Improving Non-invasive Hemoglobin Measurement Accuracy Using Nonparametric Models

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

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.

Point-of-care haemoglobin accuracy and transfusion outcomes in non-cardiac surgery at a Canadian tertiary academic hospital: protocol for the PREMISE observational study

INTRODUCTION

Transfusions in surgery can be life-saving interventions, but inappropriate transfusions may lack clinical benefit and cause harm. Transfusion decision-making in surgery is complex and frequently informed by haemoglobin (Hgb) measurement in the operating room. Point-of-care testing for haemoglobin (POCT-Hgb) is increasingly relied on given its simplicity and rapid provision of results. POCT-Hgb devices lack adequate validation in the operative setting, particularly for Hgb values within the transfusion zone (60-100 g/L). This study aims to examine the accuracy of intraoperative POCT-Hgb instruments in non-cardiac surgery, and the association between POCT-Hgb measurements and transfusion decision-making.

METHODS AND ANALYSIS

PREMISE is an observational prospective method comparison study. Enrolment will occur when adult patients undergoing major non-cardiac surgery require POCT-Hgb, as determined by the treating team. Three concurrent POCT-Hgb results, considered as index tests, will be compared with a laboratory analysis of Hgb (lab-Hgb), considered the gold standard. Participants may have multiple POCT-Hgb measurements during surgery. The primary outcome is the difference in individual Hgb measurements between POCT-Hgb and lab-Hgb, primarily among measurements that are within the transfusion zone. Secondary outcomes include POCT-Hgb accuracy within the entire cohort, postoperative morbidity, mortality and transfusion rates. The sample size is 1750 POCT-Hgb measurements to obtain a minimum of 652 Hgb measurements <100 g/L, based on an estimated incidence of 38%. The sample size was calculated to fit a logistic regression model to predict instances when POCT-Hgb are inaccurate, using 4 g/L as an acceptable margin of error.

ETHICS AND DISSEMINATION

Institutional ethics approval has been obtained by the Ottawa Health Science Network-Research Ethics Board prior to initiating the study. Findings from this study will be published in peer-reviewed journals and presented at relevant scientific conferences. Social media will be leveraged to further disseminate the study results and engage with clinicians.

Non-invasive Hemoglobin Measurement Predictive Analytics with Missing Data and Accuracy Improvement Using Gaussian Process and Functional Regression Model

Recent use of noninvasive and continuous hemoglobin (SpHb) concentration monitor has emerged as an alternative to invasive laboratory-based hematological analysis. Unlike delayed laboratory based measures of hemoglobin (HgB), SpHb monitors can provide real-time information about the HgB levels. Real-time SpHb measurements will offer healthcare providers with warnings and early detections of abnormal health status, e.g., hemorrhagic shock, anemia, and thus support therapeutic decision-making, as well as help save lives. However, the finger-worn CO-Oximeter sensors used in SpHb monitors often get detached or have to be removed, which causes missing data in the continuous SpHb measurements. Missing data among SpHb measurements reduce the trust in the accuracy of the device, influence the effectiveness of hemorrhage interventions and future HgB predictions. A model with imputation and prediction method is investigated to deal with missing values and improve prediction accuracy. The Gaussian process and functional regression methods are proposed to impute missing SpHb data and make predictions on laboratory-based HgB measurements. Within the proposed method, multiple choices of sub-models are considered. The proposed method shows a significant improvement in accuracy based on a real-data study. Proposed method shows superior performance with the real data, within the proposed framework, different choices of sub-models are discussed and the usage recommendation is provided accordingly. The modeling framework can be extended to other application scenarios with missing values.

Estimation and Identification of Nonlinear Parameter of Motion Index Based on Least Squares Algorithm

Parameter identification is an important branch of automatic control. Due to its special function, it has been widely used in various fields, especially the modeling of complex systems or systems whose parameters are not easy to determine. With the development of control technology, the scale of the control object is getting larger and larger, which makes the calculation amount of the identification algorithm larger and larger. For the nonlinear system with complex structure, especially the nonlinear system containing the product of unknown parameters, the number of parameters of the over-parameterized identification method increases greatly, and the calculation amount of the identification algorithm also increases sharply. Therefore, a parameter estimation method with a small amount of calculation is explored. The results show that the proposed method can overcome the phenomenon of “data saturation”, thus improving the parameter identification results.