Machine Learning in Medical Imaging: Key to Diagnosing Diseases
April 4, 2023
Carolyn Joy V.
There's certainly a lot to be said about how artificial intelligence and machine learning algorithms are transforming the way things are being done in just about every industry. The use of machine learning in the healthcare sector however, is life-changing and, to an extent, life-saving.
More organizations think so, too. The Future of Health Index 2022 Report commissioned by health tech conglomerate Phillips indicates that 60 percent of healthcare leaders are bent on making healthcare AI a priority. The surging interest in AI and machine learning in medicine is not without reason. Already, AI systems have been driving innovation and efficiently handling the rapidly-growing stores of healthcare data. And there's more - machine learning in medical imaging.
Medical Image Annotation for Machine Learning
Evaluating medical images is one of the most significant applications of machine learning in medicine, showing a lot of potential for diagnosing diseases and in-depth image analysis. But to work effectively, ML models for medical imagery require high quality training data―data that you can only get from labeled and annotated images. This is where medical image annotation is needed.
Medical image annotation is the process of labeling medical images such as MRI, PET, and CT scans, X-Ray, ultrasound, and the like. The labeled images that are generated form part of the collection of annotated examples of medical images which are used as training datasets for neural networks in machine learning and deep learning algorithms. Medical image annotations are provided by health specialists in that field to form as a basis for reliable and highly-accurate medical diagnostics.
Machine Learning Use Cases for Medical Images
Massive amounts of data are generated by medical imaging, and when processed with the right annotating tools, these data sets can be harnessed into structured sets that ML algorithms can glean insights from.
That said, here are some of the latest machine learning use cases for medical imagery:
Diagnostics and monitoring of brain diseases. In diagnosing diseases, neurodegenerative disorders such as Alzheimer’s and Parkinson’s disease have been the most challenging to determine and observe progression of, due to the lack of reliable tools. The same goes for monitoring the neurological effects of cancer and chemotherapy. With the aid of deep learning in medical imaging, experts can now maximize output from neurological images by incorporating computational techniques, which then helps advance the understanding of neurodegenerative diseases.
Cancer detection. Cancer can still be difficult to detect and diagnose even with modalities like CT scans and MRI. Training from high-quality annotated medical images, machine learning algorithms are able to spot tumors and cancer cells faster and more accurately than humans. This leads to early cancer detection, which can pave the way for optimizing patient treatment plans and increasing chances of survival.
Enhanced ultrasound analysis. AI models applied on annotated ultrasound images can bring out higher levels of granularity to the organ or structure inside the body that is being observed. The improved clarity can be highly impactful to understanding the issue and dealing with it. Higher detail in the images of gallbladder stones or fetal deformation, for instance, will allow specialists to better design a treatment or care plan.
Pathology. Using machine learning in medical imaging not only reduces complexity of diagnosing and detecting certain diseases; it also fastracks the diagnosis of the more common diseases. Taking scans and images from specialized equipment, labeling them accurately, and training ML models from these, allow faster diagnostic capabilities and with minimal need for human intervention.
From early tumor detection to diagnosing diseases, to monitoring of disease progression and treatment of chronic disorders, these applications of machine learning in medical imaging are nothing short of transformational for the healthcare sector. And with the rapid development in AI technology, more health innovations and better patient treatment options are not too far off in the future.
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