Role of Machine Learning Techniques to Tackle the COVID-19 Crisis: Systematic Review

Syeda, Hafsa Bareen; Syed, Mahanazuddin; Sexton, Kevin W.; Syed, Shorabuddin; Begum, Salma; Syed, Farhanuddin; Prior, Fred; Yu, Feliciano

doi:10.2196/23811

Cited by 120 publications

(90 citation statements)

References 174 publications

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“…Previous review papers on COVID-19 either focused on a technical assessment of AI in imaging 6 or elaborated on the role of imaging. 1 Related systematic reviews were either not devoted specifically to imaging 17,18 or used extremely small sample sizes (N = 11). 19 In contrast, this paper attempts to bridge clinical and technical perspectives by providing a comprehensive overview to guide researchers toward working on the most pressing problems in automating lung image analysis for COVID-19.…”

Section: Motivation and Contributionsmentioning

confidence: 99%

On the role of artificial intelligence in medical imaging of COVID-19

Born¹,

Beymer²,

Rajan³

et al. 2021

Patterns

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Although a plethora of research articles on AI methods on COVID-19 medical imaging are published, their clinical value remains unclear. We conducted the largest systematic review of the literature addressing the utility of AI in imaging for COVID-19 patient care. By keyword searches on PubMed and preprint servers throughout 2020, we identified 463 manuscripts and performed a systematic meta-analysis to assess their technical merit and clinical relevance. Our analysis evidences a significant disparity between clinical and AI communities, in the focus on both imaging modalities (AI experts neglected CT and ultrasound, favoring X-ray) and performed tasks (71.9% of AI papers centered on diagnosis). The vast majority of manuscripts were found to be deficient regarding potential use in clinical practice, but 2.7% (n = 12) publications were assigned a high maturity level and are summarized in greater detail. We provide an itemized discussion of the challenges in developing clinically relevant AI solutions with recommendations and remedies.THE BIGGER PICTURE During the COVID-19 pandemic, medical imaging (CT, X-ray, ultrasound) has played a key role in addressing the magnified need for speed, low cost, ubiquity, and precision in patient care. The contemporary digitization of medicine and rise of artificial intelligence (AI) induce a quantum leap in medical imaging: AI has proven equipollent to healthcare professionals across a diverse range of tasks, and hopes are high that AI can save time and cost and increase coverage by advancing rapid patient stratification and empowering clinicians. This review bridges medical imaging and AI in the context of COVID-19 and conducts the largest systematic review of the literature in the field. We identify several gaps and evidence significant disparities between clinicians and AI experts and foresee a need for improved, interdisciplinary collaboration to develop robust AI solutions that can be deployed in clinical practice.The key challenges on that roadmap are discussed alongside recommended solutions. ll

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Section: Motivation and Contributionsmentioning

confidence: 99%

On the role of artificial intelligence in medical imaging of COVID-19

Born¹,

Beymer²,

Rajan³

et al. 2021

Patterns

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“…Artificial intelligence has been used in numerous ways to combat COVID-19, including not only the use of blood-based laboratory data but also radiomics and remote monitoring devices [ 42 ]. The hidden potential of these tools should be explored in both other communicable and noncommunicable diseases as they are likely to provide unexpected long-term benefits for numerous other health conditions, for example, cardiacvascular disease and heart failure [ 43 ].…”

Section: Discussionmentioning

confidence: 99%

A Machine Learning PROGRAM to Identify COVID-19 and Other Diseases From Hematology Data

et al. 2021

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Aim: We propose a method for screening full blood count metadata for evidence of communicable and noncommunicable diseases using machine learning (ML). Materials & methods: High dimensional hematology metadata was extracted over an 11-month period from Sysmex hematology analyzers from 43,761 patients. Predictive models for age, sex and individuality were developed to demonstrate the personalized nature of hematology data. Both numeric and raw flow cytometry data were used for both supervised and unsupervised ML to predict the presence of pneumonia, urinary tract infection and COVID-19. Heart failure was used as an objective to prove method generalizability. Results: Chronological age was predicted by a deep neural network with R2: 0.59; mean absolute error: 12; sex with AUROC: 0.83, phi: 0.47; individuality with 99.7% accuracy, phi: 0.97; pneumonia with AUROC: 0.74, sensitivity 58%, specificity 79%, 95% CI: 0.73–0.75, p < 0.0001; urinary tract infection AUROC: 0.68, sensitivity 52%, specificity 79%, 95% CI: 0.67–0.68, p < 0.0001; COVID-19 AUROC: 0.8, sensitivity 82%, specificity 75%, 95% CI: 0.79–0.8, p = 0.0006; and heart failure area under the receiver operator curve (AUROC): 0.78, sensitivity 72%, specificity 72%, 95% CI: 0.77–0.78; p < 0.0001. Conclusion: ML applied to hematology data could predict communicable and noncommunicable diseases, both at local and global levels.

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“…AI was also used for early detection and diagnosis. DNN can be used in conjunction with x-ray or CT to diagnose COVID-19 detection and automate the process to keep up with the overwhelming demand [86]. AI played a significant role in developing novel text and data mining techniques to aid COVID-19 research.…”

Section: Emergency Response and Covid-19mentioning

confidence: 99%

Machine Learning in Healthcare Communication

Siddique

Chow

2021

Encyclopedia

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Machine learning (ML) is a study of computer algorithms for automation through experience. ML is a subset of artificial intelligence (AI) that develops computer systems, which are able to perform tasks generally having need of human intelligence. While healthcare communication is important in order to tactfully translate and disseminate information to support and educate patients and public, ML is proven applicable in healthcare with the ability for complex dialogue management and conversational flexibility. In this topical review, we will highlight how the application of ML/AI in healthcare communication is able to benefit humans. This includes chatbots for the COVID-19 health education, cancer therapy, and medical imaging.

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Role of Machine Learning Techniques to Tackle the COVID-19 Crisis: Systematic Review

Cited by 120 publications

References 174 publications

On the role of artificial intelligence in medical imaging of COVID-19

On the role of artificial intelligence in medical imaging of COVID-19

A Machine Learning PROGRAM to Identify COVID-19 and Other Diseases From Hematology Data

Machine Learning in Healthcare Communication

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