Medical imaging such as X-rays, Ultrasound, CT, PET, and MRI are important tools for diagnosis, treatment planning, and long-term health monitoring. However, analyzing these medical images manually requires significant expertise, time, and effort. In addition, increased workload for radiologists and physicians may cause potential delays in disease diagnosis and treatment.
The performance of AI models has surpassed human intelligence in other areas such as surveillance and autonomous driving, achieving high levels of accuracies. With its ability to analyze enormous volumes of image data, AI can have an even more profound impact on healthcare operational efficiency and patient outcomes through faster, more accurate diagnosis and early intervention. Some of the areas in healthcare where AI is being used are:
1. Auto-detection of specific health conditions: Advanced AI techniques applied to digitized imaging data helps in the automatic detection of health conditions. For example, it can identify anomalies from retinal images, bleeding spots in gastrointestinal (GI) tract as well as lesions, and COVID-19 severity scores from computed tomography (CT) scans. AI in medical imaging also aids in tracking the progression of wound healing, detecting and categorizing portal hypertension, and hepatobiliary disorders, as well as screening of cancer from a variety of imaging modalities.
2. Auto-identification of organs and their substructures: There are emerging AI techniques that can automatically identify cardiac sub-structures from CT angiography, anatomical landmarks from X-rays, and human cells in digital slides.
3. Synthesizing multiple image modes:
a) Multi-modal MRI imaging: Complementary information obtained from different contrasts of tissues helps physicians diagnose diseases more accurately and plan treatments effectively. However, acquiring multiple contrasts MRI for every patient is not time-efficient and cost-effective. Hence, AI-based synthesis of multi-modal MRI is an effective solution.
b) Time series analysis of images taken over time: Time-series analysis of medical images facilitates the deeper understanding of a patient’s entire lifetime’s health and clinical trajectories, across a wide range of diseases, including cancer, and cardiovascular diseases. It is an important tool in medical imaging for accomplishing tasks such as dynamic forecasting, survival analysis, early diagnosis, and treatment affect estimation.
4. Assisting surgeons: Several AI-based solutions are being developed to support surgery preparations and serve as building blocks for remote surgery. These include auto-instrument positioning during eye surgeries, tool detection and identification, smoke detection and dehazing, and surgical visualization.