Simplifying Wound Care with AI-Computer Vision Techniques
By MedTech Outlook | Thursday, December 06, 2018
Infectious disease outbreak and intervention of AI and computer vision techniques in diagnosis have resulted in the healthcare sector resorting toward advanced technologies to ensure reliable operation with highly precise and better quality services with respect to patient expectations.
In today’s society, many are suffering from chronic wounds which have become a major healthcare burden—costing billions of dollars every year. Costs associated with the treatment are staggering. The annual expenditure for global wound care may be of $13 billion to $15 billion. Also, treatment driven with myriad factors may delay the wound healing process.
Healthcare service providers such as Swift Medical and Healthy.io are employing AI-driven technologies to automate their traditional diagnostic systems comprising manual and inaccurate measurement units with computer processing units. Instead of providing still image as the input, a video of the wound is taken and processed through machine learning and classification algorithms to automatically determine the type of wound, its length, width, and its present condition with in-depth analysis.
Kevin Keenahan, CEO of Tissue Analytics, said that an approach towards complete automation of diagnostic process helps medical sectors to gain better insight about the wound. With a single still image or a video, the entire nursing will be taken care of by AI and image processing systems. This approach can be difficult and may take some time for medical practitioners to get a better understanding, but its effectiveness and robust nature helps numerous patients to gain better treatment with reduced cost.
During the analysis, AI plays a critical role in analyzing the image with its deep learning strategy, and convolutional neural networks allow companies to train their algorithms faster compared to the existing techniques.
In AI design process, the overall procedure is divided into training and testing phases. A set of standard images determining the class of wound is processed at the initial stage to extract unique features from the wound dataset. In the testing phase, the test image comprising either wound video or image is processed and then compared with the extracted features to determine the type of wound.
Presently, there are numerous image processing techniques and deep learning algorithms such as artificial neural networks, support vector machines, and Naive Bayes algorithm, but it is still a challenging task for medical practitioners to analyze the present condition. AI techniques suffer from limitations such as performance, accuracy, and true positive rate. As researches still continue, these limitations should be considered as critical metrics and analysis should be carried on the same to enhance seamless workflow that is empathetic to the medical user in providing better healthcare.