Deep learning can diagnose lung cancers with high accuracy when accounting for uncertainty

While artificial intelligence used to be confined to science fiction, it is now part of our reality, found technologies from facial recognition to digital transcription and music recommendation systems. But using artificial intelligence for lifesaving purposes can still be risky.

Researchers at the University of Chicago Medicine are trying to mitigate this risk by developing algorithms that can use deep learning to determine whether images of cancerous tissue come from either of two lung cancers, with high certainty. This technology comes closer to being safe for use with patients because it also estimates how confident it is about its predictions.

“Artificial intelligence systems are great when they work, but you don’t always know when they start to fail. One of the important hurdles that we need to pass is how we make this safe for patients,” said James Dolezal, MD, who is a fellow in the Section of Hematology and Oncology at the UChicago Medicine and the first author of the study.

The research paper, “Uncertainty-informed deep learning models enable high-confidence predictions for digital histopathology,” was published in Nature CommunicationsAlexander Pearson, MD, PhD, Assistant Professor of Medicine, is the senior author on the study.

Estimating uncertainty improves confidence of algorithm’s predictions

The algorithm that Dolezal and his team developed is a type of artificial intelligence called a deep learning model, which can be given large sets of data and then learn to make ‘predictions’ about similar sets of data. The researchers input around 1,000 microscope slide images of lung cancer tissue from The Cancer Genome Atlas and the Clinical Proteomic Tumor Analysis Consortium. The tissue images were either from lung adenocarcinoma or squamous cell carcinoma of the lung, and the model was told which type of cancer each image represented. The model was then shown more images of lung cancer tissue from an additional set of data, and it applied what it had learned from its training to categorize each new image as belonging to one of the two cancer types. Finally, to determine their accuracy, the study authors compared the model’s predictions to the diagnoses that the images had been manually assigned in the dataset.

 

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(Originally published By Lily Burton  PhD candidate in Biochemistry and Molecular Biophysics pm 111/9/2022)