“During the last decade, medical image processing has been revolutionized by the development of advanced technologies such as robotics or artificial intelligence. Until today, conventional image analysis was performed and interpreted primarily by healthcare professionals. Certain tools can help them better visualize information, but it is up to the practitioner to search, identify and diagnose. However, the field of artificial intelligence and more specifically Deep Learning (a sub-domain of the latter) has made it possible to provide new solutions to solve certain problems, particularly in “pattern recognition”. Today the demand for automated medical image processing is growing. At the time of the global covid-19 pandemic, artificial intelligence was in high demand. For example to increase the speed and reliability of medical diagnostics. Or to optimize the management of patient flows within hospitals. Its use in the medical field can help transform a very wide variety of sectors of activity: from management, through diagnosis to treatment, but we will only focus here on image processing {1}…”
“AI can provide an answer to this new bottleneck in health processes. Indeed, an AI system during its training will analyze and cross-reference the information from each image to establish relationships between them. These relationships take the form of patterns. These can be similarities in terms of textures, location, shapes and colors associated with a disease. These similarities are then used to discriminate the presence or absence of a pathology for example. If properly trained, the system will be able to detect the disease with a reliability comparable to that of the data it has learned about. Since these data are generally obtained from health professionals, the system can perform at a level comparable, if not better, than that of the health professionals. Indeed, one study showed that a Deep Learning system correctly detects a disease state in 87% of cases compared to 86% for healthcare professionals {2}. This slightly better performance is due to the fact that the system can also find new reasons for its diagnosis. Today, medical image analysis performed by Deep Learning is used for three major functions: semantic segmentation of items in medical images, classification and disease detection {3}.
The emergence of AI technologies can therefore fill the gap in the number of physicians while absorbing the growth in the amount of data to be analyzed. This is all the more urgent as the number of healthcare professionals tends to remain the same or even decline in some countries, notably in the UK, the USA or Japan {4}. Deep Learning in the field of medical image classification therefore lightens the workload of, for example, radiologists, since it allows them to efficiently evaluate a large number of images for them {5}. As such, the diagnoses provided by medical image analysis algorithms are often used as a “second opinion” by practitioners. They can help avoid human misinterpretation when the analysis is too rapid, but also facilitate clinical decision making to improve the outcome for the patient {6}. It is a decision aid for practitioners but is in no way intended to replace them…” Read the full article here.
Source: Medical images processing: How far can it be automated? – By Arnault Ioualalen, February 25, 2021. LinkedIn.
