be addressed through surgery or pharmacological management.

Automating the detection of abnormalities in commonly-ordered imaging tests, such as chest x-rays, could lead to quicker decision-making and fewer diagnostic errors.

Quick screening tool for cardiomegaly can be done, which in and of itself can be used as a marker for heart disease using artificial intelligence to identify left atrial enlargement from chest x-rays could rule out other cardiac or pulmonary problems and help providers target appropriate treatments for patients.

Similar AI tools could be used to automate other measurement tasks, such as aortic valve analysis, carina angle measurement, and pulmonary artery diameter.

Applying AI to imaging data may also help to identify thickening of certain muscle structures, such as the left ventricle wall, or monitor changes in blood flow through the heart and associated arteries.

Automated pulmonary artery flow quantification would save the interpreting physician time via elimination of manual measurements, prevent detection errors, and provide structured quantitative data, which could be used in later studies or risk stratification schemes.

Algorithms could automatically populate reports, saving time for human clinicians, and identify measurements or values that qualify as abnormal.

Detecting fractures and other musculoskeletal injuries

Fractures and musculoskeletal injuries can contribute to long-term, chronic pain if not treated quickly and correctly.

Injuries such as hip fractures in elderly patients are also tied to poor overall outcomes due to reductions in mobility and associated hospitalizations.

Using artificial intelligence to identify hard-to-see fractures, dislocations, or soft tissue injuries could allow surgeons and specialists to be more confident in their treatment choices.

After trauma, fractures are often considered secondary in importance, at least compared to internal bleeding or organ injury.  When human diagnosticians looking at trauma-related imaging focus first on their immediate clinical concerns, fractures can sometimes are overlooked.

In one example, a patient presenting to the ED with head and neck trauma could be assessed for odontoid fracture a type of fracture in the cervical spine by using an AI radiology tool.

The fracture type is often difficult to detect on standard images, but AI tools may be more likely to see subtle variations in the image that could indicate an instability that requires surgery.

Allowing unbiased algorithms to review images in trauma patients may help to ensure that all injuries are accounted for and receive the care required to secure a positive outcome.

Providers may also find that AI provides a useful safety net when conducting routine follow-ups for common hip surgeries, such as hip joint replacements.

There are roughly 400,000 total hip arthroplasties (THAs) performed annually. Every patient has annual follow-up exams, which can add up to about 100 exams per day for a musculoskeletal radiologist who works with arthroplasty surgeons.

If a joint replacement device becomes loosened or the tissue around the device reacts poorly, the patient could require an expensive and invasive revision.

Unfortunately, identifying problems around the site can be challenging.

AI meeting this use case would help to reduce the false negative rate, patient risk, and medical legal risk for the radiologists. High-risk patients could be screened for elevated serum cobalt levels and sent to MRI for further evaluation.

Prevention in the diagnosis of Neurological Diseases

Degenerative neurological diseases, such as amyotrophic lateral sclerosis can be a devastating diagnosis for patients.  While there is currently no cure for ALS and many similar neurological conditions, accurate diagnoses could help individuals understand their likely outcomes and plan for long-term care or end-of-life wishes.

Identifying ALSand distinguishing between ALS and primary lateral sclerosis (PLS) relies on imaging studies, says the College.  Radiologists must decide if lesions are relevant or simply mimicking the structures of one of the diseases, and false positives are relatively common.

Recent research into improving the speed and accuracy of diagnoses has focused on identifying new biomarkers.

Algorithms may be able to streamline this process by flagging images that indicate suspect results and offering risk ratios that the images contain evidence of ALS or PLS.  Algorithms may also be able to automatically populate reports, reducing workflow burdens on providers.

Screening Imaging of cancer

Medical imaging is often used in routine, preventive screenings for cancers, such as breast cancer and colon cancer.

In breast cancer, microcalcification in tissue can often be difficult to conclusively identify as either malignant or benign.  False positives could lead to unnecessary invasive testing or treatment, while missed malignancies could result in delayed diagnoses and worse outcomes.

There is variability in radiologist interpretation of microcalcifications at the time of diagnostic imaging.

It can improve the level of accuracy and use quantitative imaging features to more accurately categorize microcalcifications by level of suspicion for ductal carcinoma in situ potentially decreasing the rate of unnecessary benign biopsies.

Providing risk scores for areas of concern could allow providers and patients to make more informed decisions about how to proceed with testing or treatment.

Similarly, patients undergoing screenings for colorectal cancer may have more productive conversations with their providers if polyps are found during routine checks. Polyps are precursors to cancer.

For patients with established cancers, AI could support the detection of malignancies that have spread. Extra nodal extension of cancers is associated with poor prognosis, and is often only discovered at the time of a surgery.

“A per formant algorithm could potentially identify ECE for diagnoses that do not usually proceed to surgery, potentially enabling better treatment stratification in this population. Automated ECE classification and identification could also enable improved radiotherapy targeting of nodal basins, as well as treatment optimization for post-operative imaging-detected nodal disease.

AI could be useful for head and neck cancers, prostate cancer, colorectal cancers, and cervical cancer, the society says.

While more study will be required to test the utility of AI for these and other use cases, ACR DIS appears confident that medical imaging is ready for artificial intelligence.  Supplementing diagnostics and decision-making with AI could offer providers and patient’s life-changing insights into a variety of diseases, injuries, and conditions that may be difficult to identify with the human eye alone.

Conclusion

I hope the above information on medical imaging technology may get you some interest to help in your stream those who are enthusiastic as to be an AI developers or an app developer, where app development companies or a researchers were still working to bring an innovative ideas which is useful for the society.

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