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General General

An image processing method for recognition of four aquatic macroinvertebrates genera in freshwater environments in the Andean region of Colombia.

In Environmental monitoring and assessment

The aquatic macroinvertebrate community reflects the ecological status of a river. Typically, some extraction methods have been implemented, but the capture and preservation of organisms are necessary. The techniques of digital image processing applied to ecology have become innovative tools for the characterization of aquatic macroinvertebrates. This research implements a methodology for the processing and classification of four aquatic macroinvertebrates genera Thraulodes, Traverella (Ephemeroptera), Anacroneuria (Plecoptera), and Smicridea (Trichoptera) present in three rivers in Antioquia (Colombia), which includes two phases. The first of these was the collection and capture of organisms to obtain a database of the most abundant genera, at laboratory scale. The second was the use of simulations that allow the classification of data through a process of selection and extraction of characteristics using the bag of visual words technique. Of all the classifiers tested, Gaussian vector support machines obtained a percentage of success in the recognition up method of four organisms to the genus level of 97.1 %. The training and computational processing for classification enabled the standardization of an appropriate methodology that will serve as a starting point for aquatic biomonitoring and inventory in Colombia and internationally.

Serna López Juan Pablo, Fernández Mc Cann David Stephen, Vélez Macías Fabio de Jesús, Aguirre Ramírez Néstor Jaime


Aquatic macroinvertebrates, Digital image processing, Machine learning, Vector support machines, Water quality monitoring

Cardiology Cardiology

Prediction of revascularization by coronary CT angiography using a machine learning ischemia risk score.

In European radiology ; h5-index 62.0

OBJECTIVES : The machine learning ischemia risk score (ML-IRS) is a machine learning-based algorithm designed to identify hemodynamically significant coronary disease using quantitative coronary computed tomography angiography (CCTA). The purpose of this study was to examine whether the ML-IRS can predict revascularization in patients referred for invasive coronary angiography (ICA) after CCTA.

METHODS : This study was a post hoc analysis of a prospective dual-center registry of sequential patients undergoing CCTA followed by ICA within 3 months, referred from inpatient, outpatient, and emergency department settings (n = 352, age 63 ± 10 years, 68% male). The primary outcome was revascularization by either percutaneous coronary revascularization or coronary artery bypass grafting. Blinded readers performed semi-automated quantitative coronary plaque analysis. The ML-IRS was automatically computed. Relationships between clinical risk factors, coronary plaque features, and ML-IRS with revascularization were examined.

RESULTS : The study cohort consisted of 352 subjects with 1056 analyzable vessels. The ML-IRS ranged between 0 and 81% with a median of 18.7% (6.4-34.8). Revascularization was performed in 26% of vessels. Vessels receiving revascularization had higher ML-IRS (33.6% (21.1-55.0) versus 13.0% (4.5-29.1), p < 0.0001), as well as higher contrast density difference, and total, non-calcified, calcified, and low-density plaque burden. ML-IRS, when added to a traditional risk model based on clinical data and stenosis to predict revascularization, resulted in increased area under the curve from 0.69 (95% CI: 0.65-0.72) to 0.78 (95% CI: 0.75-0.81) (p < 0.0001), with an overall continuous net reclassification improvement of 0.636 (95% CI: 0.503-0.769; p < 0.0001).

CONCLUSIONS : ML-IRS from quantitative coronary CT angiography improved the prediction of future revascularization and can potentially identify patients likely to receive revascularization if referred to cardiac catheterization.

KEY POINTS : • Machine learning ischemia risk from quantitative coronary CT angiography was significantly higher in patients who received revascularization versus those who did not receive revascularization. • The machine learning ischemia risk score was significantly higher in patients with invasive fractional flow ≤ 0.8 versus those with > 0.8. • The machine learning ischemia risk score improved the prediction of future revascularization significantly when added to a standard prediction model including stenosis.

