Predicting anemia using NIR spectrum of spent dialysis fluid in hemodialysis patients

Matovic, Valentina; Jeftić, Branislava; Trbojević-Stanković, Jasna; Matija, Lidija

doi:10.1038/s41598-021-88821-4

Cited by 5 publications

(3 citation statements)

References 36 publications

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“…Another research [13]effort utilized a Convolutional Neural Network (CNN) analyzing eye images for anemia classi cation, achieving an impressive accuracy rate of 94%. Additional techniques [14]for Hb level estimation have employed facial feature extraction alongside Inception V3 for classi cation, as well as the analysis of the near-infrared spectrum of spent dialysis uid to predict anemia [15]. Despite these innovations, non-invasive quanti cation of Hb levels remains an area with limited exploration.…”

Section: Related Workmentioning

confidence: 99%

Automatic Non-Invasive Prediction of Hemoglobin Using a Deep Learning-Assisted Smartphone-Based System

Chen,

Yi,

et al. 2024

Preprint

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Background The context and purpose of this study is to introduce a novel, compact, and efficient system that leverages deep learning and smartphone technology to estimate hemoglobin levels. Accurate measurement of hemoglobin concentration is essential for various medical scenarios, including preoperative evaluations and determining blood loss. Current models, due to their complex parameters, are not well-suited for mobile medical settings, which limits the ability to conduct frequent and rapid testing. Methods The study employed a smartphone application to capture images of the eye, which were then analyzed by a deep neural network trained using invasive blood test data. For the task of eyelid segmentation, the EGE-Unet model was used. The performance of this model was evaluated using statistical metrics including mean intersection over union (MIOU), F1 Score, accuracy, specificity, and sensitivity. For hemoglobin level prediction, the DHA(C3AE) model was employed. The performance of this model was assessed using mean absolute error (MAE), mean absolute percentage error (MAPE), root mean square error (RMSE), and R^2. Results The EGE-Unet model demonstrated robust performance in eyelid segmentation, achieving a MIOU of 0.78, an F1 Score of 0.87, an accuracy of 0.97, a specificity of 0.98, and a sensitivity of 0.86. The DHA(C3AE) model for hemoglobin level prediction yielded promising outcomes with a MAE of 1.34, a MAPE of 2.85, an RMSE of 1.69, and an R^2 of 0.34. The overall size of the model is modest at 1.08M, with a computational complexity of 0.12 FLOPs (G). Conclusions This groundbreaking approach eliminates the necessity for supplementary devices, providing a cost-effective, swift, and accurate method for healthcare professionals to enhance treatment planning and improve patient care in perioperative environments. The proposed system has the potential to enable frequent and rapid testing of hemoglobin levels, which can be particularly beneficial in mobile medical settings. Trial Registration: The clinical trial was registered on the Chinese Clinical Trial Registry (No. ChiCTR2100044138) on March 11, 2021.

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Section: Related Workmentioning

confidence: 99%

Automatic Non-Invasive Prediction of Hemoglobin Using a Deep Learning-Assisted Smartphone-Based System

Chen,

Yi,

et al. 2024

Preprint

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show abstract

“…Another research [ 22 ] effort utilized a Convolutional Neural Network (CNN) analyzing eye images for anemia classification, achieving an impressive accuracy rate of 94%. Additional techniques [ 23 ] for Hb level estimation have employed facial feature extraction alongside Inception V3 for classification, as well as the analysis of the near-infrared spectrum of spent dialysis fluid to predict anemia [ 24 ].…”

Section: Introductionmentioning

confidence: 99%

Real-time non-invasive hemoglobin prediction using deep learning-enabled smartphone imaging

