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Public Health Public Health

An AI-enabled research support tool for the classification system of COVID-19.

In Frontiers in public health

The outbreak of COVID-19, a little more than 2 years ago, drastically affected all segments of society throughout the world. While at one end, the microbiologists, virologists, and medical practitioners were trying to find the cure for the infection; the Governments were laying emphasis on precautionary measures like lockdowns to lower the spread of the virus. This pandemic is perhaps also the first one of its kind in history that has research articles in all possible areas as like: medicine, sociology, psychology, supply chain management, mathematical modeling, etc. A lot of work is still continuing in this area, which is very important also for better preparedness if such a situation arises in future. The objective of the present study is to build a research support tool that will help the researchers swiftly identify the relevant literature on a specific field or topic regarding COVID-19 through a hierarchical classification system. The three main tasks done during this study are data preparation, data annotation and text data classification through bi-directional long short-term memory (bi-LSTM).

Tiwari Arti, Bhattacharjee Kamanasish, Pant Millie, Srivastava Shilpa, Snasel Vaclav

2023

Artificial Intelligence, COVID-19, bi-directional LSTM, classification, long short-term memory

General General

Large AI Models in Health Informatics: Applications, Challenges, and the Future

ArXiv Preprint

Large AI models, or foundation models, are models recently emerging with massive scales both parameter-wise and data-wise, the magnitudes of which often reach beyond billions. Once pretrained, large AI models demonstrate impressive performance in various downstream tasks. A concrete example is the recent debut of ChatGPT, whose capability has compelled people's imagination about the far-reaching influence that large AI models can have and their potential to transform different domains of our life. In health informatics, the advent of large AI models has brought new paradigms for the design of methodologies. The scale of multimodality data in the biomedical and health domain has been ever-expanding especially since the community embraced the era of deep learning, which provides the ground to develop, validate, and advance large AI models for breakthroughs in health-related areas. This article presents an up-to-date comprehensive review of large AI models, from background to their applications. We identify seven key sectors that large AI models are applicable and might have substantial influence, including 1) molecular biology and drug discovery; 2) medical diagnosis and decision-making; 3) medical imaging and vision; 4) medical informatics; 5) medical education; 6) public health; and 7) medical robotics. We examine their challenges in health informatics, followed by a critical discussion about potential future directions and pitfalls of large AI models in transforming the field of health informatics.

Jianing Qiu, Lin Li, Jiankai Sun, Jiachuan Peng, Peilun Shi, Ruiyang Zhang, Yinzhao Dong, Kyle Lam, Frank P. -W. Lo, Bo Xiao, Wu Yuan, Dong Xu, Benny Lo

2023-03-21

General General

Clinical Biomarkers and Prediction Models for Poststroke Epilepsy: Have We Settled the Scores Yet?

In Neurology. Clinical practice

In an era of time-dependent reperfusion and recanalization therapy for stroke leading to improved survival, there is a growing population at risk of poststroke epilepsy (PSE). Accumulating evidence suggests a multidirectional interaction among stroke, PSE, and dementia in stroke survivors. There is no evidence to justify prophylactic antiseizure medication (ASM) to reduce these morbidities. Although several predictive molecular biomarkers and scoring models have been proposed, they remain inadequately validated for stratifying risk and indicating who will benefit from prophylactic ASM. Studies leveraging advances in genetics, metabolomics, electrophysiology, imaging, and artificial intelligence (AI) may help to discover noninvasive molecular biomarkers and easy-to-score models. These discoveries should improve our understanding of epileptogenesis in PSE and identify new pharmacologic targets. Besides, accurately identifying high-risk patients and timely initiating prophylactic ASM therapy has the potential to disrupt the feed-forward multidirectional interaction among stroke, PSE, and dementia.

Yonas Amen S, Meschia James F, Feyissa Anteneh M

2023-Apr

oncology Oncology

Prognosis and Characterization of Microenvironment in Cervical Cancer Influenced by Fatty Acid Metabolism-Related Genes.

