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Radiology Radiology

AI-Enhanced Diagnosis of Challenging Lesions in Breast MRI: A Methodology and Application Primer.

In Journal of magnetic resonance imaging : JMRI

Computer-aided diagnosis (CAD) systems have become an important tool in the assessment of breast tumors with magnetic resonance imaging (MRI). CAD systems can be used for the detection and diagnosis of breast tumors as a "second opinion" review complementing the radiologist's review. CAD systems have many common parts, such as image preprocessing, tumor feature extraction, and data classification that are mostly based on machine-learning (ML) techniques. In this review article, we describe applications of ML-based CAD systems in MRI covering the detection of diagnostically challenging lesions of the breast such as nonmass enhancing (NME) lesions, and furthermore discuss how multiparametric MRI and radiomics can be applied to the study of NME, including prediction of response to neoadjuvant chemotherapy (NAC). Since ML has been widely used in the medical imaging community, we provide an overview about the state-of-the-art and novel techniques applied as classifiers to CAD systems. The differences in the CAD systems in MRI of the breast for several standard and novel applications for NME are explained in detail to provide important examples, illustrating: 1) CAD for detection and diagnosis, 2) CAD in multiparametric imaging, 3) CAD in NAC, and 4) breast cancer radiomics. We aim to provide a comparison between these CAD applications and to illustrate a global view on intelligent CAD systems based on machine and deep learning in MRI of the breast. LEVEL OF EVIDENCE: 2 TECHNICAL EFFICACY STAGE: 2.

Meyer-Base Anke, Morra Lia, Tahmassebi Amirhessam, Lobbes Marc, Meyer-Base Uwe, Pinker Katja

2020-Aug-30

breast cancer, computer-aided diagnosis systems, kinetic features, machine learning, magnetic resonance imaging, morphologic features

General General

Recent Advances in Single-Atom Electrocatalysts for Oxygen Reduction Reaction.

In Research (Washington, D.C.)

Oxygen reduction reaction (ORR) plays significant roles in electrochemical energy storage and conversion systems as well as clean synthesis of fine chemicals. However, the ORR process shows sluggish kinetics and requires platinum-group noble metal catalysts to accelerate the reaction. The high cost, rare reservation, and unsatisfied durability significantly impede large-scale commercialization of platinum-based catalysts. Single-atom electrocatalysts (SAECs) featuring with well-defined structure, high intrinsic activity, and maximum atom efficiency have emerged as a novel field in electrocatalytic science since it is promising to substitute expensive platinum-group noble metal catalysts. However, finely fabricating SAECs with uniform and highly dense active sites, fully maximizing the utilization efficiency of active sites, and maintaining the atomically isolated sites as single-atom centers under harsh electrocatalytic conditions remain urgent challenges. In this review, we summarized recent advances of SAECs in synthesis, characterization, oxygen reduction reaction (ORR) performance, and applications in ORR-related H2O2 production, metal-air batteries, and low-temperature fuel cells. Relevant progress on tailoring the coordination structure of isolated metal centers by doping other metals or ligands, enriching the concentration of single-atom sites by increasing metal loadings, and engineering the porosity and electronic structure of the support by optimizing the mass and electron transport are also reviewed. Moreover, general strategies to synthesize SAECs with high metal loadings on practical scale are highlighted, the deep learning algorithm for rational design of SAECs is introduced, and theoretical understanding of active-site structures of SAECs is discussed as well. Perspectives on future directions and remaining challenges of SAECs are presented.

Han Junxing, Bian Juanjuan, Sun Chunwen

2020

Public Health Public Health

The myth of generalisability in clinical research and machine learning in health care.

In The Lancet. Digital health

An emphasis on overly broad notions of generalisability as it pertains to applications of machine learning in health care can overlook situations in which machine learning might provide clinical utility. We believe that this narrow focus on generalisability should be replaced with wider considerations for the ultimate goal of building machine learning systems that are useful at the bedside.

Futoma Joseph, Simons Morgan, Panch Trishan, Doshi-Velez Finale, Celi Leo Anthony

2020-Sep

Radiology Radiology

Cardiovascular/stroke risk prevention: A new machine learning framework integrating carotid ultrasound image-based phenotypes and its harmonics with conventional risk factors.

In Indian heart journal

MOTIVATION : Machine learning (ML)-based stroke risk stratification systems have typically focused on conventional risk factors (CRF) (AtheroRisk-conventional). Besides CRF, carotid ultrasound image phenotypes (CUSIP) have shown to be powerful phenotypes risk stratification. This is the first ML study of its kind that integrates CUSIP and CRF for risk stratification (AtheroRisk-integrated) and compares against AtheroRisk-conventional.

