The intersection of video capsule endoscopy and artificial intelligence: addressing unique challenges using machine learning

Introduction: Technical burdens and time-intensive review processes limit the practical utility of video capsule endoscopy (VCE). Artificial intelligence (AI) is poised to address these limitations, but the intersection of AI and VCE reveals challenges that must first be overcome. We identified five challenges to address. Challenge #1: VCE data are stochastic and contains significant artifact. Challenge #2: VCE interpretation is cost-intensive. Challenge #3: VCE data are inherently imbalanced. Challenge #4: Existing VCE AIMLT are computationally cumbersome. Challenge #5: Clinicians are hesitant to accept AIMLT that cannot explain their process. Methods: An anatomic landmark detection model was used to test the application of convolutional neural networks (CNNs) to the task of classifying VCE data. We also created a tool that assists in expert annotation of VCE data. We then created more elaborate models using different approaches including a multi-frame approach, a CNN based on graph representation, and a few-shot approach based on meta-learning. Results: When used on full-length VCE footage, CNNs accurately identified anatomic landmarks (99.1%), with gradient weighted-class activation mapping showing the parts of each frame that the CNN used to make its decision. The graph CNN with weakly supervised learning (accuracy 89.9%, sensitivity of 91.1%), the few-shot model (accuracy 90.8%, precision 91.4%, sensitivity 90.9%), and the multi-frame model (accuracy 97.5%, precision 91.5%, sensitivity 94.8%) performed well. Discussion: Each of these five challenges is addressed, in part, by one of our AI-based models. Our goal of producing high performance using lightweight models that aim to improve clinician confidence was achieved

Pathobiome driven gut inflammation in Pakistani children with environmental enteric dysfunction

Environmental Enteric Dysfunction (EED) is an acquired small intestinal inflammatory condition underlying high rates of stunting in children \u3c5 years of age in low- and middle-income countries. Children with EED are known to have repeated exposures to enteropathogens and environmental toxins that leads to malabsorptive syndrome. We aimed to characterize association of linear growth faltering with enteropathogen burden and subsequent changes in EED biomarkers. In a longitudinal birth cohort (n = 272), monthly anthropometric measurements (Length for Age Z score- LAZ) of asymptomatic children were obtained up to 18 months. Biological samples were collected at 6 and 9 months for the assessment of biomarkers. A customized TaqMan array card was used to target 40 enteropathogens in fecal samples. Linear regression was applied to study the effect of specific enteropathogen infection on change in linear growth (ΔLAZ). Presence of any pathogen in fecal sample correlated with serum flagellin IgA (6 mo, r = 0.19, p = 0.002), fecal Reg 1b (6 mo, r = 0.16, p = 0.01; 9mo, r = 0.16, p = 0.008) and serum Reg 1b (6 mo, r = 0.26, p\u3c0.0001; 9 mo, r = 0.15, p = 0.008). At 6 months, presence of Campylobacter [β (SE) 7751.2 (2608.5), p = 0.003] and ETEC LT [β (SE) 7089.2 (3015.04), p = 0.019] was associated with increase in MPO. Giardia was associated with increase in Reg1b [β (SE) 72.189 (26.394), p = 0.006] and antiflic IgA[β (SE) 0.054 (0.021), p = 0.0091]. Multiple enteropathogen infections in early life negatively correlated with ΔLAZ, and simultaneous changes in gut inflammatory and permeability markers. A combination vaccine targeting enteropathogens in early life could help in the prevention of future stuntin

Deep Learning for Whole-Slide Tissue Histopathology Classification: A Comparative Study in the Identification of Dysplastic and Non-Dysplastic Barrett’s Esophagus

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

Artificial intelligence-based analytics for diagnosis of small bowel enteropathies and black box feature detection

Objectives: Striking histopathological overlap between distinct but related conditions poses a disease diagnostic challenge. There is a major clinical need to develop computational methods enabling clinicians to translate heterogeneous biomedical images into accurate and quantitative diagnostics. This need is particularly salient with small bowel enteropathies; Environmental Enteropathy (EE) and Celiac Disease (CD). We built upon our preliminary analysis by developing an artificial intelligence (AI)-based image analysis platform utilizing deep learning convolutional neural networks (CNNs) for these enteropathies.Methods: Data for secondary analysis was obtained from three primary studies at different sites. The image analysis platform for EE and CD was developed using convolutional neural networks (CNNs) including one with multi-zoom architecture. Gradient-weighted Class Activation Mappings (Grad-CAMs) were used to visualize the models\u27 decision making process for classifying each disease. A team of medical experts simultaneously reviewed the stain color normalized images done for bias reduction and Grad-CAMs to confirm structural preservation and biomedical relevance, respectively.Results: 461 high-resolution biopsy images from 150 children were acquired. Median age (interquartile range) was 37·5 (19·0 to 121·5) months with a roughly equal sex distribution; 77 males (51·3%). ResNet50 and Shallow CNN demonstrated 98% and 96% case-detection accuracy, respectively, which increased to 98·3% with an ensemble. Grad-CAMs demonstrated models\u27 ability to learn different microscopic morphological features for EE, CD, and controls.Conclusion: Our AI-based image analysis platform demonstrated high classification accuracy for small bowel enteropathies which was capable of identifying biologically relevant microscopic features and emulating human pathologist decision making process. Grad-CAMs illuminated the otherwise \u27black box\u27 of deep learning in medicine, allowing for increased physician confidence in adopting these new technologies in clinical practice

Prostate-Specific Antigen 5 Years following Stereotactic Body Radiation Therapy for Low- and Intermediate-Risk Prostate Cancer: An Ablative Procedure?

