Artificial Intelligence Improves Brain Tumor Diagnosis
Wednesday, 01 08 2020, Category: Health, Country: World
Neurosurgeons can leave the operating room more confident today than ever before about their patient’s brain tumor diagnosis, thanks to integration of a new system that will allow them to quickly see diagnostic tissue and tumor margins in near-real time.
And the accuracy and precision will only continue to advance as they work toward incorporating deep learning and computer vision to make the whole process quicker, too, surgeons at Michigan Medicine say. In the operating room, faster also means more affordable.
Todd Hollon, M.D., a chief neurosurgical resident at Michigan Medicine, describes a two-part approach to improving intraoperative diagnostic accuracy and efficiency in a new publication in Nature Medicine.
Hollon, along with Daniel Orringer, M.D., an associate professor of neurosurgery at NYU Langone Health, and colleagues, report the most recent application of a technique called stimulated Raman histology (SRH), developed at Michigan Medicine to rapidly generate images of tumor tissue at the bedside. This means neuropathologists can review the images without the need for a pathology lab, eliminating the long wait time needed for traditional processing, staining and interpretation.
The researchers also used an artificial intelligence algorithm called a deep convolutional neural network to learn the characteristics of the 10 most common types of brain cancer and predict diagnosis. Surgeons are provided with a diagnostic prediction within minutes at the bedside with accuracy comparable to that of the conventional method.