Chinese Academy of Sciences explores the application of intelligent imaging technology

The rapid advancement of artificial intelligence (AI) has substantially expanded its application in multidisciplinary hospitals including medical imaging. Intelligent imaging technology is one of the most important applications of AI, which can process and analyze medical images produced from various sources such as CTI, ultrasound, and MRI. This analysis allows the rapid diagnosis of disease with greater accuracy and precision.

A comprehensive review conducted by research team led by Dr. Ze Zhao, Institute of Computing Technology, Chinese Academy of Sciences to explore the current development status and future direction of imaging technology and its application in various medical department. The researchers showcase how intelligent imaging technology has evolved to be a basic yet vital component of hospitals, aiding patients with quick, efficient, and precise diagnostic processes. This paper was made available online on 18 December, 2024 and was published in Chinese Medical Journal.

Dr. Ze Zhao state in their manuscript that “Intelligent imaging technology exhibits robust image recognition capabilities, making it applicable across diverse medical imaging modalities within hospital departments.” The technology interface is explained to extract and recognize any of the complex features that aid the technology to identify diseases and assist in making clinical diagnosis.

In this review article, researchers conducted a thorough search in various data sources including PubMed, Web of Science, and Google Scholar databases. Out of 332 articles, 56 different studies were selected to investigate the role of AI in analyzing intricate structures and clinical projections in areas such as cardiology, oncology, neurology and ophthalmology.

In cardiology, AI-based imaging technology has proven highly effective in interpreting electrocardiograms and echocardiograms. AI is capable of distinguishing between relevant features concerning heart health, such as detecting left ventricular hypertrophy or diseases like myocardial infarction or atrial fibrillation with unparalleled accuracy. Deep learning algorithms are also being used by researchers to better assess ejection fractions and predict cardiovascular risks. Such advancements equip cardiologists with reliable tools for faster and more precise evaluations.

Intelligent imaging has become indispensable tool in oncology for identifying, segmenting and characterizing tumors. These tools simplify diagnostic workflows and reduce the workload of oncologists while enhancing diagnostic accuracy. One novel approach employs the concurrent use of PET and CT imaging to enhance the localization and characterization of tumors, showcasing the potential benefit of multimodal techniques.

Neurology has also seen significant advancements with the application of AI-driven imaging. MRI-based AI models can identify early signs of Alzheimer’s disease, enabling interventions at earlier stages. Similarly, AI-powered systems analyzing EEG signals can pinpoint epileptic regions with greater accuracy than conventional methods. These advancements promise to revolutionize neurological care by providing earlier, more reliable diagnoses.

In ophthalmology, AI has shown unique capabilities in detecting conditions like diabetic retinopathy, glaucoma, and dry eye syndrome using fundus photography and other imaging techniques. By automating the analysis of retinal photographs, these systems significantly enhance diagnostic efficiency and reduce the strain on ophthalmologists.

Beyond the scope of individual specialties, the broader impact of intelligent imaging is its potential to reduce healthcare disparities. Advanced AI tools can deliver high-quality diagnostics to underserved areas, providing patients in remote or low-resource regions with access to cutting-edge medical care. Additionally, automated imaging systems free medical professionals to focus on more complex cases, optimizing hospital resources and improving patient outcomes.

Despite the enormous potential of AI-driven medical diagnostic, challenges remain. In context of this, Dr Zhao states that “Many issues remain, including data artifacts, noise, higher model complexity, and the need to adjust the model according to the actual task.” They point out that AI systems often lack transparency, making it difficult for clinicians to understand how decisions are made. To overcome this, they suggest a focus on improving model interpretability and ensuring diverse, high-quality datasets.

Looking forward, the authors envision intelligent imaging technologies evolving into adaptive systems capable of tackling increasingly complex medical challenges. “We anticipate developing deep learning models that are more transparent and explanatory through technological innovation,” Dr Zhao conclude.

Reference

DOI: https://doi.org/10.1097/CM9.0000000000003436