A Deep Learning Model to Predict a Diagnosis of Alzheimer Disease by Using 18F-FDG PET of the Brain, 2018, Ding et al

[Webdog]

Artificial intelligence predicts Alzheimer's years before diagnosis in small UCSF study.

Study:
A Deep Learning Model to Predict a Diagnosis of Alzheimer Disease by Using 18F-FDG PET of the Brain
https://pubs.rsna.org/doi/10.1148/radiol.2018180958

Press Release:
AI Predicts Alzheimers Years Before Diagnosis
Metabolic brain changes can be identified earlier leading to timely diagnosis and intervention.
https://www.rsna.org/en/news/2018/November-December/Artificial Intelligence Predicts Alzheimers Years Before Diagnosis

Artificial intelligence (AI) improves the ability of brain imaging to predict Alzheimer’s disease (AD), according to a study published in Radiology.

While timely diagnosis of AD is extremely important, early diagnosis has proven to be challenging. Research has linked the disease process to changes in metabolism, as shown by glucose uptake in certain regions of the brain, but these changes can be difficult to recognize.

“Differences in the pattern of glucose uptake in the brain are very subtle and diffuse,” said study co-author Jae Ho Sohn, MD, from the Radiology & Biomedical Imaging Department at the University of California in San Francisco (UCSF). “People are good at finding specific biomarkers of disease, but metabolic changes represent a more global and subtle process.”

 

[Hutan]

Sounds pretty good. A lot better than ME research that typically makes an algorithm based on 20 patients, doesn't test it on any independent data and then loudly proclaims a discovered biomarker.

The ADNI dataset included more than 2,100 FDG-PET brain images from 1,002 patients. Researchers trained the DL algorithm on 90 percent of the dataset and then tested it on the remaining 10 percent of the dataset.

Finally, the researchers tested the algorithm on an independent set of 40 imaging exams from 40 patients that it had never studied. The algorithm achieved 100 percent sensitivity at detecting the disease an average of more than six years prior to the final diagnosis.

“We were very pleased with the algorithm’s performance,” Dr. Sohn said. “It was able to predict every single case that advanced to Alzheimer’s disease.”