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Surgery Surgery

Commentary on a combined approach to the problem of developing biomarkers for the prediction of spontaneous preterm labor that leads to preterm birth.

In Placenta ; h5-index 40.0

INTRODUCTION : Globally, preterm birth has replaced congenital malformation as the major cause of perinatal mortality and morbidity. The reduced rate of congenital malformation was not achieved through a single biophysical or biochemical marker at a specific gestational age, but rather through a combination of clinical, biophysical and biochemical markers at different gestational ages. Since the aetiology of spontaneous preterm birth is also multifactorial, it is unlikely that a single biomarker test, at a specific gestational age will emerge as the definitive predictive test.

METHODS : The Biomarkers Group of PREBIC, comprising clinicians, basic scientists and other experts in the field, with a particular interest in preterm birth have produced this commentary with short, medium and long-term aims: i) to alert clinicians to the advances that are being made in the prediction of spontaneous preterm birth; ii) to encourage clinicians and scientists to continue their efforts in this field, and not to be disheartened or nihilistic because of a perceived lack of progress and iii) to enable development of novel interventions that can reduce the mortality and morbidity associated with preterm birth.

RESULTS : Using language that we hope is clear to practising clinicians, we have identified 11 Sections in which there exists the potential, feasibility and capability of technologies for candidate biomarkers in the prediction of spontaneous preterm birth and how current limitations to this research might be circumvented.

DISCUSSION : The combination of biophysical, biochemical, immunological, microbiological, fetal cell, exosomal, or cell free RNA at different gestational ages, integrated as part of a multivariable predictor model may be necessary to advance our attempts to predict sPTL and PTB. This will require systems biological data using "omics" data and artificial intelligence/machine learning to manage the data appropriately. The ultimate goal is to reduce the mortality and morbidity associated with preterm birth.

Lamont R F, Richardson L S, Boniface J J, Cobo T, Exner M M, Christensen I B, Forslund S K, Gaba A, Helmer H, Jørgensen J S, Khan R N, McElrath T F, Petro K, Rasmussen M, Singh R, Tribe R M, Vink J S, Vinter C A, Zhong N, Menon R


Biobanking, Bioinformatics, Biomarkers, Computational statistics, Immunology, Preterm birth, Proteomics, Transvaginal ultrasound of cervical length, Vaginal microbiome

Radiology Radiology

Overview of Machine Learning Part 1: Fundamentals and Classic Approaches.

In Neuroimaging clinics of North America

The extensive body of research and advances in machine learning (ML) and the availability of a large volume of patient data make ML a powerful tool for producing models with the potential for widespread deployment in clinical settings. This article provides an overview of the classic supervised and unsupervised ML methods as well as fundamental concepts required for understanding how to develop generalizable and high-performing ML applications. It also describes the important steps for developing a ML model and how decisions made in these steps affect model performance and ability to generalize.

Maleki Farhad, Ovens Katie, Najafian Keyhan, Forghani Behzad, Reinhold Caroline, Forghani Reza


Classification, Clustering, Dimensionality reduction, Machine learning, Regression, Supervised learning, Unsupervised learning, Visualization

Radiology Radiology

Artificial Intelligence Applications for Workflow, Process Optimization and Predictive Analytics.

In Neuroimaging clinics of North America

There is great potential for artificial intelligence (AI) applications, especially machine learning and natural language processing, in medical imaging. Much attention has been garnered by the image analysis tasks for diagnostic decision support and precision medicine, but there are many other potential applications of AI in radiology and have potential to enhance all levels of the radiology workflow and practice, including workflow optimization and support for interpretation tasks, quality and safety, and operational efficiency. This article reviews the important potential applications of informatics and AI related to process improvement and operations in the radiology department.

Letourneau-Guillon Laurent, Camirand David, Guilbert Francois, Forghani Reza


Artificial intelligence, Decision support, Machine learning, Medical imaging, Operational efficiency, Radiology, Smart device, Workflow

Radiology Radiology

Machine Learning Applications for Head and Neck Imaging.

In Neuroimaging clinics of North America

The head and neck (HN) consists of a large number of vital anatomic structures within a compact area. Imaging plays a central role in the diagnosis and management of major disorders affecting the HN. This article reviews the recent applications of machine learning (ML) in HN imaging with a focus on deep learning approaches. It categorizes ML applications in HN imaging into deep learning and traditional ML applications and provides examples of each category. It also discusses the main challenges facing the successful deployment of ML-based applications in the clinical setting and provides suggestions for addressing these challenges.

Maleki Farhad, Le William Trung, Sananmuang Thiparom, Kadoury Samuel, Forghani Reza


Artificial intelligence, Autosegmentation, Classification, Convolutional neural network, Deep learning, Head and neck cancer, Head and neck imaging, Machine learning

Radiology Radiology

Diverse Applications of Artificial Intelligence in Neuroradiology.

In Neuroimaging clinics of North America

Recent advances in artificial intelligence (AI) and deep learning (DL) hold promise to augment neuroimaging diagnosis for patients with brain tumors and stroke. Here, the authors review the diverse landscape of emerging neuroimaging applications of AI, including workflow optimization, lesion segmentation, and precision education. Given the many modalities used in diagnosing neurologic diseases, AI may be deployed to integrate across modalities (MR imaging, computed tomography, PET, electroencephalography, clinical and laboratory findings), facilitate crosstalk among specialists, and potentially improve diagnosis in patients with trauma, multiple sclerosis, epilepsy, and neurodegeneration. Together, there are myriad applications of AI for neuroradiology."

Duong Michael Tran, Rauschecker Andreas M, Mohan Suyash


Artificial intelligence, Deep learning, Epilepsy, Multiple sclerosis, Neural network, Neurodegeneration, Neuroradiology, Trauma

General General

Machine learning techniques for sequence-based prediction of viral-host interactions between SARS-CoV-2 and human proteins.

In Biomedical journal

BACKGROUND : COVID-19 (Coronavirus Disease-19), a disease caused by the SARS-CoV-2 virus, has been declared as a pandemic by the World Health Organization on March 11, 2020. Over 15 million people have already been affected worldwide by COVID-19, resulting in more than 0.6 million deaths. Protein-protein interactions (PPIs) play a key role in the cellular process of SARS-CoV-2 virus infection in the human body. Recently a study has reported some SARS-CoV-2 proteins that interact with several human proteins while many potential interactions remain to be identified.

METHOD : In this article, various machine learning models are built to predict the PPIs between the virus and human proteins that are further validated using biological experiments. The classification models are prepared based on different sequence-based features of human proteins like amino acid composition, pseudo amino acid composition, and conjoint triad.

RESULT : We have built an ensemble voting classifier using SVMRadial, SVMPolynomial, and Random Forest technique that gives a greater accuracy, precision, specificity, recall, and F1 score compared to all other models used in the work. A total of 1326 potential human target proteins of SARS-CoV-2 have been predicted by the proposed ensemble model and validated using gene ontology and KEGG pathway enrichment analysis. Several repurposable drugs targeting the predicted interactions are also reported.

CONCLUSION : This study may encourage the identification of potential targets for more effective anti-COVID drug discovery.

Dey Lopamudra, Chakraborty Sanjay, Mukhopadhyay Anirban


COVID-19, Classifier ensemble, Machine learning, Protein–protein interaction, SARS-CoV-2, Supervised classification