BackgroundOur previous work on early PSA kinetics following prostate stereotactic body radiation therapy (SBRT) demonstrated that an initial rapid and then slow PSA decline may result in very low PSA nadirs. This retrospective study sought to evaluate the PSA nadir 5 years following SBRT for low- and intermediate-risk prostate cancer (PCa).Methods65 low- and 80 intermediate-risk PCa patients were treated definitively with SBRT to 35–37.5 Gy in 5 fractions at Georgetown University Hospital between January 2008 and October 2011. Patients who received androgen deprivation therapy were excluded from this study. Biochemical relapse was defined as a PSA rise >2 ng/ml above the nadir and analyzed using the Kaplan–Meier method. The PSA nadir was defined as the lowest PSA value prior to biochemical relapse or as the lowest value recorded during follow-up. Prostate ablation was defined as a PSA nadir <0.2 ng/ml. Univariate logistic regression analysis was used to evaluate relevant variables on the likelihood of achieving a PSA nadir <0.2 ng/ml.ResultsThe median age at the start of SBRT was 72 years. These patients had a median prostate volume of 36 cc with a median 25% of total cores involved. At a median follow-up of 5.6 years, 86 and 37% of patients achieved a PSA nadir ≤0.5 and <0.2 ng/ml, respectively. The median time to PSA nadir was 36 months. Two low and seven intermediate risk patients experienced a biochemical relapse. Regardless of the PSA outcome, the median PSA nadir for all patients was 0.2 ng/ml. The 5-year biochemical relapse free survival (bRFS) rate for low- and intermediate-risk patients was 98.5 and 95%, respectively. Initial PSA (p = 0.024) and a lower testosterone at the time of the PSA nadir (p = 0.049) were found to be significant predictors of achieving a PSA nadir <0.2 ng/ml.ConclusionSBRT for low- and intermediate-risk PCa is a convenient treatment option with low PSA nadirs and a high rate of early bRFS. Fewer than 40% of patients, however, achieved an ablative PSA nadir. Thus, the role of further dose escalation is an area of active investigation

TGF-β expression data.

<p>TGF-β expression levels were not significantly different in healthy controls, CD patients, and EE patients (Ctl mean±SD = 162.7±66.3 pg/mL, CD = 190.4±87.8 pg/mL, EE = 193.9±180.4 pg/mL, p-value = 0.793).</p

SMAD protein expression data from healthy controls, celiac disease, and environmental enteropathy.

<p>A) Representative western blot example investigating SMAD7; p-SMAD2,3 expression (using ERK1/2 as a normalizing protein) from controls (Ctl), celiac disease (CD), and environmental enteropathy (EE) patients. Note that for p-SMAD2,3, patients 3 and 4 have a double band that controls lacked. This band was likely an artifact and consequently was not considered in the densitometric analyses. B) Elevated SMAD7 densitometry levels in EE compared to controls (Ctl mean±SD = 0.47±0.20 a.u., EE = 1.13±0.25 a.u., p-value<0.05). Corresponds to WB5 (see <a href="http://www.plosntds.org/article/info:doi/10.1371/journal.pntd.0006224#pntd.0006224.s006" target="_blank">S3 Table</a>). C) Elevated p-SMAD2,3 densitometry levels in EE compared to controls (Ctl mean±SD = 0.38±0.14 a.u., EE = 0.60±0.10 a.u., p-value<0.05). Corresponds to WB2 (see <a href="http://www.plosntds.org/article/info:doi/10.1371/journal.pntd.0006224#pntd.0006224.s006" target="_blank">S3 Table</a>). D) Elevated p-SMAD2,3 densitometry levels in EE and CD compared to controls, with significant differences only between EE and controls (p-value<0.05) (Ctl mean±SD = 0.34±0.12 a.u., CD = 0.87±0.36 a.u., EE = 0.97±0.11 a.u.). Corresponds to WB1 (see <a href="http://www.plosntds.org/article/info:doi/10.1371/journal.pntd.0006224#pntd.0006224.s006" target="_blank">S3 Table</a>).</p

Immunohistochemical staining of duodenal biopsies from EE subjects for SMAD proteins.

<p>A) Representative immunohistochemical (IHC) photomicrographs at 100x and 400x from an EE duodenal biopsy showing SMAD7 staining in <u>both</u> the epithelium (arrows) and lamina propria (arrowheads). B) Representative IHC photomicrographs at 100x and 400x from an EE duodenal biopsy showing p-SMAD3 staining in <u>only</u> the epithelium (arrows).</p