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.

Effect of lidocaine or dexmedetomidine on postoperative recovery in elder patients undergoing colorectal surgery.

BACKGROUND: Opioid sparing techniques have been shown to promote gastrointestinal recovery, shorten length of stay (LOS), and reduce opioid-related complications. We investigated whether intraoperative intravenous lidocaine or dexmedetomidine infusion could improve gastrointestinal recovery in elderly patients undergoing laparoscopic colorectal surgery. METHODS: Ninety-six patients aged 65 years or older who underwent elective laparoscopic colorectal resection were randomly allocated into the following three groups: the control group (N.=32) received an equal volume of saline, the lidocaine group (N.=32) received intraoperative intravenous lidocaine infusion, and the dexmedetomidine group (N.=32) received intraoperative intravenous dexmedetomidine infusion. The primary outcome was time to first feces. Secondary outcomes were time to first flatus, postoperative pain intensity, patient-controlled intravenous analgesia (PCIA) consumption, postoperative inflammatory response, postoperative complications, anesthetic adverse events, and LOS. RESULTS: The lidocaine group had a significantly shorter time to first flatus (24.6 [IQR, 14.4-48.8] hours vs. 48.1 [IQR, 30.0-67.1] hours; adjusted P=0.022) and time to first feces (48.0 [IQR, 19.0-67.8] hours vs. 74.8 [IQR, 40.3-113.3] hours; adjusted P=0.032) than the control group. However, no significant differences were found between dexmedetomidine and control group for first flatus or first feces. Intraoperative sufentanil consumption and postoperative plasma concentrations of IL-6 were significantly lower in lidocaine group and dexmedetomidine group compared with control group. No difference could be observed in postoperative PCIA consumption, pain scores, postoperative complications, anesthetic adverse events, and LOS among the groups. CONCLUSIONS: Intraoperative intravenous lidocaine infusion accelerated return of the bowel function in elderly patients undergoing elective colorectal surgery.

Two-stage hemoglobin prediction based on prior causality.

Introduction: Perioperative hemoglobin (Hb) levels can influence tissue metabolism. For clinical physicians, precise Hb concentration greatly contributes to intraoperative blood transfusion. The reduction in Hb during an operation weakens blood's oxygen-carrying capacity and poses threats to multiple systems and organs of the whole body. Patients can die from perioperative anemia. Thus, a timely and accurate non-invasive prediction for patients' Hb content is of enormous significance. Method: In this study, targeted toward the palpebral conjunctiva images in perioperative patients, a non-invasive model for predicting Hb levels is constructed by means of deep neural semantic segmentation and a convolutional network based on a priori causal knowledge, then an automatic framework was proposed to predict the precise concentration value of Hb. Specifically, according to a priori causal knowledge, the palpebral region was positioned first, and patients' Hb concentration was subjected to regression prediction using a neural network. The model proposed in this study was experimented on using actual medical datasets. Results: The R 2 of the model proposed can reach 0.512, the explained variance score can reach 0.535, and the mean absolute error is 1.521. Discussion: In this study, we proposed to predict the accurate hemoglobin concentration and finally constructed a model using the deep learning method to predict eyelid Hb of perioperative patients based on the a priori casual knowledge.

Optimization of anesthetic decision-making in ERAS using Bayesian network.

Enhanced recovery after surgery (ERAS) can accelerate patient recovery. However, little research has been done on optimizing the ERAS-related measures and how the measures interact with each other. The Bayesian network (BN) is a graphical model that describes the dependencies between variables and is also a model for uncertainty reasoning. In this study, we aimed to develop a method for optimizing anesthetic decisions in ERAS and then investigate the relationship between anesthetic decisions and outcomes. First, assuming that the indicators used were independent, the effects of combinations of single indicators were analyzed based on BN. Additionally, the impact indicators for outcomes were selected with statistical tests. Then, based on the previously selected indicators, the Bayesian network was constructed using the proposed structure learning method based on Strongly Connected Components (SCC) Local Structure determination by Hill Climbing Twice (LSHCT) and adjusted according to the expert's knowledge. Finally, the relationship is analyzed. The proposed method is validated by the real clinical data of patients with benign gynecological tumors from 3 hospitals in China. Postoperative length of stay (LOS) and total cost (TC) were chosen as the outcomes. Experimental results show that the ERAS protocol has some pivotal indicators influencing LOS and TC. Identifying the relationship between these indicators can help anesthesiologists optimize the ERAS protocol and make individualized decisions.