Chen,

Hu,

Zhu

et al. 2024

BMC Med Inform Decis Mak

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Background Accurate measurement of hemoglobin concentration is essential for various medical scenarios, including preoperative evaluations and determining blood loss. Traditional invasive methods are inconvenient and not suitable for rapid, point-of-care testing. Moreover, current models, due to their complex parameters, are not well-suited for mobile medical settings, which limits the ability to conduct frequent and rapid testing. This study aims to introduce a novel, compact, and efficient system that leverages deep learning and smartphone technology to accurately estimate hemoglobin levels, thereby facilitating rapid and accessible medical assessments. Methods The study employed a smartphone application to capture images of the eye, which were subsequently analyzed by a deep neural network trained on data from invasive blood test data. Specifically, the EGE-Unet model was utilized for eyelid segmentation, while the DHA(C3AE) model was employed for hemoglobin level prediction. The performance of the EGE-Unet was evaluated using statistical metrics including mean intersection over union (MIOU), F1 Score, accuracy, specificity, and sensitivity. The DHA(C3AE) model’s performance was assessed using mean absolute error (MAE), mean-square error (MSE), root mean square error (RMSE), and R^2. Results The EGE-Unet model demonstrated robust performance in eyelid segmentation, achieving an MIOU of 0.78, an F1 Score of 0.87, an accuracy of 0.97, a specificity of 0.98, and a sensitivity of 0.86. The DHA(C3AE) model for hemoglobin level prediction yielded promising outcomes with an MAE of 1.34, an MSE of 2.85, an RMSE of 1.69, and an R^2 of 0.34. The overall size of the model is modest at 1.08 M, with a computational complexity of 0.12 FLOPs (G). Conclusions This system presents a groundbreaking approach that eliminates the need for supplementary devices, providing a cost-effective, swift, and accurate method for healthcare professionals to enhance treatment planning and improve patient care in perioperative environments. The proposed system has the potential to enable frequent and rapid testing of hemoglobin levels, which can be particularly beneficial in mobile medical settings. Trial Registration The clinical trial was registered on the Chinese Clinical Trial Registry (No. ChiCTR2100044138) on 20/02/2021.

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“…Other methods have been used to predict Hb levels [15], in which the extracted facial features and inception V3 are employed to classify the Hb level. Unenhanced computed tomography scanning of the thorax [16] and near‐infrared spectrum of spent dialysis fluid [17] methods were used to predict anemia. However, a lack of studies has used the non‐invasive Hb quantification method.…”

Section: Introductionmentioning

confidence: 99%

A self‐supervised causal feature reinforcement learning method for non‐invasive hemoglobin prediction

Xu,

Chen,

et al. 2023

IET Image Processing

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Anemia (hemoglobin (Hb) < 12.0 g/dL) is significantly correlated with many diseases. An invasive technique is the peripheral blood Hb detection method, which is used to examine red and white blood cells and platelets in clinical laboratory settings. However, non‐invasive methods for measuring Hb mainly include low‐precision prediction based on eye images and complex operation prediction based on fundus images. Moreover, these types of anemia testing techniques are time‐consuming, tedious, or prone to errors. Thus, developing a convenient and high‐precision method is vital for predicting Hb concentration. This study proposes self‐supervised causal features using actor‐critical reinforcement learning to improve the model prediction performance. Two networks are proposed: Actor Predictor and Hemoglobin Predictor to predict Hb concentration. Moreover, the model performance is evaluated using different techniques, namely, Mean Absolute Error (MAE) and Mean Square Error (MSE), via real eye image data and a smartphone. This model achieved 1.19(1.01,1.38) on the MAE and 2.25(1.59,2.90) on the MSE, which outperformed previous eye images' Hb prediction methods and was nearly similar to the fundus images' Hb prediction methods. The inference time was less than 0.05 s, making it efficient and accurate for predicting Hb. This model can be used for mobile deployment and health self‐screening.

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Predicting anemia using NIR spectrum of spent dialysis fluid in hemodialysis patients

Cited by 5 publications

References 36 publications

Automatic Non-Invasive Prediction of Hemoglobin Using a Deep Learning-Assisted Smartphone-Based System

Automatic Non-Invasive Prediction of Hemoglobin Using a Deep Learning-Assisted Smartphone-Based System

Real-time non-invasive hemoglobin prediction using deep learning-enabled smartphone imaging

A self‐supervised causal feature reinforcement learning method for non‐invasive hemoglobin prediction

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