Digitization of Symbol-Denoted Blood Pressure Data From Intraoperative Paper Health Records in a Low-Middle-Income Country Using Deep Image Segmentation and Associated Postoperative Outcomes: A Feasibility Study.

BACKGROUND: In low-middle-income countries (LMICs), perioperative clinical information is almost universally collected on paper health records (PHRs). The lack of accessible digital databases limits LMICs in leveraging data to predict and improve patient outcomes after surgery. In this feasibility study, our aims were to: (1) determine the detection performance and prediction error of the U-Net deep image segmentation approach for digitization of hand-drawn blood pressure symbols from an image of the intraoperative PHRs and (2) evaluate the association between deep image segmentation-derived blood pressure parameters and postoperative mortality and length of stay. METHODS: A smartphone mHealth platform developed by our team was used to capture images of completed intraoperative PHRs. A 2-stage deep image segmentation modeling approach was used to create 2 separate segmentation masks for systolic blood pressure (SBP) and diastolic blood pressure (DBP). Iterative postprocessing was utilized to convert the segmentation mask results into numerical SBP and DBP values. Detection performance and prediction errors were evaluated for the U-Net models by comparison with ground-truth values. Using multivariate regression analysis, we investigated the association of deep image segmentation-derived blood pressure values, total time spent in predefined blood pressure ranges, and postoperative outcomes including in-hospital mortality and length of stay. RESULTS: A total of 350 intraoperative PHRs were imaged following surgery. Overall accuracy was 0.839 and 0.911 for SBP and DBP symbol detections, respectively. The mean error rate and standard deviation for the difference between the actual and predicted blood pressure values were 2.1 ± 4.9 and -0.8 ± 3.9 mm Hg for SBP and DBP, respectively. Using the U-Net model-derived blood pressures, minutes of time where DBP <50 mm Hg (odds ratio [OR], 1.03; CI, 1.01-1.05; P = .003) was associated with an increased in-hospital mortality. In addition, increased cumulative minutes of time with SBP between 80 and 90 mm Hg was significantly associated with a longer length of stay (incidence rate ratio, 1.02 [1.0-1.03]; P < .05), while increased cumulative minutes of time where SBP between 140 and 160 mm Hg was associated with a shorter length of stay (incidence rate ratio, 0.9 [0.96-0.99]; P < .05). CONCLUSIONS: In this study, we report our experience with a deep image segmentation model for digitization of symbol-denoted blood pressure from intraoperative anesthesia PHRs. Our data support further development of this novel approach to digitize PHRs from LMICs, to provide accessible, curated, and reproducible data for both quality improvement- and outcome-based research.

Machine Learning Predictive Outcomes Modeling in Inflammatory Bowel Diseases.

There is a rising interest in use of big data approaches to personalize treatment of inflammatory bowel diseases (IBDs) and to predict and prevent outcomes such as disease flares and therapeutic nonresponse. Machine learning (ML) provides an avenue to identify and quantify features across vast quantities of data to produce novel insights in disease management. In this review, we cover current approaches in ML-driven predictive outcomes modeling for IBD and relate how advances in other fields of medicine may be applied to improve future IBD predictive models. Numerous studies have incorporated clinical, laboratory, or omics data to predict significant outcomes in IBD, including hospitalizations, outpatient corticosteroid use, biologic response, and refractory disease after colectomy, among others, with considerable health care dollars saved as a result. Encouraging results in other fields of medicine support efforts to use ML image analysis-including analysis of histopathology, endoscopy, and radiology-to further advance outcome predictions in IBD. Though obstacles to clinical implementation include technical barriers, bias within data sets, and incongruence between limited data sets preventing model validation in larger cohorts, ML-predictive analytics have the potential to transform the clinical management of IBD. Future directions include the development of models that synthesize all aforementioned approaches to produce more robust predictive metrics.

Distance from Healthcare Facilities Is Associated with Increased Morbidity of Acute Infection in Pediatric Patients in Matiari, Pakistan.

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.

Self-Attentive Adversarial Stain Normalization.

Hematoxylin and Eosin (H&E) stained Whole Slide Images (WSIs) are utilized for biopsy visualization-based diagnostic and prognostic assessment of diseases. Variation in the H&E staining process across different lab sites can lead to significant variations in biopsy image appearance. These variations introduce an undesirable bias when the slides are examined by pathologists or used for training deep learning models. Traditionally proposed stain normalization and color augmentation strategies can handle the human level bias. But deep learning models can easily disentangle the linear transformation used in these approaches, resulting in undesirable bias and lack of generalization. To handle these limitations, we propose a Self-Attentive Adversarial Stain Normalization (SAASN) approach for the normalization of multiple stain appearances to a common domain. This unsupervised generative adversarial approach includes self-attention mechanism for synthesizing images with finer detail while preserving the structural consistency of the biopsy features during translation. SAASN demonstrates consistent and superior performance compared to other popular stain normalization techniques on H&E stained duodenal biopsy image data.

Advancing Eosinophilic Esophagitis Diagnosis and Phenotype Assessment with Deep Learning Computer Vision.

Eosinophilic Esophagitis (EoE) is an inflammatory esophageal disease which is increasing in prevalence. The diagnostic gold-standard involves manual review of a patient's biopsy tissue sample by a clinical pathologist for the presence of 15 or greater eosinophils within a single high-power field (400× magnification). Diagnosing EoE can be a cumbersome process with added difficulty for assessing the severity and progression of disease. We propose an automated approach for quantifying eosinophils using deep image segmentation. A U-Net model and post-processing system are applied to generate eosinophil-based statistics that can diagnose EoE as well as describe disease severity and progression. These statistics are captured in biopsies at the initial EoE diagnosis and are then compared with patient metadata: clinical and treatment phenotypes. The goal is to find linkages that could potentially guide treatment plans for new patients at their initial disease diagnosis. A deep image classification model is further applied to discover features other than eosinophils that can be used to diagnose EoE. This is the first study to utilize a deep learning computer vision approach for EoE diagnosis and to provide an automated process for tracking disease severity and progression.

HMIC: Hierarchical Medical Image Classification, A Deep Learning Approach.

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).

Diagnosis of Celiac Disease and Environmental Enteropathy on Biopsy Images Using Color Balancing on Convolutional Neural Networks.

Celiac Disease (CD) and Environmental Enteropathy (EE) are common causes of malnutrition and adversely impact normal childhood development. CD is an autoimmune disorder that is prevalent worldwide and is caused by an increased sensitivity to gluten. Gluten exposure destructs the small intestinal epithelial barrier, resulting in nutrient mal-absorption and childhood under-nutrition. EE also results in barrier dysfunction but is thought to be caused by an increased vulnerability to infections. EE has been implicated as the predominant cause of under-nutrition, oral vaccine failure, and impaired cognitive development in low-and-middle-income countries. Both conditions require a tissue biopsy for diagnosis, and a major challenge of interpreting clinical biopsy images to differentiate between these gastrointestinal diseases is striking histopathologic overlap between them. In the current study, we propose a convolutional neural network (CNN) to classify duodenal biopsy images from subjects with CD, EE, and healthy controls. We evaluated the performance of our proposed model using a large cohort containing 1000 biopsy images. Our evaluations show that the proposed model achieves an area under ROC of 0.99, 1.00, and 0.97 for CD, EE, and healthy controls, respectively. These results demonstrate the discriminative power of the proposed model in duodenal biopsies classification.