The Medical Futurist just published an article mentioning Resapp:
Diagnostic algorithms aiding pulmonologists
Digital health technologies can effectively aid the diagnosis and management of chronic respiratory diseases in many cases. In the case of asthma, it is widely accepted that current diagnostic tools should be greatly enhanced as the illness is still rather treated as one single disease instead of an umbrella term for many types of conditions. In the future, researchers will harness the power of artificial intelligence and create diagnostic algorithms that could transform the ability of non-specialist healthcare professionals to make an accurate diagnosis. Machine learning could be used for the analysis of breath sounds obtained from electronic stethoscopes, or even from smartphones, for the detection of wheezes and crackles, for the interpretation of pulmonary function test (PFT) scores, or for the analysis of bronchoscopy images, as well as X-rays and CT scans.
That’s already happening when it comes to lung cancer. Screening and early detection are one of the most important factors in connection with these ugly diseases, however, the current method of lung cancer detection has a 96 percent false-positive rate. Using machine learning for medical imaging, researchers at the University of Pittsburgh and UPMC Hillman Cancer Center have found a way to substantially reduce false positives without missing a single case of cancer. A study from Google and Northwestern Medicine also showed that an algorithm was able to detect malignant lung nodules on low-dose chest computed tomography (LDCT) scans on par with or even better than radiologists, demonstrating how A.I. could enhance the accuracy of early lung cancer diagnosis in the future. In another, European research, an algorithm for pattern recognition outperformed pulmonologists in the interpretation of PFTs. Plus, in yet another study, researchers created a machine learning algorithm to predict a patient’s risk for pulmonary embolism and may help improve the use of CT imaging for the condition.
Looking at all these studies, some might arrive at the conclusion that smart algorithms are not yet being used in clinical settings. But that’s not the case: GE Healthcare’s Critical Care Suite has recently got FDA-clearance and is ready to get into as many hospitals as demand requires. The algorithm automatically processes chest scans right on the X-ray machine and flags those where it detects potential signs of pneumothorax. The attending radiologist immediately gets a copy of the scan via the hospital’s PACS system and the technologist performing the scan is alerted as well, to help make sure that the patient is triaged properly.
In addition, companies such as Fluidda have been developing artificial intelligence tools to combine high-resolution CT scans with advanced Computational Fluid Dynamics (CFD) tools to help pulmonologists visualize both structural and functional parameters of the lungs, which can also aid the diagnostic process. Another example is the excellent smartphone app, ResApp, from Australia, which provides a reliable diagnostic test for respiratory diseases.
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