Abstract:The complete blood count (CBC) performed by automated haematology analysers is the most common clinical procedure in the world. Used for health checkup, diagnosis and patient follow-up, the CBC impacts the majority of medical decisions. If the analysis does not fit an expected setting, the laboratory staff manually reviews a blood smear, which is highly time-consuming. Criteria for reviewing CBCs are based on international consensus guidelines and locally adjusted to account for laboratory resources and popula… Show more
“…But there is some drawback to these methods, they are obsolete and requires very highly skilled personal to investigate the blood smear. Sometimes it is also very timeconsuming which creates a negative impact on patient efficient and timely treatment (10). One more difficult part of hematology identification is the presence of other blood components around WBCs.…”
Section: Obstacles In Haematological Managementmentioning
In this Era of Machine Learning (ML) and Artificial Intelligence (AI), there is no field left in which these two have not left their impact. Blood cancer or Leukemia is now a days one of the very common hematological disorders. It is very difficult to diagnose leukemia in its very early stages, when it is diagnosed in its later stages it is very difficult to treat it because there are very limited treatments available. So, it is now very important to improve the diagnostic tools and techniques in traditional diagnostics procedures. ML and AI 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 of detecting early symptoms. Many patients with blood cancer do not get properly diagnosed until their cancer has reached an unnumbered stage with limited treatment prospects. Hence, the state-of-the-art revolves around the latest artificial intelligence applications in hematology management.
“…But there is some drawback to these methods, they are obsolete and requires very highly skilled personal to investigate the blood smear. Sometimes it is also very timeconsuming which creates a negative impact on patient efficient and timely treatment (10). One more difficult part of hematology identification is the presence of other blood components around WBCs.…”
Section: Obstacles In Haematological Managementmentioning
In this Era of Machine Learning (ML) and Artificial Intelligence (AI), there is no field left in which these two have not left their impact. Blood cancer or Leukemia is now a days one of the very common hematological disorders. It is very difficult to diagnose leukemia in its very early stages, when it is diagnosed in its later stages it is very difficult to treat it because there are very limited treatments available. So, it is now very important to improve the diagnostic tools and techniques in traditional diagnostics procedures. ML and AI 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 of detecting early symptoms. Many patients with blood cancer do not get properly diagnosed until their cancer has reached an unnumbered stage with limited treatment prospects. Hence, the state-of-the-art revolves around the latest artificial intelligence applications in hematology management.
“…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%
“…Therefore, several microscopic evaluations of the blood smear are performed to reach a final diagnosis [ 5 ]. As all the available methods are manual and require highly skilled medical personnel for interpretation, a blood cancer diagnosis can be costly and time consuming, which negatively impacts the patient’s efficient and timely treatment [ 10 ]. Another challenging aspect in hematology detection is that WBCs are surrounded by other blood components.…”
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.
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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