Automated blood volume estimation in surgical drains for clinical decision support.

OBJECTIVE: Monitoring Jackson Pratt and Hemovac drains plays a crucial role in assessing a patient's recovery and identifying potential postoperative complications. Accurate and regular monitoring of the blood volume in the drain is essential for making decisions about patient care. However, transferring blood to a measuring cup and recording it is a challenging task for both patients and doctors, exposing them to bloodborne pathogens such as the human immunodeficiency virus (HIV), hepatitis B virus (HBV), and hepatitis C virus (HCV). To automate the recording process with a non-contact approach, we propose an innovative approach that utilizes deep learning techniques to detect a drain in a photograph, compute the blood level in the drain, estimate the blood volume, and display the results on both web and mobile interfaces. MATERIALS AND METHODS: Our system employs semantic segmentation on images taken with mobile phones to effectively isolate the blood-filled portion of the drain from the rest of the image and compute the blood volume. These results are then sent to mobile and web applications for convenient access. To validate the accuracy and effectiveness of our system, we collected the Drain Dataset, which consists of 1,004 images taken under various background and lighting conditions. RESULTS: With an average error rate of less than 5% in milliliters, our proposed approach achieves highly accurate blood level detection and estimation, as demonstrated by our trials on this dataset. The system also exhibits robustness to variations in lighting conditions and drain shapes, ensuring its applicability in different clinical scenarios. CONCLUSIONS: The proposed automated blood volume estimation system can significantly reduce the time and effort required for manual measurements, enabling healthcare professionals to focus on other critical tasks. The dataset and annotations are available at: https://www.kaggle.com/datasets/ayenahin/liquid-volume-detection-from-drain-images and the code for the web application is available at https://github.com/itsjustaplant/AwesomeProject.git.

COMT rs4680 and DRD2 rs6275 variants and their association with YMRS scores in children with early-onset bipolar disorder

Background and objectives: Bipolar disorder (BD) is a clinical status with at least one manic, hypomanic or mixed attacks. Genetic factors take part significantly in early-onset BD (EOBD). Dopamine receptors (DRD) act in neurological mechanisms like motivation, learning, memory, and, control of neuroendocrine signaling. DRD2 receptor has been reported to influence the stability of DRD2 transcript. Catechol-O-Methyl transferase (COMT) inactivates catecholamines and Val158Met variation on COMT has effects on COMT activity. This study aims to explore DRD2 and COMT variants in the clinical development of EOBD.MethodsIn this case-control study, 102 EOBD patients and 168 healthy control subjects were used. DRD2 rs6275 and COMT Val158Met variations were detected by real-time polymerase chain reaction (RT-PCR). Young Mania Rating Scale (YMRS) was utilized to determine the EOBD severity.ResultsFor DRD2 rs6275 and COMT Val158Met polymorphisms, no significant relationship was observed in the genotype and allele frequencies between patient and control groups. Nevertheless, TT genotype carriers of DRD2 rs6275 polymorphism demonstrated significantly increased YMRS scores when compared with CC and CT genotype carriers (p = 0.039). Nevertheless, no significant difference was observed between COMT Val158Met genotypes and YMRS scores.ConclusionsWe suggest that the DRD2 rs6275 TT variant can be associated with symptom severity in children with EOBD and can have a clinical significance in EOBD pathogenesis. However, these results need to be confirmed with larger samples of patient and control groups. (AU)