Alzheimer’s disease (AD) diagnosis is one of the major research areas in computational medicine. Optical coherence tomography (OCT) is a non-invasive, inexpensive, and timely efficient method that scans the human’s retina with depth. It has been hypothesized that the thickness of the retinal layers extracted from OCTs could be an efficient and effective biomarker for early diagnosis of AD. In this work, we aim to design a self-training model architecture for the task of segmenting the retinal layers in OCT scans. Neural architecture search (NAS) is a subfield of AutoML domain, which has a significant impact on improving the accuracy of machine vision tasks. We integrate the NAS algorithm with a Unet auto-encoder architecture as its backbone. Then, we employ our proposed model to segment the retinal nerve fiber layer in our preprocessed OCT images with the aim of AD diagnosis. In this work, we trained a super-resolution generative adversarial network on the raw OCT scans to improve the quality of the images before the modeling stage. In our architecture search strategy, different primitive operations suggested to find down- \& up-sampling Unet cell blocks and the binary gate method has been applied to make the search strategy more practical. Our architecture search method is empirically evaluated by training on the Unet and NAS-Unet from scratch. Specifically, the proposed NAS-Unet training significantly outperforms the baseline human-designed architecture by achieving 95.1\% in the mean Intersection over Union metric and 79.1\% in the Dice similarity coefficient.

Dealing with opioid addiction and its long-term consequences is of great importance, as the addiction to opioids is emerged gradually, and established strongly in a given patient's body. Based on recent research, quitting the opioid requires clinicians to arrange a gradual plan for the patients who deal with the difficulties of overcoming addiction. This, in turn, necessitates observing the patients' wellness periodically, which is conventionally made by setting clinical appointments. However, this approach of dealing runs the risk of relapse for patients, as there would not be any monitoring between the clinical sessions. Thus, we need to increase the number of clinical appointments for opioid patients, which is not feasible due to the high financial costs, and the patients not having enough forbearance. Nevertheless, with the advent of wearable sensors continuous patient monitoring becomes possible. However, the data collected through the sensors is pervasively noisy, where using sensors with different sampling frequency challenges the data processing. In this work, we handle this problem by using 12-hour resolution data from cognitive tests, along with heart rate (HR) and heart rate variability (HRV), sampled at each 15 and 180 seconds, respectively. The proposed recipe enables us to interpret the multi-modal sensor data as a feature space, where we can predict the wellness of the opioid patients by employing extreme gradient boosting (XGBoost), which results in 96.12% average accuracy of prediction as the best achieved performance.