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Introduction: Upper endoscopy is the standard method for diagnosing early-stage gastric cancer. However, according to estimates, up to 20% of tumors are not detected, and their accuracy may be affected by the variability in their performance. In Colombia, most diagnoses take place in advanced stages, which aggravates the problem. Protocols have been proposed to ensure the complete observation of areas prone to premalignant lesions to address variability. Objective: To build and validate an automatic audit system for endoscopies using artificial intelligence techniques. Methodology: In this study, 96 patients from a teaching hospital underwent video-documented endoscopies, spanning 22 stations rearranged to minimize overlaps and improve the identification of 13 key gastric regions. An advanced convolutional network was used to process the images, extracting visual characteristics, which facilitated the training of artificial intelligence in the classification of these areas. Results: the model, called Gastro UNAL, was trained and validated with images of 67 patients (70% of cases) and tested with 29 different patients (30% of cases), which reached an average sensitivity of 85,5% and a specificity of 98,8% in detecting the 13 gastric regions. Conclusions: The effectiveness of the model suggests its potential to ensure the quality and accuracy of endoscopies. This approach could confirm the regions evaluated, alerting less experienced or trained endoscopists about blind spots in the examinations, thus, increasing the quality of these procedures. [ABSTRACT FROM AUTHOR]

This paper presents a methodology developed to perform the processing, analysis, and characterization of AMI measurement data from the substations of three network operators of the Colombian electrical grid. This methodology includes the analysis of the data, which presents the sources of information used by the model, along with the preprocessing and exploratory analysis of the substations data. It also includes the formulation of the data reconstruction method, which uses a constrained optimization model to characterize the substations, based on the different behaviors of the end users of the Colombian electrical grid. In addition to the proposed methodology, the results of its application to the data provided by the operators are provided. These results show the capacity of the proposed methodology to adequately identify the most common behaviors of the users in a given area and characterize most of the energy demand profiles of each substation. [ABSTRACT FROM AUTHOR]

This study characterized water quality at sites of preference of the West Indian manatee (Thrichechus manatus) in the Cienaga of Paredes, an area recognized as of great ecological importance for the establishment of the species within the Middle Magdalena of Santander state (Colombia). From previous work and structured interviews eight locations were identified of which four sites were selected: Los Pozos, La Chorola and Quebrada La Gómez for greater sighting reports for shelter and food and the Punta del Roblar site for reproductive activities. The environmental quality of these sites was assessed using the physicochemical index ICA and biological BMWP / col both agree that the site with the worst water quality corresponds to the Quebrada Gomez with acceptable and very critical appraisals, respectively; the other sites were very similar to this assessment. The results of the analysis of repeated measures (ANOVA) showed no significant differences between these, but did find differences between seasons with the lowest value assigned to the last month of the year of sampling (ICA ̅X = 0.62 ± 0.15). Continuous monitoring of the environmental conditions of Manatee habitat is urgent, to allow us to obtain an adequate understanding of the relationship between ecosystem health and animal health, in order to establish management strategies that contribute to the conservation of the species at the regional scale. [ABSTRACT FROM AUTHOR]

This paper presents a hybrid classical-quantum program for density estimation and supervised classification. The program is implemented as a quantum circuit in a high-dimensional quantum computer simulator. We show that the proposed quantum protocols allow to estimate probability density functions and to make predictions in a supervised learning manner. This model can be generalized to find expected values of density matrices in high-dimensional quantum computers. Experiments on various data sets are presented. Results show that the proposed method is a viable strategy to implement supervised classification and density estimation in a high-dimensional quantum computer. [ABSTRACT FROM AUTHOR]

• An end-to-end deep learning-based method for automatic classification of B-scans inside a volume for three retinal diseases. • The proposed model includes a feedback stage that highlights the areas of the scans to support the interpretation of the results. This information is potentially useful for a medical specialist while assessing the prediction produced by the model. • The proposed model tested on SERI+CUHK data set with healthy, DME and DR-DME patients obtained a precision of 0,93 and an AUC of 0,86. On the A2A SD-OCT data set the model outperformed the state-of-the-art methods with an AUC of 0.99 for AMD diagnosis. Background and objectives: Spectral Domain Optical Coherence Tomography (SD-OCT) is a volumetric imaging technique that allows measuring patterns between layers such as small amounts of fluid. Since 2012, automatic medical image analysis performance has steadily increased through the use of deep learning models that automatically learn relevant features for specific tasks, instead of designing visual features manually. Nevertheless, providing insights and interpretation of the predictions made by the model is still a challenge. This paper describes a deep learning model able to detect medically interpretable information in relevant images from a volume to classify diabetes-related retinal diseases. Methods: This article presents a new deep learning model, OCT-NET, which is a customized convolutional neural network for processing scans extracted from optical coherence tomography volumes. OCT-NET is applied to the classification of three conditions seen in SD-OCT volumes. Additionally, the proposed model includes a feedback stage that highlights the areas of the scans to support the interpretation of the results. This information is potentially useful for a medical specialist while assessing the prediction produced by the model. Results: The proposed model was tested on the public SERI-CUHK and A2A SD-OCT data sets containing healthy, diabetic retinopathy, diabetic macular edema and age-related macular degeneration. The experimental evaluation shows that the proposed method outperforms conventional convolutional deep learning models from the state of the art reported on the SERI+CUHK and A2A SD-OCT data sets with a precision of 93% and an area under the ROC curve (AUC) of 0.99 respectively. Conclusions: The proposed method is able to classify the three studied retinal diseases with high accuracy. One advantage of the method is its ability to produce interpretable clinical information in the form of highlighting the regions of the image that most contribute to the classifier decision. [ABSTRACT FROM AUTHOR]

