AI is assisting in unraveling crucial insights into clinical-decision making and in the extraction of useful information from indigenously unstructured, inaccessible data assets.
FREMONT, CA: Healthcare industry is increasingly incorporating technology for better patient outcomes and streamlining several healthcare processes. Efforts to automate processes and digitize the systems have resulted in fruitful outcomes. Artificial intelligence (AI) and machine learning (ML) has been an integral part of these changes. Moreover, as these technologies and innovative analytics strategies are evolving into more accurate and applicable forms, healthcare will see magical transformations in the future.
AI is assisting in unraveling crucial insights into clinical-decision making, equipping patients with resources for self-management and in the extraction of useful information from indigenously unstructured, inaccessible data assets. Medical imaging is considered one of the most productive reservoirs of information about patients. However, enormous data packed into the results from CAT scans, X-rays, MRIs, and other testing modalities, gauging through high-resolution images is a challenge, even for experienced clinical professionals.
AI has already been proved to be a valuable alloy for pathologists and radiologists who are eyeing to improve their accuracy and accelerate their productivity. Several studies have shown that AI can perform at par with human clinicians in quickly and precisely identifying features in images. So what are the top applications for AI in the imaging world, and how can AI improve the diagnosis and detection of potentially fatal conditions? Here is an insight into it:
Spotting Cardiovascular Abnormalities
The measurement of various structures of the heart can predict an individual’s risk for cardiovascular diseases or reveal issues that may require attention and can be thereafter addressed via surgery or pharmacological management.
Automation of the detection of abnormalities in frequently-ordered imaging tests, like chest x-rays, can lead to quicker decision-making and lesser diagnostic errors. For instance, the chest radiograph is often the first imaging study when a patient is admitted to the emergency department with a complaint such as breathlessness or shortness of breath. It can be utilized as a quick initial screening measure for cardiomegaly.
AI can be used to identify left arterial enlargement from chest x-rays can also help providers target appropriate treatments for patients. AI tools can help in automating other measurement tasks such as carina angle measurement, aortic valve analysis, and pulmonary artery diameter.
Detecting Fractures and Other Musculoskeletal Injury
Fractures and musculoskeletal injuries can result in chronic, long-term pain if not addressed correctly and quickly. Injuries like hip fractures are common among elderly patients. AI can play a crucial role in identifying hard-to-see fractures, soft tissue injuries, and dislocations, which will allow specialists and surgeons to define the line of treatment in the future clearly. Using unbiased algorithms to review images in case of trauma will ensure that all the injuries are addressed. AI also provides a safety net while operating routine follow-ups for hip surgeries such as hip joint replacements. AI will help reduce the patient risk, false-negative rate, and medical and legal risk for the radiologists. Patients with high-risk can be screened for elevated serum cobalt levels and sent to MRI for evaluation.
Diagnosis of Neurological Diseases
AI can assist in early diagnosis of degenerative urological diseases. While neurological disorders such as amyotrophic lateral sclerosis (ALS) and other similar neurological conditions have no cure, accurate diagnosis can help individuals understand their future outcomes and plan their care accordingly.
Currently, assessments for quantitative susceptibility mapping (QSM) of the motor cortex and manual segmentation are difficult, necessary, and time-consuming. Automating the process with ML will assist in the development of an effective imaging biomarker. Algorithms will streamline the process with the help images that indicate suspect results and providing risk ratios that the images contain evidence of PLS or ALS. Algorithms can also have a say in automatically populating reports and offsetting workflow burdens on providers.