Using artificial intelligence to improve body iron quantification: A scoping review

Nashwan, Abdulqadir J.; Alkhawaldeh, Ibraheem M.; Shaheen, Nour; Albalkhi, Ibrahem; Serag, Ibrahim; Sarhan, Khalid; Abujaber, Ahmad A.; Abd-Alrazaq, Alaa; Yassin, Mohamed A.

doi:10.1016/j.blre.2023.101133

Cited by 5 publications

(5 citation statements)

References 47 publications

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“…The published ML models for anemia classification all use different data than used here or perform a task other than discriminating IDA. For example, they rely on image data, or they use CBC variables to identify genetic disorders related to hemoglobin [10][11][12][13][14][15][16][17][18][19][20][27][28][29][30]. Since the cause of anemia is multifactorial [4], identifying concurrent iron deficiency is required to guide iron therapy.…”

Section: Discussionmentioning

confidence: 99%

“…However, a recent study reported the prediction of low ferritin levels or IDA among adult anemic subjects (more than 18 years of age) in referred lab tests with 90-98% specificity and sensitivity, using a random forest algorithm [28]. However, as suggested in a recent review [29], these models need to be validated in larger datasets and across larger age and gender subgroups to show robust generalizability. No prior studies have been shown to discriminate IDA effectively for a wide sample of subjects using only features from the CBC alone.…”

Section: Introductionmentioning

confidence: 99%

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Classification and Explanation of Iron Deficiency Anemia from Complete Blood Count Data Using Machine Learning

Pullakhandam,

McRoy

2024

BioMedInformatics

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Background: Currently, discriminating Iron Deficiency Anemia (IDA) from other anemia requires an expensive test (serum ferritin). Complete Blood Count (CBC) tests are less costly and more widely available. Machine learning models have not yet been applied to discriminating IDA but do well for similar tasks. Methods: We constructed multiple machine learning methods to classify IDA from CBC data using a US NHANES dataset of over 19,000 instances, calculating accuracy, precision, recall, and precision AUC (PR AUC). We validated the results using an unseen dataset from Kenya, using the same model. We calculated ranked feature importance to explain the global behavior of the model. Results: Our model classifies IDA with a PR AUC of 0.87 and recall/sensitivity of 0.98 and 0.89 for the original dataset and an unseen Kenya dataset, respectively. The explanations indicate that low blood level of hemoglobin, higher age, and higher Red Blood Cell distribution width were most critical. We also found that optimization made only minor changes to the explanations and that the features used remained consistent with professional practice. Conclusions: The overall high performance and consistency of the results suggest that the approach would be acceptable to health professionals and would support enhancements to current automated CBC analyzers.

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Section: Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Classification and Explanation of Iron Deficiency Anemia from Complete Blood Count Data Using Machine Learning

Pullakhandam,

McRoy

2024

BioMedInformatics

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“…Iron body quantification and measurement are usually performed via slightly invasive methods, such as routine blood tests (serum ferritin, serum iron, and transferrin saturation)[ 2 ]. Serum ferritin is an acute phase reactant protein, elevated in inflammation and distressed patients, therefore, it might give wrong impression about body iron[ 2 , 3 ]. Noninvasive procedures are increasingly utilized for quantifying and assessing body iron load, and MRI is the best noninvasive examination of the liver for quantifying iron in iron-overloaded patients and helps in the early detection of liver fibrosis, liver cirrhosis, and liver cancer induced by iron overload[ 4 ].…”

Section: Iron Quantificationmentioning

confidence: 99%

“…Evidence drawn from the literature on the use of AI for iron quantification and the detection of various liver diseases is insufficient. However, current literature shows promising results regarding iron quantification using MRI, and various models and algorithms have been used[ 3 ]. Furthermore, MRI has several windows, each focusing on certain body structures; however, it is still uncertain which MRI window is the most accurate for training ML and DL algorithms[ 5 ].…”

Section: Mri Based Disease Detectionmentioning

confidence: 99%

Revolutionizing disease diagnosis and management: Open-access magnetic resonance imaging datasets a challenge for artificial intelligence driven liver iron quantification

Jaradat,

Nashwan

2024

World J Clin Cases

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Artificial intelligence (AI), particularly machine learning (ML) and deep learning (DL) techniques, such as convolutional neural networks (CNNs), have emerged as transformative technologies with vast potential in healthcare. Body iron load is usually assessed using slightly invasive blood tests (serum ferritin, serum iron, and serum transferrin). Serum ferritin is widely used to assess body iron and drive medical management; however, it is an acute phase reactant protein offering wrong interpretation in the setting of inflammation and distressed patients. Magnetic resonance imaging is a non-invasive technique that can be used to assess liver iron. The ML and DL algorithms can be used to enhance the detection of minor changes. However, a lack of open-access datasets may delay the advancement of medical research in this field. In this letter, we highlight the importance of standardized datasets for advancing AI and CNNs in medical imaging. Despite the current limitations, embracing AI and CNNs holds promise in revolutionizing disease diagnosis and treatment.

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“…Quality professionals have the potential to unleash the capabilities of AI to enhance the quality of care, as AI has demonstrated its transformative impact across various medical domains; this was evident in its ability to navigate extensive medical imaging datasets,[ 4 ] detect patterns within EHRs,[ 5 ] make predictions,[ 6 ] support clinical decision-making,[ 7 ] and unveil valuable insights concealed within the unstructured narratives in EHRs. [ 8 ] AI encompasses diverse concepts, algorithms, and models, using computers or machines to simulate human intelligence.…”

Section: Introductionmentioning

confidence: 99%

Transforming Hospital Quality Improvement Through Harnessing the Power of Artificial Intelligence

Abukhadijah,

Nashwan

2024

Global Journal on Quality and Safety in Healthcare

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This policy analysis focuses on harnessing the power of artificial intelligence (AI) in hospital quality improvement to transform quality and patient safety. It examines the application of AI at the two following fundamental levels: (1) diagnostic and treatment and (2) clinical operations. AI applications in diagnostics directly impact patient care and safety. At the same time, AI indirectly influences patient safety at the clinical operations level by streamlining (1) operational efficiency, (2) risk assessment, (3) predictive analytics, (4) quality indicators reporting, and (5) staff training and education. The challenges and future perspectives of AI application in healthcare, encompassing technological, ethical, and other considerations, are also critically analyzed.

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Using artificial intelligence to improve body iron quantification: A scoping review

Cited by 5 publications

References 47 publications

Classification and Explanation of Iron Deficiency Anemia from Complete Blood Count Data Using Machine Learning

Classification and Explanation of Iron Deficiency Anemia from Complete Blood Count Data Using Machine Learning

Revolutionizing disease diagnosis and management: Open-access magnetic resonance imaging datasets a challenge for artificial intelligence driven liver iron quantification

Transforming Hospital Quality Improvement Through Harnessing the Power of Artificial Intelligence

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