Purpose: Adaptive optics scanning light ophthalmoscope (AOSLO) imaging offers a microscopic view of the living retina, holding promise for diagnosing and researching eye diseases like retinitis pigmentosa and Stargardt's disease. The technology's clinical impact of AOSLO hinges on early detection through automated analysis tools., Methods: We introduce Cone Density Estimation (CoDE) and CoDE for Diagnosis (CoDED). CoDE is a deep density estimation model for cone counting that estimates a density function whose integral is equal to the number of cones. CoDED is an integration of CoDE with deep image classifiers for diagnosis. We use two AOSLO image datasets to train and evaluate the performance of cone density estimation and classification models for retinitis pigmentosa and Stargardt's disease., Results: Bland-Altman plots show that CoDE outperforms state-of-the-art models for cone density estimation. CoDED reported an F1 score of 0.770 ± 0.04 for disease classification, outperforming traditional convolutional networks., Conclusions: CoDE shows promise in classifying the retinitis pigmentosa and Stargardt's disease cases from a single AOSLO image. Our preliminary results suggest the potential role of analyzing patterns in the retinal cellular mosaic to aid in the diagnosis of genetic eye diseases., Translational Relevance: Our study explores the potential of deep density estimation models to aid in the analysis of AOSLO images. Although the initial results are encouraging, more research is needed to fully realize the potential of such methods in the treatment and study of genetic retinal pathologies.

Although for many diseases there is a progressive diagnosis scale, automatic analysis of grade-based medical images is quite often addressed as a binary classification problem, missing the finer distinction and intrinsic relation between the different possible stages or grades. Ordinal regression (or classification) considers the order of the values of the categorical labels and thus takes into account the order of grading scales used to assess the severity of different medical conditions. This paper presents a quantum-inspired deep probabilistic learning ordinal regression model for medical image diagnosis that takes advantage of the representational power of deep learning and the intrinsic ordinal information of disease stages. The method is evaluated on two different medical image analysis tasks: prostate cancer diagnosis and diabetic retinopathy grade estimation on eye fundus images. The experimental results show that the proposed method not only improves the diagnosis performance on the two tasks but also the interpretability of the results by quantifying the uncertainty of the predictions in comparison to conventional deep classification and regression architectures. The code and datasets are available at https://github.com/stoledoc/DQOR., (Copyright © 2022 Elsevier Ltd. All rights reserved.)

Neurodegenerative diseases comprise a wide variety of mental symptoms whose evolution is not directly related to the visual analysis made by radiologists, who can hardly quantify systematic differences. Moreover, automatic brain morphometric analyses, that do perform this quantification, contribute very little to the comprehension of the disease, i.e., many of these methods classify but they do not produce useful anatomo-functional correlations. This paper presents a new fully automatic image analysis method that reveals discriminative brain patterns associated to the presence of neurodegenerative diseases, mining systematic differences and therefore grading objectively any neurological disorder. This is accomplished by a fusion strategy that mixes together bottom-up and top-down information flows. Bottom-up information comes from a multiscale analysis of different image features, while the top-down stage includes learning and fusion strategies formulated as a max-margin multiple-kernel optimization problem. The capacity of finding discriminative anatomic patterns was evaluated using the Alzheimer's disease (AD) as the use case. The classification performance was assessed under different configurations of the proposed approach in two public brain magnetic resonance datasets (OASIS-MIRIAD) with patients diagnosed with AD, showing an improvement varying from 6.2% to 13% in the equal error rate measure, with respect to what has been reported by the feature-based morphometry strategy. In terms of the anatomical analysis, discriminant regions found by the proposed approach highly correlates to what has been reported in clinical studies of AD.