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Internal Medicine Internal Medicine

Machine Learning Analysis of the Bleomycin Mouse Model Reveals the Compartmental and Temporal Inflammatory Pulmonary Fingerprint.

In iScience

The bleomycin mouse model is the extensively used model to study pulmonary fibrosis; however, the inflammatory cell kinetics and their compartmentalization is still incompletely understood. Here we assembled historical flow cytometry data, totaling 303 samples and 16 inflammatory-cell populations, and applied advanced data modeling and machine learning methods to conclusively detail these kinetics. Three days post-bleomycin, the inflammatory profile was typified by acute innate inflammation, pronounced neutrophilia, especially of SiglecF+ neutrophils, and alveolar macrophage loss. Between 14 and 21 days, rapid responders were increasingly replaced by T and B cells and monocyte-derived alveolar macrophages. Multicolour imaging revealed the spatial-temporal cell distribution and the close association of T cells with deposited collagen. Unbiased immunophenotyping and data modeling exposed the dynamic shifts in immune-cell composition over the course of bleomycin-triggered lung injury. These results and workflow provide a reference point for future investigations and can easily be applied in the analysis of other datasets.

Bordag Natalie, Biasin Valentina, Schnoegl Diana, Valzano Francesco, Jandl Katharina, Nagy Bence M, Sharma Neha, Wygrecka Malgorzata, Kwapiszewska Grazyna, Marsh Leigh M


Artificial Intelligence, Immune Response, Immunology

Radiology Radiology

Artificial intelligence in gastrointestinal endoscopy.

In VideoGIE : an official video journal of the American Society for Gastrointestinal Endoscopy

Background and Aims : Artificial intelligence (AI)-based applications have transformed several industries and are widely used in various consumer products and services. In medicine, AI is primarily being used for image classification and natural language processing and has great potential to affect image-based specialties such as radiology, pathology, and gastroenterology (GE). This document reviews the reported applications of AI in GE, focusing on endoscopic image analysis.

Methods : The MEDLINE database was searched through May 2020 for relevant articles by using key words such as machine learning, deep learning, artificial intelligence, computer-aided diagnosis, convolutional neural networks, GI endoscopy, and endoscopic image analysis. References and citations of the retrieved articles were also evaluated to identify pertinent studies. The manuscript was drafted by 2 authors and reviewed in person by members of the American Society for Gastrointestinal Endoscopy Technology Committee and subsequently by the American Society for Gastrointestinal Endoscopy Governing Board.

Results : Deep learning techniques such as convolutional neural networks have been used in several areas of GI endoscopy, including colorectal polyp detection and classification, analysis of endoscopic images for diagnosis of Helicobacter pylori infection, detection and depth assessment of early gastric cancer, dysplasia in Barrett's esophagus, and detection of various abnormalities in wireless capsule endoscopy images.

Conclusions : The implementation of AI technologies across multiple GI endoscopic applications has the potential to transform clinical practice favorably and improve the efficiency and accuracy of current diagnostic methods.

Pannala Rahul, Krishnan Kumar, Melson Joshua, Parsi Mansour A, Schulman Allison R, Sullivan Shelby, Trikudanathan Guru, Trindade Arvind J, Watson Rabindra R, Maple John T, Lichtenstein David R


ADR, adenoma detection rate, AI, artificial intelligence, AMR, adenoma miss rate, ANN, artificial neural network, BE, Barrett’s esophagus, CAD, computer-aided diagnosis, CADe, CAD studies for colon polyp detection, CADx, CAD studies for colon polyp classification, CI, confidence interval, CNN, convolutional neural network, CRC, colorectal cancer, DL, deep learning, GI, gastroenterology, HD-WLE, high-definition white light endoscopy, HDWL, high-definition white light, ML, machine learning, NBI, narrow-band imaging, NPV, negative predictive value, PIVI, preservation and Incorporation of Valuable Endoscopic Innovations, SVM, support vector machine, VLE, volumetric laser endomicroscopy, WCE, wireless capsule endoscopy, WL, white light

General General

Identifying patterns in urban housing density in developing countries using convolutional networks and satellite imagery.

In Heliyon

The use of Deep Neural Networks for remote sensing scene image analysis is growing fast. Despite this, data sets on developing countries are conspicuously absent in the public domain for benchmarking machine learning algorithms, rendering existing data sets unrepresentative. Secondly, current literature uses low-level semantic scene image class definitions, which may not have many relevant applications in certain domains. To examine these problems, we applied Convolutional Neural Networks (CNN) to high-level scene image classification for identifying patterns in urban housing density in a developing country setting. An end-to-end model training workflow is proposed for this purpose. A method for quantifying spatial extent of urban housing classes which gives insight into settlement patterns is also proposed. The method consists of computing the ratio between area covered by a given housing class and total area occupied by all classes. In the current work this method is implemented based on grid count, whereby the number of predicted grids for one housing class is divided by the total grid count for all classes. Results from the proposed method were validated against building density data computed on OpenStreetMap data. Our results for scene image classification are comparable to current state-of-the-art, despite focusing only on most difficult classes in those works. We also contribute a new satellite scene image data set that captures some general characteristics of urban housing in developing countries. The data set has similar but also some distinct attributes to existing data sets.