METHODS : Two types of ML-based setups called (i) AtheroRisk-integrated and (ii) AtheroRisk-conventional were developed using random forest (RF) classifiers. AtheroRisk-conventional uses a feature set of 13 CRF such as age, gender, hemoglobin A1c, fasting blood sugar, low-density lipoprotein, and high-density lipoprotein (HDL) cholesterol, total cholesterol (TC), a ratio of TC and HDL, hypertension, smoking, family history, triglyceride, and ultrasound-based carotid plaque score. AtheroRisk-integrated system uses the feature set of 38 features with a combination of 13 CRF and 25 CUSIP features (6 types of current CUSIP, 6 types of 10-year CUSIP, 12 types of quadratic CUSIP (harmonics), and age-adjusted grayscale median). Logistic regression approach was used to select the significant features on which the RF classifier was trained. The performance of both ML systems was evaluated by area-under-the-curve (AUC) statistics computed using a leave-one-out cross-validation protocol.

RESULTS : Left and right common carotid arteries of 202 Japanese patients were retrospectively examined to obtain 404 ultrasound scans. RF classifier showed higher improvement in AUC (~57%) for leave-one-out cross-validation protocol. Using RF classifier, AUC statistics for AtheroRisk-integrated system was higher (AUC = 0.99,p-value<0.001) compared to AtheroRisk-conventional (AUC = 0.63,p-value<0.001).

CONCLUSION : The AtheroRisk-integrated ML system outperforms the AtheroRisk-conventional ML system using RF classifier.

Jamthikar Ankush, Gupta Deep, Khanna Narendra N, Saba Luca, Laird John R, Suri Jasjit S

10-Year measurements, AtheroRisk-conventional, AtheroRisk-integrated, Atherosclerosis, Carotid, Conventional risk factors, Covariates, Features, Harmonics, Image-based phenotypes, Ultrasound

General General

Prediction of the final size for COVID-19 epidemic using machine learning: A case study of Egypt.

In Infectious Disease Modelling

COVID-19 is spreading within the sort of an enormous epidemic for the globe. This epidemic infects a lot of individuals in Egypt. The World Health Organization states that COVID-19 could be spread from one person to another at a very fast speed through contact and respiratory spray. On these days, Egypt and all countries worldwide should rise to an effective step to investigate this disease and eliminate the effects of this epidemic. In this paper displayed, the real database of COVID-19 for Egypt has been analysed from February 15, 2020, to June 15, 2020, and predicted with the number of patients that will be infected with COVID-19, and estimated the epidemic final size. Several regression analysis models have been applied for data analysis of COVID-19 of Egypt. In this study, we've been applied seven regression analysis-based models that are exponential polynomial, quadratic, third-degree, fourth-degree, fifth-degree, sixth-degree, and logit growth respectively for the COVID-19 dataset. Thus, the exponential, fourth-degree, fifth-degree, and sixth-degree polynomial regression models are excellent models specially fourth-degree model that will help the government preparing their procedures for one month. In addition, we have applied the well-known logit growth regression model and we obtained the following epidemiological insights: Firstly, the epidemic peak could possibly reach at 22-June 2020 and final time of epidemic at 8-September 2020. Secondly, the final total size for cases 1.6676E+05 cases. The action from government of interevent over a relatively long interval is necessary to minimize the final epidemic size.

Amar Lamiaa A, Taha Ashraf A, Mohamed Marwa Y

2020-Aug-25

COVID-19, Epidemic model, Regression analysis model

General General

Real-time Prediction of COVID-19 related Mortality using Electronic Health Records

ArXiv Preprint

Coronavirus Disease 2019 (COVID-19) is an emerging respiratory disease caused by the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) with rapid human-to-human transmission and a high case fatality rate particularly in older patients. Due to the exponential growth of infections, many healthcare systems across the world are under pressure to care for increasing amounts of at-risk patients. Given the high number of infected patients, identifying patients with the highest mortality risk early is critical to enable effective intervention and optimal prioritisation of care. Here, we present the COVID-19 Early Warning System (CovEWS), a clinical risk scoring system for assessing COVID-19 related mortality risk. CovEWS provides continuous real-time risk scores for individual patients with clinically meaningful predictive performance up to 192 hours (8 days) in advance, and is automatically derived from patients' electronic health records (EHRs) using machine learning. We trained and evaluated CovEWS using de-identified data from a cohort of 66430 COVID-19 positive patients seen at over 69 healthcare institutions in the United States (US), Australia, Malaysia and India amounting to an aggregated total of over 2863 years of patient observation time. On an external test cohort of 5005 patients, CovEWS predicts COVID-19 related mortality from $78.8\%$ ($95\%$ confidence interval [CI]: $76.0$, $84.7\%$) to $69.4\%$ ($95\%$ CI: $57.6, 75.2\%$) specificity at a sensitivity greater than $95\%$ between respectively 1 and 192 hours prior to observed mortality events - significantly outperforming existing generic and COVID-19 specific clinical risk scores. CovEWS could enable clinicians to intervene at an earlier stage, and may therefore help in preventing or mitigating COVID-19 related mortality.

Patrick Schwab, Arash Mehrjou, Sonali Parbhoo, Leo Anthony Celi, Jürgen Hetzel, Markus Hofer, Bernhard Schölkopf, Stefan Bauer

2020-08-31