In Journal of oncology

Increasing evidence suggests that diverse activation patterns of metabolic signalling pathways may lead to molecular diversity of cervical cancer (CC). But rare research focuses on the alternation of fatty acid metabolism (FAM) in CC. Therefore, we constructed and compared models based on the expression of FAM-related genes from the Cancer Genome Atlas by different machine learning algorithms. The most reliable model was built with 14 significant genes by LASSO-Cox regression, and the CC cohort was divided into low-/high-risk groups by the median of risk score. Then, a feasible nomogram was established and validated by C-index, calibration curve, net benefit, and decision curve analysis. Furthermore, the hub genes among differential expression genes were identified and the post-transcriptional and translational regulation networks were characterized. Moreover, the somatic mutation and copy number variation landscapes were depicted. Importantly, the specific mutation drivers and signatures of the FAM phenotypes were excavated. As a result, the high-risk samples were featured by activated de novo fatty acid synthesis, epithelial to mesenchymal transition, angiogenesis, and chronic inflammation response, which might be caused by mutations of oncogenic driver genes in RTK/RAS, PI3K, and NOTCH signalling pathways. Besides the hyperactivity of cytidine deaminase and deficiency of mismatch repair, the mutations of POLE might be partially responsible for the mutations in the high-risk group. Next, the antigenome including the neoantigen and cancer germline antigens was estimated. The decreasing expression of a series of cancer germline antigens was identified to be related to reduction of CD8 T cell infiltration in the high-risk group. Then, the comprehensive evaluation of connotations between the tumour microenvironment and FAM phenotypes demonstrated that the increasing risk score was related to the suppressive immune microenvironment. Finally, the prediction of therapy targets revealed that the patients with high risk might be sensitive to the RAF inhibitor AZ628. Our findings provide a novel insight for personalized treatment in CC.

Zhou Yanjun, Zhu Jiahao, Gu Mengxuan, Gu Ke

2023

Dermatology Dermatology

Towards AI-driven longevity research: An overview.

In Frontiers in aging

While in the past technology has mostly been utilized to store information about the structural configuration of proteins and molecules for research and medical purposes, Artificial Intelligence is nowadays able to learn from the existing data how to predict and model properties and interactions, revealing important knowledge about complex biological processes, such as aging. Modern technologies, moreover, can rely on a broader set of information, including those derived from the next-generation sequencing (e.g., proteomics, lipidomics, and other omics), to understand the interactions between human body and the external environment. This is especially relevant as external factors have been shown to have a key role in aging. As the field of computational systems biology keeps improving and new biomarkers of aging are being developed, artificial intelligence promises to become a major ally of aging research.

Marino Nicola, Putignano Guido, Cappilli Simone, Chersoni Emmanuele, Santuccione Antonella, Calabrese Giuliana, Bischof Evelyne, Vanhaelen Quentin, Zhavoronkov Alex, Scarano Bryan, Mazzotta Alessandro D, Santus Enrico

2023

artificial intelligence, biomarkers, deep aging clock, feature selection, longevity medicine, machine learning

Ophthalmology Ophthalmology

Using deep leaning models to detect ophthalmic diseases: A comparative study.

In Frontiers in medicine

PURPOSE : The aim of this study was to prospectively quantify the level of agreement among the deep learning system, non-physician graders, and general ophthalmologists with different levels of clinical experience in detecting referable diabetic retinopathy, age-related macular degeneration, and glaucomatous optic neuropathy.

METHODS : Deep learning systems for diabetic retinopathy, age-related macular degeneration, and glaucomatous optic neuropathy classification, with accuracy proven through internal and external validation, were established using 210,473 fundus photographs. Five trained non-physician graders and 47 general ophthalmologists from China were chosen randomly and included in the analysis. A test set of 300 fundus photographs were randomly identified from an independent dataset of 42,388 gradable images. The grading outcomes of five retinal and five glaucoma specialists were used as the reference standard that was considered achieved when ≥50% of gradings were consistent among the included specialists. The area under receiver operator characteristic curve of different groups in relation to the reference standard was used to compare agreement for referable diabetic retinopathy, age-related macular degeneration, and glaucomatous optic neuropathy.

RESULTS : The test set included 45 images (15.0%) with referable diabetic retinopathy, 46 (15.3%) with age-related macular degeneration, 46 (15.3%) with glaucomatous optic neuropathy, and 163 (55.4%) without these diseases. The area under receiver operator characteristic curve for non-physician graders, ophthalmologists with 3-5 years of clinical practice, ophthalmologists with 5-10 years of clinical practice, ophthalmologists with >10 years of clinical practice, and the deep learning system for referable diabetic retinopathy were 0.984, 0.964, 0.965, 0.954, and 0.990 (p = 0.415), respectively. The results for referable age-related macular degeneration were 0.912, 0.933, 0.946, 0.958, and 0.945, respectively, (p = 0.145), and 0.675, 0.862, 0.894, 0.976, and 0.994 for referable glaucomatous optic neuropathy, respectively (p < 0.001).

CONCLUSION : The findings of this study suggest that the accuracy of this deep learning system is comparable to that of trained non-physician graders and general ophthalmologists for referable diabetic retinopathy and age-related macular degeneration, but the deep learning system performance is better than that of trained non-physician graders for the detection of referable glaucomatous optic neuropathy.

Li Zhixi, Guo Xinxing, Zhang Jian, Liu Xing, Chang Robert, He Mingguang

2023

age-related macular degeneration, deep learning, diabetic retinopathy, fundus photograph, glaucomatous optic neuropathy