How AI is Propelling Radiology & Medical Imaging Capabilities

Medical imaging has been used extensively for early diagnosis of various diseases. Be it computed tomography (CT), mammography, ultrasonography, magnetic resonance imaging (MRI), and nuclear medicine tests, radiologists and pathologists have relied on these images to analyse the condition of patients. However, most of the analysis has been manual, heavily depending on the skill sets of the radiologists or pathologists to identify anomalies in the images. The challenge has been the enormous volumes of patient samples to be analysed and the limited number of radiologists or pathologists. Let's take an in-depth look at the impact and possibilities of AI and new applications in the Indian context.

- Suraj Nair

In India, there is only one radiologist for every one lakh patients, which translates to one radiologist for every 100 scans performed each day. There is a severe shortage of radiologists in tier 2 and 3 cities as well. As the number of scans keeps rising and new high-resolution imaging technologies enter the market, it has become highly imperative to develop faster analytical techniques to assist pathologists and radiologists.

Enter AI, ML

Artificial intelligence (AI), using machine learning (ML) and deep learning (DL) algorithms has provided interventions in this direction. Machine learning and deep learning algorithms have been used for the past decade to interpret and analyse medical images and aid radiologists and pathologists in screening and diagnosis. Today AI has extended its capability to the entire radiology world, which includes digitising the devices, aiding in diagnosis and finally collating all the data and information into a digital assistant. Image analysis can be broken down into computer-aided detection, diagnosis, and image segmentation. Computer-aided detection and diagnosis involves the use of AI algorithms to analyse medical images and detect abnormalities and diseases.

On the other hand, image segmentation uses AI algorithms to identify and label various parts of a medical image as organs, blood vessels, or tumours. This can help radiologists locate abnormalities and design surgeries more accurately. For example, around 3.5 billion chest X-rays are performed annually worldwide, which require timely and accurate interpretation. AI solutions categorise X-rays based on suspicions of diseases which can be prioritised by radiologists for faster diagnoses boosting efficiency by 30-50 per cent.