You with Alzheimer’s 6 years from now?

>>> tic, toc, time’s up… /n

Alzheimer’s is the most common type of dementia, a set of brain disorders that result in the loss of brain function. To throw some statistics highlighting the problem we face, 1 in 3 UK citizens will develop dementia during their lifetime where there is a 62% chance it will be Alzheimer’s, and it is the 6^th leading cause of death in the USA.

The problem is that it is a multi-factorial disease as there are many factors influencing its development, i.e. reactive oxygen species, plaque aggregation, and protein malfunction. But these are just the tip of the iceberg, as at the heart of these activities leading to Alzheimer’s, there is a dysregulation (dyshomeostasis) of key biological transition metals such as Cu²⁺ and Zn ²⁺  that are vital to maintaining regular brain function and preventing dementia.  These factors contribute to the fact that there is no cure, and thus we are in competition against the clock to try and diagnose it as fast as possible to slow its progress.

Alzheimer’s (left) versus normal brain (right). Source.

Radiologist use Positron Emission Tomography (PET) scans to try and detect Alzheimer’s. PET allows the monitoring of molecular events as the disease evolves through the detection of positron emission using radioactive isotopes such as ¹⁸F. This isotope is attached to a version of glucose(¹⁸F-FDG), as glucose is the primary source of energy for brain cells, allowing their visualization. As brain cells become diseased, the amount of glucose decreases compared to normal brain cells. To aid in the war against time, Dr. Jae Ho Sohn combined machine learning with neuroimaging in the following article.

“One of the difficulties of Alzheimer’s disease is that by the time all the clinical symptoms manifest and we can make a definitive diagnosis, too many neurons have died, making it essentially irreversible. “

–Jae Ho Sohn, MD,MS 

 

Debriefing the Article “A Deep Learning Model to Predict a Diagnosis  of Alzheimer’s Disease by Using ¹⁸F-FDG PET of the Brain” by Sohn et al.  

Objective. To develop a deep learning algorithm to forecast the diagnosis of Alzheimer’s disease (AD), mild cognitive impairment (MCI), or neither (non-AD/MCI)  of patients undergoing ¹⁸F-FDG PET brain imaging, and compare the results with that of conventional radiologic readers.

Reasoning. Due to the inefficacy of humans to detect slow, global imaging changes, and the awareness that deep learning may help address the complexity of imaging data as deep learning has been applied to help the detection of breast cancer using mammography, pulmonary nodule using CT, and hip osteoarthritis using radiography.

Methodology.  Sohn et al. trained the convolutional neural network of Inception V3 architecture using 90% (1921 imaging studies, 899 patients) of the total imaging studies from patients who had either AD, MCI, or neither enrolled in the Alzheimer’s Disease Neuroimaging Initiative (ADNI). This trained algorithm was then used for testing on the remaining 10% (188 imaging studies, 103 patients) of the ADNI images (labelled ADNI test set) , and on an independent set from 40 patients not in ADNI. To further asses the proficiency of this method, results from the trained algorithm were compared to radiological readers.

Results. The algorithm was able to predict with high ability those patients who were diagnosed with AD ( 92% in ADNI test set and 98% in the independent test set),  with MCI ( 63% in ADNI test set and 52% in the independent test set), and with non-AD/MCI (73% in ADNI test set and 84% in the independent test set). It outperformed three radiology readers in ROC space in forecasting the final AD diagnosis.

Limitations. The independent test data was small (n=40), not from a clinical trial, and also excluded data from patients with non-AD neurodegenerative cases and disorders like stroke that can affect memory function. The training of the algorithm was solely based on ADNI information and thus is limited by the ADNI patient population, which did not include patients with non-AD neurodegenerative diseases. The algorithm performed its predictions distinctly from human expert approaches, and the MCI and non-AD/MCI were unstable compared to AD diagnosis and their accuracy depends on the follow up time.

Conclusion. The trained deep learning algorithm using ¹⁸F-FDG PET images achieved 82% specificity with 100% sensitivity in predicting AD specifically, an average of 75.8 months(~6 years) before final diagnosis. It has the potential to diagnose Alzheimer’s 6 years in advance at the clinic, but further validation and analysis is needed per mentioned limitations.