Prediction of osteoporosis using MRI and CT scans with unimodal and multimodal deep-learning models.

PURPOSE: Osteoporosis is the systematic degeneration of the human skeleton, with consequences ranging from a reduced quality of life to mortality. Therefore, the prediction of osteoporosis reduces risks and supports patients in taking precautions. Deep-learning and specific models achieve highly accurate results using different imaging modalities. The primary purpose of this research was to develop unimodal and multimodal deep-learning-based diagnostic models to predict bone mineral loss of the lumbar vertebrae using magnetic resonance (MR) and computed tomography (CT) imaging. METHODS: Patients who received both lumbar dual-energy X-ray absorptiometry (DEXA) and MRI (n = 120) or CT (n = 100) examinations were included in this study. Unimodal and multimodal convolutional neural networks (CNNs) with dual blocks were proposed to predict osteoporosis using lumbar vertebrae MR and CT examinations in separate and combined datasets. Bone mineral density values obtained by DEXA were used as reference data. The proposed models were compared with a CNN model and six benchmark pre-trained deep-learning models. RESULTS: The proposed unimodal model obtained 96.54%, 98.84%, and 96.76% balanced accuracy for MRI, CT, and combined datasets, respectively, while the multimodal model achieved 98.90% balanced accuracy in 5-fold cross-validation experiments. Furthermore, the models obtained 95.68%-97.91% accuracy with a hold-out validation dataset. In addition, comparative experiments demonstrated that the proposed models yielded superior results by providing more effective feature extraction in dual blocks to predict osteoporosis. CONCLUSION: This study demonstrated that osteoporosis was accurately predicted by the proposed models using both MR and CT images, and a multimodal approach improved the prediction of osteoporosis. With further research involving prospective studies with a larger number of patients, there may be an opportunity to implement these technologies into clinical practice.

BRCA Variations Risk Assessment in Breast Cancers Using Different Artificial Intelligence Models.

Artificial intelligence provides modelling on machines by simulating the human brain using learning and decision-making abilities. Early diagnosis is highly effective in reducing mortality in cancer. This study aimed to combine cancer-associated risk factors including genetic variations and design an artificial intelligence system for risk assessment. Data from a total of 268 breast cancer patients have been analysed for 16 different risk factors including genetic variant classifications. In total, 61 BRCA1, 128 BRCA2 and 11 both BRCA1 and BRCA2 genes associated breast cancer patients' data were used to train the system using Mamdani's Fuzzy Inference Method and Feed-Forward Neural Network Method as the model softwares on MATLAB. Sixteen different tests were performed on twelve different subjects who had not been introduced to the system before. The rates for neural network were 99.9% for training success, 99.6% for validation success and 99.7% for test success. Despite neural network's overall success was slightly higher than fuzzy logic accuracy, the results from developed systems were similar (99.9% and 95.5%, respectively). The developed models make predictions from a wider perspective using more risk factors including genetic variation data compared with similar studies in the literature. Overall, this artificial intelligence models present promising results for BRCA variations' risk assessment in breast cancers as well as a unique tool for personalized medicine software.

Developing an Online Portal for Determining the Genomic Signature of Archaic DNA that are Associated to Modern Human Genetic Diseases: A Meta-Analysis Study.

OBJECTIVE: Mutations or introgression can cause and rise adaptive alleles of which some can be beneficial. Archaic humans lived more than 200,000 years ago in Europe and Western Asia. They were adapted to the environment and pathogens that prevailed in these locations. It can therefore be thought that modern humans obtained significant immune advantage from the archaic alleles. MATERIALS AND METHODS: First, data were collected by meta-analysis from previously identified genetic diseases caused by alleles that were introgressed from archaics. Second, the in silico model portal (http://www.archaics2phenotype.xxx.edu.tr) was designed to trace the history of the Neanderthal allele. The portal also shows the current distribution of the genotypes of the selected alleles within different populations and correlates with the individuals phenotype. RESULTS: Our developed model provides a better understanding for the origin of genetic diseases or traits that are associated with the Neanderthal genome. CONCLUSION: The developed medicine model will help individuals and their populations to receive the best treatment. It also clarifies why there are differences in disease phenotypes in modern humans.