Image classification is central to the big data revolution in medicine. Improved information processing methods for diagnosis and classification of digital medical images have shown to be successful via deep learning approaches. As this field is explored, there are limitations to the performance of traditional supervised classifiers. This paper outlines an approach that is different from the current medical image classification tasks that view the issue as multi-class classification. We performed a hierarchical classification using our Hierarchical Medical Image classification (HMIC) approach. HMIC uses stacks of deep learning models to give particular comprehension at each level of the clinical picture hierarchy. For testing our performance, we use biopsy of the small bowel images that contain three categories in the parent level (Celiac Disease, Environmental Enteropathy, and histologically normal controls). For the child level, Celiac Disease Severity is classified into 4 classes (I, IIIa, IIIb, and IIIC).

The gold standard of histopathology for the diagnosis of Barrett’s esophagus (BE) is hindered by inter-observer variability among gastrointestinal pathologists. Deep learning-based approaches have shown promising results in the analysis of whole-slide tissue histopathology images (WSIs). We performed a comparative study to elucidate the characteristics and behaviors of different deep learning-based feature representation approaches for the WSI-based diagnosis of diseased esophageal architectures, namely, dysplastic and non-dysplastic BE. The results showed that if appropriate settings are chosen, the unsupervised feature representation approach is capable of extracting more relevant image features from WSIs to classify and locate the precursors of esophageal cancer compared to weakly supervised and fully supervised approaches.

The relationship between environmental factors and child health is not well understood in rural Pakistan. This study characterized the environmental factors related to the morbidity of acute respiratory infections (ARIs), diarrhea, and growth using geographical information systems (GIS) technology. Anthropometric, address and disease prevalence data were collected through the SEEM (Study of Environmental Enteropathy and Malnutrition) study in Matiari, Pakistan. Publicly available map data were used to compile coordinates of healthcare facilities. A Pearson correlation coefficient (r) was used to calculate the correlation between distance from healthcare facilities and participant growth and morbidity. Other continuous variables influencing these outcomes were analyzed using a random forest regression model. In this study of 416 children, we found that participants living closer to secondary hospitals had a lower prevalence of ARI (r = 0.154, p < 0.010) and diarrhea (r = 0.228, p < 0.001) as well as participants living closer to Maternal Health Centers (MHCs): ARI (r = 0.185, p < 0.002) and diarrhea (r = 0.223, p < 0.001) compared to those living near primary facilities. Our random forest model showed that distance has high variable importance in the context of disease prevalence. Our results indicated that participants closer to more basic healthcare facilities reported a higher prevalence of both diarrhea and ARI than those near more urban facilities, highlighting potential public policy gaps in ameliorating rural health.

Background: Breast cancer is the most common malignancy in women, affecting over 1.5 million women every year, which accounts for the highest number of cancer-related deaths in women globally. Hereditary breast cancer (HBC), an important subset of breast cancer, accounts for 5–10% of total cases. However, in Low Middle-Income Countries (LMICs), the population-specific risk of HBC in different ethnicities and the correlation with certain clinical characteristics remain unexplored. Methods: Retrospective chart review of patients who visited the HBC clinic and proceeded with multi-gene panel testing from May 2017 to April 2020. Descriptive and inferential statistics were used to analyze clinical characteristics of patients. Fisher’s exact, Pearson’s chi-squared tests and Logistic regression analysis were used for categorical variables and Wilcoxon rank-sum test were used for quantitative variables. For comparison between two independent groups, Mann-Whitney test was performed. Results were considered significant at a p value of < 0.05. Results: Out of 273 patients, 22% tested positive, 37% had a VUS and 41% had a negative genetic test result. Fifty-five percent of the positive patients had pathogenic variants in either BRCA1 or BRCA2, while the remaining positive results were attributed to other genes. Patients with a positive result had a younger age at diagnosis compared to those having a VUS and a negative result; median age 37.5 years, IQR (Interquartile range) (31.5–48). Additionally, patients with triple negative breast cancer (TNBC) were almost 3 times more likely to have a positive result (OR = 2.79, CI = 1.42–5.48 p = 0.003). Of all patients with positive results, 25% of patients had a negative family history of breast and/or related cancers. Conclusions: In our HBC clinic, we observed that our rate of positive results is comparable, yet at the higher end of the range which is reported in other populations. The importance of expanded, multi-gene panel testing is highlighted by the fact that almost half of the patients had pathogenic or likely pathogenic variants in genes other than BRCA1/2, and that our test positivity rate would have only been 12.8% if only BRCA1/2 testing was done. As the database expands and protocol-driven referrals are made across the country, our insight about the genetic architecture of HBC in our population will continue to increase.