Sanya Rahman, Mwebaze Ernest


Computer science, Convolutional neural networks, Developing countries, Housing classification, Satellite imagery, Urban areas

General General

A dataset for automatic violence detection in videos.

In Data in brief

The automatic detection of violence and crimes in videos is gaining attention, specifically as a tool to unburden security officers and authorities from the need to watch hours of footages to identify event lasting few seconds. So far, most of the available datasets was composed of few clips, in low resolution, often built on too specific cases (e.g. hockey fight). While high resolution datasets are emerging, there is still the need of datasets to test the robustness of violence detection techniques to false positives, due to behaviours which might resemble violent actions. To this end, we propose a dataset composed of 350 clips (MP4 video files, 1920 × 1080 pixels, 30 fps), labelled as non-violent (120 clips) when representing non-violent behaviours, and violent (230 clips) when representing violent behaviours. In particular, the non-violent clips include behaviours (hugs, claps, exulting, etc.) that can cause false positives in the violence detection task, due to fast movements and the similarity with violent behaviours. The clips were performed by non-professional actors, varying from 2 to 4 per clip.

Bianculli Miriana, Falcionelli Nicola, Sernani Paolo, Tomassini Selene, Contardo Paolo, Lombardi Mara, Dragoni Aldo Franco


Computer vision, Crime detection, Deep learning, Violence detection

General General

Helminth Egg Automatic Detector (HEAD): Improvements in development for digital identification and quantification of Helminth eggs and its application online.

In MethodsX

Conventional analytical techniques for evaluating Helminth eggs are based on different steps to concentrate them in a pellet for direct observation and quantification under a light microscope, which can generate under-counts or over-counts and be time consuming. To enhance this process, a new approach via automatic identification was implemented in which various image processing detectors were developed and incorporated into a Helminth Egg Automatic Detector (HEAD) system. This allowed the identification and quantification of pathogenic eggs of global medical importance. More than 2.6 billion people are currently affected and infected, and this results in approximately 80,000 child deaths each year. As a result, since 1980 the World Health Organization (WHO) has implemented guidelines, regulations and criteria for the control of the health risk. After the initial release of the analytical technique, two improvements were developed in the detector: first, a texture verification process that reduced the number of false positive results; and second, the establishment of the optimal thresholds for each species. In addition, the software was made available on a free platform. After performing an internal statistical verification of the system, testing with internationally recognized parasitology laboratories was carried out, Subsequently, the HEAD System is capable of identifying and quantifying different species of Helminth eggs in different environmental samples: wastewater, sludge, biosolids, excreta and soil, with in-service sensitivity and specificity values for the open library for machine learning TensorFlow (TF) model of 96.82% and 97.96% respectively. The current iteration uses AutoML Vision (a computer platform for the automatization of machine learning models, making it easier to train, optimize and export results to cloud applications or devices). It represents a useful and cheap tool that could be utilized by environmental monitoring facilities and laboratories around the world.•The HEAD Software will significantly reduce the costs associated with the detection and quantification of helminth eggs to a high level of accuracy.•It represents a tool, not only for microbiologists and researchers, but also for various agencies involved in sanitation, such as environmental regulation agencies, which currently require highly trained technicians.•The simplicity of the device contributes to the control the contamination of water, soil, and crops, even in poor and isolated communities.

Jiménez Blanca, Maya Catalina, Velásquez Gustavo, Barrios José Antonio, Pérez Mónica, Román Angélica


AutoML vision, Automatic identification, Environmental samples, Helminth eggs, Object characterization, Sensitivity, Specificity, TensorFlow

General General

Machine Learning Models for covid-19 future forecasting.

In Materials today. Proceedings

Computational methods for machine learning (ML) have shown their meaning for the projection of potential results for informed decisions. Machine learning algorithms have been applied for a long time in many applications requiring the detection of adverse risk factors. This study shows the ability to predict the number of individuals who are affected by the COVID-19[1] as a potential threat to human beings by ML modelling. In this analysis, the risk factors of COVID-19 were exponential smoothing (ES). The Lower Absolute Reductor and Selection Operator, (LASSo), Vector Assistance (SVM), four normal potential forecasts, such as Linear Regression (LR)). [2] Each of these machine-learning models has three distinct kinds of predictions: the number of newly infected COVID 19 people, mortality rates and the recovered COVID-19 estimates in the next 10 days. These approaches are better used in the latest COVID-19 situation, as shown by the findings of the analysis. The LR, that is effective in predicting new cases of corona, death numbers and recovery.

Mojjada Ramesh Kumar, Yadav Arvind, Prabhu A V, Natarajan Yuvaraj


COVID-19, R2 score adjusted, exponential process of smoothing, future forecasting, machine learning supervised