Author Correction: Artificial Intelligence based Models for Screening of Hematologic Malignancies using Cell Population Data

Syed-Abdul, Shabbir; Firdani, Rianda Putra; Chung, Hee Jung; Uddin, Mohy; Hur, Mina; Park, Jae Hyeon; Kim, Hyung Woo; Gradišek, Anton; Dovgan, Erik

doi:10.1038/s41598-020-67559-5

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“…No standardized leukemia screening tests have been proven reliable and efficient enough to identify the presence of blood cancer in its early stages [ 83 ]. The causes of leukemia remain unknown and a diagnosis cannot be confirmed until the initial symptoms develop.…”

Section: Resultsmentioning

confidence: 99%

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A Review of Artificial Intelligence Applications in Hematology Management: Current Practices and Future Prospects

Alaoui¹,

Elomri²,

Qaraqe³

et al. 2022

J Med Internet Res

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Background Machine learning (ML) and deep learning (DL) methods have recently garnered a great deal of attention in the field of cancer research by making a noticeable contribution to the growth of predictive medicine and modern oncological practices. Considerable focus has been particularly directed toward hematologic malignancies because of the complexity in detecting early symptoms. Many patients with blood cancer do not get properly diagnosed until their cancer has reached an advanced stage with limited treatment prospects. Hence, the state-of-the-art revolves around the latest artificial intelligence (AI) applications in hematology management. Objective This comprehensive review provides an in-depth analysis of the current AI practices in the field of hematology. Our objective is to explore the ML and DL applications in blood cancer research, with a special focus on the type of hematologic malignancies and the patient’s cancer stage to determine future research directions in blood cancer. Methods We searched a set of recognized databases (Scopus, Springer, and Web of Science) using a selected number of keywords. We included studies written in English and published between 2015 and 2021. For each study, we identified the ML and DL techniques used and highlighted the performance of each model. Results Using the aforementioned inclusion criteria, the search resulted in 567 papers, of which 144 were selected for review. Conclusions The current literature suggests that the application of AI in the field of hematology has generated impressive results in the screening, diagnosis, and treatment stages. Nevertheless, optimizing the patient’s pathway to treatment requires a prior prediction of the malignancy based on the patient’s symptoms or blood records, which is an area that has still not been properly investigated.

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Section: Resultsmentioning

confidence: 99%

“…Values, n (%) Pathway stage [1,6,7,29,38,40,41,50,51,53,55,64,69,82,92,94,96,[99][100][101][102]105,117,118,136,143,144] 27 (20.6) Prediction [3,10,28,30,[32][33][34]36,[44][45][46]57,58,61,66,71,72,[76][77][78]80,83,85,89,91,…”

Section: Studiesmentioning

confidence: 99%

A Review of Artificial Intelligence Applications in Hematology Management: Current Practices and Future Prospects

Alaoui¹,

Elomri²,

Qaraqe³

et al. 2022

J Med Internet Res

View full text Add to dashboard Cite

show abstract

Author Correction: Artificial Intelligence based Models for Screening of Hematologic Malignancies using Cell Population Data

Abstract: An amendment to this paper has been published and can be accessed via a link at the top of the paper.

Cited by 1 publication

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A Review of Artificial Intelligence Applications in Hematology Management: Current Practices and Future Prospects

A Review of Artificial Intelligence Applications in Hematology Management: Current Practices and Future Prospects

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