A systematic review of artificial intelligence-based COVID-19 modeling on multimodal genetic information

Sekaran, K. Chandra; Gnanasambandan, R.; Ramkumar, T.; Iyyadurai, Ramya; Karthick, G.; C, George Priya Doss

doi:10.1016/j.pbiomolbio.2023.02.003

Cited by 12 publications

(6 citation statements)

References 50 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Previous studies revealed the potation of AI in medical imaging [ 17 – 18 ]. A systematic review summarized a total of 48 studies about AI methods applied to COVID-19 diagnosis, biomarker discovery, therapeutic evaluation and survival analysis from January 2020 to June 2022 [ 19 ]. This review provided evidence to delineate the potential of AI in analysing complex gene information for COVID-19 modeling on multiple aspects including diagnosis.…”

Section: Discussionmentioning

confidence: 99%

Automated machine learning for the identification of asymptomatic COVID-19 carriers based on chest CT images

Yin,

Xu,

Zhu

et al. 2024

BMC Med Imaging

View full text Add to dashboard Cite

Background Asymptomatic COVID-19 carriers with normal chest computed tomography (CT) scans have perpetuated the ongoing pandemic of this disease. This retrospective study aimed to use automated machine learning (AutoML) to develop a prediction model based on CT characteristics for the identification of asymptomatic carriers. Methods Asymptomatic carriers were from Yangzhou Third People’s Hospital from August 1st, 2020, to March 31st, 2021, and the control group included a healthy population from a nonepizootic area with two negative RT‒PCR results within 48 h. All CT images were preprocessed using MATLAB. Model development and validation were conducted in R with the H2O package. The models were built based on six algorithms, e.g., random forest and deep neural network (DNN), and a training set (n = 691). The models were improved by automatically adjusting hyperparameters for an internal validation set (n = 306). The performance of the obtained models was evaluated based on a dataset from Suzhou (n = 178) using the area under the curve (AUC), accuracy, sensitivity, specificity, positive predictive value (PPV), negative predictive value (NPV) and F1 score. Results A total of 1,175 images were preprocessed with high stability. Six models were developed, and the performance of the DNN model ranked first, with an AUC value of 0.898 for the test set. The sensitivity, specificity, PPV, NPV, F1 score and accuracy of the DNN model were 0.820, 0.854, 0.849, 0.826, 0.834 and 0.837, respectively. A plot of a local interpretable model-agnostic explanation demonstrated how different variables worked in identifying asymptomatic carriers. Conclusions Our study demonstrates that AutoML models based on CT images can be used to identify asymptomatic carriers. The most promising model for clinical implementation is the DNN-algorithm-based model.

show abstract

Section: Discussionmentioning

confidence: 99%

Automated machine learning for the identification of asymptomatic COVID-19 carriers based on chest CT images

Yin,

Xu,

Zhu

et al. 2024

BMC Med Imaging

View full text Add to dashboard Cite

show abstract

“…The patient has care-seeking type IAD in the setting of LC-19-associated symptoms after experiencing mild COVID-19. LC-19 remains poorly defined due to the wide variation in symptom presentation, symptom timeline, and nonspecific nature of LC-19 symptoms [5]. In literature, LC-19 has been described to persist for a minimum of 4 weeks after the onset of COVID-19 [2].…”

Section: Discussionmentioning

confidence: 99%

A Case Report on Care-Seeking Type Illness Anxiety Disorder after COVID-19 Infection

Kasi,

Moorthy

2023

Case Reports in Psychiatry

View full text Add to dashboard Cite

This case report highlights the diagnostic challenges presented by the overlapping symptoms of illness anxiety disorder (IAD) and long COVID-19 (LC-19). This case report focuses on a 58-year-old woman with care-seeking type IAD in the context of LC-19-associated symptoms. The patient experienced mild COVID-19 in August 2021. Since then, she has reported an increase in LC-19-associated symptoms, including cognitive deficits, breathlessness, fatigue, and anosmia. Despite largely normal laboratory results, imaging, and physical examinations, the patient’s distress and care-seeking behaviors persisted, resulting in the diagnosis of IAD. Accurately differentiating between LC-19 and IAD is crucial for appropriate patient care. We discuss the importance of recognizing and treating IAD in patients with LC-19-associated symptoms and the need for further research on the correlation between IAD and both COVID-19 and LC-19.

show abstract

“…Challenges include the need for large-scale research efforts, the development of effective and safe TPD molecules for human use, and the implementation of these technologies in psychedelic therapy. Insights from COVID-19 research, including large-scale genetic testing and AI use for drug discovery, could be harnessed for psychedelic research, potentially improving the safety and efficacy of psychedelic therapies.…”

Section: Current State Of Psychedelic Researchmentioning

confidence: 99%

Unraveling Psychedelic Responses: Targeted Protein Degradation and Genetic Diversity

Kargbo

2023

ACS Med. Chem. Lett.

View full text Add to dashboard Cite

This Viewpoint discusses the intersection of targeted protein degradation (TPD) technologies and psychedelic research. The resurgence in interest in psychedelics for treating mental disorders and the known genetic variability in responses require new strategies. TPD technologies might address this variability, modulating protein expressions based on genetic profiles. The discussion includes potential challenges in implementing TPD technologies in psychedelic research and potential strategies to address these issues. It considers lessons from COVID-19 research on genetic variability, proposing integration of TPD technologies into psychedelic research as a promising field despite these challenges, possibly leading to personalized treatments and improved patient outcomes.

show abstract

A systematic review of artificial intelligence-based COVID-19 modeling on multimodal genetic information

Cited by 12 publications

References 50 publications

Automated machine learning for the identification of asymptomatic COVID-19 carriers based on chest CT images

Automated machine learning for the identification of asymptomatic COVID-19 carriers based on chest CT images

A Case Report on Care-Seeking Type Illness Anxiety Disorder after COVID-19 Infection

Unraveling Psychedelic Responses: Targeted Protein Degradation and Genetic Diversity

Contact Info

Product

Resources

About