Kwan Alan C, McElhinney Priscilla A, Tamarappoo Balaji K, Cadet Sebastien, Hurtado Cecilia, Miller Robert J H, Han Donghee, Otaki Yuka, Eisenberg Evann, Ebinger Joseph E, Slomka Piotr J, Cheng Victor Y, Berman Daniel S, Dey Damini


Artificial intelligence, Cardiac catheterization, Coronary CT angiography, Coronary revascularization, Machine learning

Surgery Surgery

A web-based machine-learning algorithm predicting postoperative acute kidney injury after total knee arthroplasty.

In Knee surgery, sports traumatology, arthroscopy : official journal of the ESSKA

PURPOSE : Acute kidney injury (AKI) is a deleterious complication after total knee arthroplasty (TKA). The purposes of this study were to identify preoperative risk factors and develop a web-based prediction model for postoperative AKI, and assess how AKI affected the progression to ESRD.

METHOD : The study included 5757 patients treated in three tertiary teaching hospitals. The model was developed using data on 5302 patients from two hospitals and externally validated in 455 patients from the third hospital. Eighteen preoperative variables were collected and feature selection was performed. A gradient boosting machine (GBM) was used to predict AKI. A tenfold-stratified area under the curve (AUC) served as the metric for internal validation. Calibration was performed via isotonic regression and evaluated using a calibration plot. End-stage renal disease (ESRD) was followed up for an average of 41.7 months.

RESULTS : AKI develops in up to 10% of patients undergoing TKA, increasing the risk of progression to ESRD. The ESRD odds ratio of AKI patients (compared to non-AKI patients) was 9.8 (95% confidence interval 4.3-22.4). Six key predictors of postoperative AKI were selected: higher preoperative levels of creatinine in serum, the use of general anesthesia, male sex, a higher ASA class (> 3), use of a renin-angiotensin-aldosterone system inhibitor, and no use of tranexamic acid (all p < 0.001). The predictive performance of our model was good (area under the curve 0.78 [95% CI 0.74-0.81] in the developmental cohort and improved in the external validation cohort (0.89). Our model can be accessed at .

CONCLUSIONS : A web-based predictive model for AKI after TKA was developed using a machine-learning algorithm featuring six preoperative variables. The model is simple and has been validated to improve both short- and long-term prognoses of TKA patients. Postoperative AKI may lead to ESRD, which surgeons should strive to avoid.

LEVEL OF EVIDENCE : Diagnostic level II.

Ko Sunho, Jo Changwung, Chang Chong Bum, Lee Yong Seuk, Moon Young-Wan, Youm Jae Woo, Han Hyuk-Soo, Lee Myung Chul, Lee Hajeong, Ro Du Hyun


Acute kidney injury, End-stage renal disease, Machine learning, Prediction, Total knee arthroplasty, Total knee replacement

Ophthalmology Ophthalmology

Cost-effectiveness of Autonomous Point-of-Care Diabetic Retinopathy Screening for Pediatric Patients With Diabetes.

In JAMA ophthalmology ; h5-index 58.0

Importance : Screening for diabetic retinopathy is recommended for children with type 1 diabetes (T1D) and type 2 diabetes (T2D), yet screening rates remain low. Point-of-care diabetic retinopathy screening using autonomous artificial intelligence (AI) has become available, providing immediate results in the clinic setting, but the cost-effectiveness of this strategy compared with standard examination is unknown.

Objective : To assess the cost-effectiveness of detecting and treating diabetic retinopathy and its sequelae among children with T1D and T2D using AI diabetic retinopathy screening vs standard screening by an eye care professional (ECP).

Design, Setting, and Participants : In this economic evaluation, parameter estimates were obtained from the literature from 1994 to 2019 and assessed from March 2019 to January 2020. Parameters included out-of-pocket cost for autonomous AI screening, ophthalmology visits, and treating diabetic retinopathy; probability of undergoing standard retinal examination; relative odds of undergoing screening; and sensitivity, specificity, and diagnosability of the ECP screening examination and autonomous AI screening.

Main Outcomes and Measures : Costs or savings to the patient based on mean patient payment for diabetic retinopathy screening examination and cost-effectiveness based on costs or savings associated with the number of true-positive results identified by diabetic retinopathy screening.

