Abstract
In recent years, a number of new products introduced to the global market combine intelligent robotics, artificial intelligence and smart interfaces to provide powerful tools to support professional decision making. However, while brain disease diagnosis from the brain scan images is supported by imaging robotics, the data analysis to form a medical diagnosis is performed solely by highly trained medical professionals. Recent advances in medical imaging techniques, artificial intelligence, machine learning and computer vision present new opportunities to build intelligent decision support tools to aid the diagnostic process, increase the disease detection accuracy, reduce error, automate the monitoring of patient's recovery, and discover new knowledge about the disease cause and its treatment. This article introduces the topic of medical diagnosis of brain diseases from the MRI based images. We describe existing, multi-modal imaging techniques of the brain's soft tissue and describe in detail how are the resulting images are analyzed by a radiologist to form a diagnosis. Several comparisons between the best results of classifying natural scenes and medical image analysis illustrate the challenges of applying existing image processing techniques to the medical image analysis domain. The survey of medical image processing methods also identified several knowledge gaps, the need for automation of image processing analysis, and the identification of the brain structures in the medical images that differentiate healthy tissue from a pathology. This survey is grounded in the cases of brain tumor analysis and the traumatic brain injury diagnoses, as these two case studies illustrate the vastly different approaches needed to define, extract, and synthesize meaningful information from multiple MRI image sets for a diagnosis. Finally, the article summarizes artificial intelligence frameworks that are built as multi-stage, hybrid, hierarchical information processing work-flows and the benefits of applying these models for medical diagnosis to build intelligent physician's aids with knowledge transparency, expert knowledge embedding, and increased analytical quality.
Highlights
Medical diagnosis aided by software systems that use a variety of artificial intelligent and machine learning algorithms have been available since early 1990s (Kononenko, 2001)
The opportunity to augment the imaging robotics with intelligent image processing can produce a wide range of decision support tools (DST) that will (1) reduce the time it takes for a radiologist to analyse images to extract meaningful features, (2) diagnose brain disease that cannot be detected from the images using naked eye the information exists in the images and the machine learning algorithms can learn to differentiate the normal cases from the pathology ones, and (3) automate the monitoring of brain tissue recovery
Weishaupt et al.’s studies recommend that even the straight forward diagnosis of the large structural changes such as tumors can benefit from cross-referencing the image sets from all available imaging techniques to reveal all different tumor sub-components, surrounding inflammation, fluid accumulation etc., as no imaging technique alone shows all tumor aspects (Weishaupt et al, 2008)
Summary
Medical diagnosis aided by software systems that use a variety of artificial intelligent and machine learning algorithms have been available since early 1990s (Kononenko, 2001) These medical diagnostic tools use a patient’s symptoms, diagnostic measurements and lab results as inputs, and the system returns a ranked list of diagnoses along with suggested treatments (Kononenko, 1993; Soni et al, 2011). The opportunity to augment the imaging robotics with intelligent image processing can produce a wide range of decision support tools (DST) that will (1) reduce the time it takes for a radiologist to analyse images to extract meaningful features, (2) diagnose brain disease that cannot be detected from the images using naked eye the information exists in the images and the machine learning algorithms can learn to differentiate the normal cases from the pathology ones, and (3) automate the monitoring of brain tissue recovery. A narrow application domain, the use of ML algorithms for feature extraction and classification has significant benefits for the early disease diagnosis that can be missed by a physician, malignant tissue segmentation for surgical intervention, and predicting patient’s survival rates after forming a preliminary diagnosis
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