Application of Deep Learning to Pancreatic Cancer Detection: Lessons Learned From Our Initial Experience.
J Am Coll Radiol. 2019 Sep;16(9 Pt B):1338-1342. doi: 10.1016/j.jacr.2019.05.034.
Chu LC, Park S, Kawamoto S, Wang Y, Zhou Y, Shen W, Zhu Z, Xia Y, Xie L, Liu F, Yu Q, Fouladi DF, Shayesteh S, Zinreich E, Graves JS, Horton KM, Yuille AL, Hruban RH, Kinzler KW, Vogelstein B, Fishman EK.
Excitement has been steadily growing over the promise of artificial intelligence (AI) for radiology. Deep learning, a form of AI, uses training data and multiple layers of equations to develop a mathematical model that best fits the data . The model can make predictions on the basis of new data. These algorithms deliver the prospect of improved disease detection and disease prognostication. As radiologists face increased pressure to read more cases each day, deep learning and other forms of AI offer the potential to serve as a “second reader” to decrease misses and increase efficiency. AI can analyze thousands of images on a pixel-by-pixel level and is not susceptible to mistakes due to fatigue, interruptions, or satisfaction of search.
Read Full Article Here: https://doi.org/10.1016/j.jacr.2019.05.034