Results : In this study, the expected true-positive proportions for standard ophthalmologic screening by an ECP were 0.006 for T1D and 0.01 for T2D, and the expected true-positive proportions for autonomous AI were 0.03 for T1D and 0.04 for T2D. The base case scenario of 20% adherence estimated that use of autonomous AI would result in a higher mean patient payment ($8.52 for T1D and $10.85 for T2D) than conventional ECP screening ($7.91 for T1D and $8.20 for T2D). However, autonomous AI screening was the preferred strategy when at least 23% of patients adhered to diabetic retinopathy screening.

Conclusions and Relevance : These results suggest that point-of-care diabetic retinopathy screening using autonomous AI systems is effective and cost saving for children with diabetes and their caregivers at recommended adherence rates.

Wolf Risa M, Channa Roomasa, Abramoff Michael D, Lehmann Harold P


General General

Investigating Core Signaling Pathways of Hepatitis B Virus Pathogenesis for Biomarkers Identification and Drug Discovery via Systems Biology and Deep Learning Method.

In Biomedicines

Hepatitis B Virus (HBV) infection is a major cause of morbidity and mortality worldwide. However, poor understanding of its pathogenesis often gives rise to intractable immune escape and prognosis recurrence. Thus, a valid systematic approach based on big data mining and genome-wide RNA-seq data is imperative to further investigate the pathogenetic mechanism and identify biomarkers for drug design. In this study, systems biology method was applied to trim false positives from the host/pathogen genetic and epigenetic interaction network (HPI-GEN) under HBV infection by two-side RNA-seq data. Then, via the principal network projection (PNP) approach and the annotation of KEGG (Kyoto Encyclopedia of Genes and Genomes) pathways, significant biomarkers related to cellular dysfunctions were identified from the core cross-talk signaling pathways as drug targets. Further, based on the pre-trained deep learning-based drug-target interaction (DTI) model and the validated pharmacological properties from databases, i.e., drug regulation ability, toxicity, and sensitivity, a combination of promising multi-target drugs was designed as a multiple-molecule drug to create more possibility for the treatment of HBV infection. Therefore, with the proposed systems medicine discovery and repositioning procedure, we not only shed light on the etiologic mechanism during HBV infection but also efficiently provided a potential drug combination for therapeutic treatment of Hepatitis B.

Chang Shen, Wang Lily Hui-Ching, Chen Bor-Sen


deep learning, drug-target interaction (DTI) model, hepatitis B virus infection, host/pathogen interspecies genetic and epigenetic network (HPI-GEN), multiple-molecule drug, pathogenesis, systems medicine discovery

General General

New technologies and Amyotrophic Lateral Sclerosis - Which step forward rushed by the COVID-19 pandemic?

In Journal of the neurological sciences

Amyotrophic Lateral Sclerosis (ALS) is a fast-progressive neurodegenerative disease leading to progressive physical immobility with usually normal or mild cognitive and/or behavioural involvement. Many patients are relatively young, instructed, sensitive to new technologies, and professionally active when developing the first symptoms. Older patients usually require more time, encouragement, reinforcement and a closer support but, nevertheless, selecting user-friendly devices, provided earlier in the course of the disease, and engaging motivated carers may overcome many technological barriers. ALS may be considered a model for neurodegenerative diseases to further develop and test new technologies. From multidisciplinary teleconsults to telemonitoring of the respiratory function, telemedicine has the potentiality to embrace other fields, including nutrition, physical mobility, and the interaction with the environment. Brain-computer interfaces and eye tracking expanded the field of augmentative and alternative communication in ALS but their potentialities go beyond communication, to cognition and robotics. Virtual reality and different forms of artificial intelligence present further interesting possibilities that deserve to be investigated. COVID-19 pandemic is an unprecedented opportunity to speed up the development and implementation of new technologies in clinical practice, improving the daily living of both ALS patients and carers. The present work reviews the current technologies for ALS patients already in place or being under evaluation with published publications, prompted by the COVID-19 pandemic.

Pinto Susana, Quintarelli Stefano, Silani Vincenzo


Amyotrophic lateral sclerosis, Artificial intelligence, Brain-computer interfaces, COVID-19, Eye-tracking, Robotics, Telemedicine, Virtual reality