Even prior to the pandemic, around 45% of radiologists struggled with burnout. This sad state of affairs was a result of an excessive administrative burden and the need to manually scan a large volume of medical images — up to a hundred per day. Furthermore, non-invasive tumor classification methods are not that common in radiology practice, while the invasive procedures cause stress and take up valuable time.
Fortunately, computer vision services and solutions can help hospitals better equip their staff. Here’s how computer vision in medical imaging can transform your healthcare facility.
How computer vision is revolutionizing the medical imaging field?
Computer vision is an artificial intelligence (AI)-based technology that enables systems to “see.” It allows computers to analyze visual input, such as videos and digital images, extract information, and take actions or give suggestions based on what they “see.”
In the medical imaging field, computer vision algorithms enable software applications to examine X-rays, MRI, CT scans, and other types of images to assist in clinical diagnosis. This technology can detect abnormalities in human organs, classify tumors, separate tissues from organs, and more.
Here are some of the main benefits of computer vision for the medical imaging field:
- Reducing the time needed for image analysis. Computer vision can automate some tedious time-consuming tasks, such as feature extraction and image segmentation.
- Increasing the accuracy of image analysis. AI-powered algorithms can spot tiny anomalies that could escape the human eye, thereby reducing human error.
- Facilitating medical research. This technology can analyze large volumes of medical images and extract specific information so that researchers don’t have to go through the heap themselves.
- Settling disputes. Computer vision can also offer a second opinion when doctors can’t come to a consensus.
5 key applications of computer vision in medical imaging
Here is what computer vision can do for medical imaging. Hire an AI development company to help you implement any of these use cases in your medical facility.
Classifying brain tumors
Previously, brain tumor patients and their surgeons were typically uncertain about the tumor type and the corresponding treatment options prior to surgery.
The standard analysis procedure involved removing a sample from the affected brain tissue and analyzing it. The pathologist could take up to 40 minutes to complete the procedure. During this time, the surgeon would be inactive until receiving the results, which would then require making a decision on the spot. By integrating computer vision into medical imaging, this process has been expedited to merely three minutes, which makes it possible to perform in the operating room without delaying the surgery.
In addition to increasing the speed, computer vision can increase the accuracy of the diagnosis. In one example, a research team built and tested a computer vision model based on common machine learning classifiers, such as support vector machine. The model could identify and classify brain tumors in MRI scans. After training the algorithm on over 15,000 medical images, it achieved the accuracy of 99.7%.
Improving the diagnosis of breast cancer
Research shows that doctors fail to detect 30-40% of breast cancers during scheduled screenings. There is also a large percentage of female patients who are diagnosed with breast lesions when they don’t actually have it. The news is devastating for these women and they go through unnecessary physical and emotional pain until additional screenings show their radiologist made a mistake.
Computer vision coupled with medical imaging can speed up and increase the accuracy of breast cancer diagnosis. For instance, scientists from Italy and Saudi Arabia used ML-based classifiers to detect and classify lesions. The team trained their algorithms on 7,900 images from the Breast Cancer Histopathological Imaging Dataset. The goal was to detect and classify cancer into benign and malignant cases. Their most successful classifier achieved the accuracy of 99.7%.
Spotting neurological abnormalities
AI can contribute to detecting neurological abnormalities in several ways. It can analyze speech patterns to accurately detect early manifestations of Alzheimer’s. Another way of detecting abnormalities is through ocular responses. For instance, AI algorithms can detect oculomotor dysfunction associated with autism.
Speaking of using computer vision to spot deformities in medical imaging, researchers experimented with using computer vision and 3D convolutional neural network algorithms to predict the progression of Alzheimer’s disease from MRI images. Researchers tested several classifiers and achieved the accuracy of 99% for some of them.
Identifying hidden fractures
In 2018, the FDA began approving AI-powered tools that can aid doctors in clinical decisions. Imagen’s OsteoDetect solution was among the first to be granted clearance. This software utilizes AI to search wrist scans for distal radius fractures. What really helped Imagen’s to receive the FDA’s approval was testing their tool on 1,000 wrist images. Additionally, 24 doctors who deployed the solution testified to its ability to aid in fracture detection, further bolstering confidence in OsteoDetect.
Another application of computer vision in medical imaging involves identifying hip fractures, a common injury among elderly people. Conventionally, medical practitioners use X-rays to identify such injuries. However, they can be difficult to detect as they may be obscured by soft tissues.
Researchers have been testing various methods of automated fracture detection and how it would compare to human doctors. For example, a team from the Khon Kaen University in Thailand trained computer vision models on 1000 hip and pelvic radiographs and evaluated their performance against human doctors. The models matched seasoned radiologists in their accuracy and outperformed first-year residents.
Supplying a second opinion in complex cases
When radiologists disagree on a challenging medical image, they can turn to computer vision for advice. This reduces the stress associated with the fear of making a wrong decision.
The New York-based Mount Sinai Health System utilized computer vision in medical imaging to read CT scans as a second opinion for identifying coronavirus, making them among the pioneers to deploy AI and medical imaging for spotting the virus. The researchers trained the model on 900 scans, and although CT scans are not the key means of detecting COVID-19 manifestation, the tool could detect mild signs of the disease that doctors failed to capture.
The future of computer vision-based medical imaging
Applying computer vision to medical imaging is unlikely to lead to the replacement of radiologists. While the AI is getting increasingly strong at performing specific tasks, human radiologists possess the expertise to handle complicated clinical issues that machines can’t deal with. Instead, computer vision will assist doctors by automating administrative tasks, like reporting, and offering support during decision making.
Radiologists’ education will need to adapt to integrate computer vision into their medical practice. This will also be valuable in developing countries where there is a shortage of radiologists and high-quality radiology equipment. The technology will allow regular physicians to perform initial diagnosis on X-ray films taken on their smartphones. However, integrating computer vision in medical imaging will raise concerns regarding regulatory requirements, reimbursement, and ethical considerations.
