The demand for computer vision software is growing
Modern medicine employs over a dozen medical imaging techniques, from radiography and ultrasound to elastography and magnetic particle imaging. This ever-increasing amount of visual data types allows doctors to study their patients more closely than ever before, improving the accuracy of diagnoses and leading to better health outcomes.
On the other hand, healthcare providers now need to boost their image processing capacity to leverage the full potential of the data available to them. The growing load on human radiologists and other imaging experts makes it very difficult to make accurate conclusions on the basis of images and predict how a condition will develop.
This article examines the latest uses of AI-driven computer vision software to help automate the collection and analysis of medical images.
Computer vision solutions for cancer screening are being implemented in leading health systems across the globe, with results often surpassing those of experienced human pathologists. The statement holds true for different imaging techniques and different types of the disease, including lung cancer and skin cancer.
In combination with pre-trained machine learning algorithms, computer vision can be used to automate blood cell identification for more efficient diagnostics of tumors.
Even before any cuts are made, computer vision can help prepare a patient and collect valuable data for the surgeon at scale. For example, an innovative dental imaging solution enables dentists to save time and effort spent on identifying the model of a broken implant before replacement.
During surgery, critical decisions have to be made quickly and accurately. A deep-learning powered solution by Gauss Surgical estimates intraoperative and postoperative hemorrhage through image analysis of blood-stained sponges, suction machines, and surgical drapes. Multiple patient studies have shown the solution brings a 34% reduction in delayed interventions to control bleeding.
In highly complex interventions such as shoulder replacement surgery, real-time image processing gives surgeons the ability to precisely map the location of fine anatomical structures and tissues that are individual for each patient. This kind of data is absolutely essential for minimizing the time spent on the operating table and in recovery.
Reducing attrition rates
Rehabilitation programs and clinical trials face the issue of attrition or a certain number of participants who drop out without completing the process. Some population groups, e.g. recovering substance abusers, are particularly susceptible to attrition, but it may also be the result of neglect or poor memory.
While computer vision seems like an unlikely way out, a case now exists for a computer vision-based solution that carries out mobile monitoring of individuals who undergo treatment plans or take part in clinical trials. By reliably identifying the person and the medication they are taking, AiCure helps to improve the efficiency of rehabilitation and ensure the accuracy of pharmaceutical trials.
Medical equipment QC
For safe and effective medical manipulations, the quality of equipment and packaging needs to be impeccable. Computer vision QC doesn’t just automate the work that was previously carried out by human specialists — in many cases, it allows to introduce the levels of inspection that were not possible before.
Currently, computer vision-assisted QC is performed both during manufacturing and when the equipment reaches the hospital. The high precision, flexibility, and repeatability of such checks help to reduce product recalls, enforce compliance with industry regulations, and prevent grave complications or injury related to faulty medical devices.
The impact of computer vision-driven automation
The advances of computer vision and AI in healthcare are transforming clinical workflows and approaches to treatment. Besides highly accurate results and faster image analysis, over time computer vision software proves to be more cost-effective than analysis performed by humans. All of this makes such systems an appealing investment for healthcare providers.
Innovation in the field of medical imaging is also starting to affect employment, boosting the demand for tech-savvy middle- and lower-level medical personnel.