Abstract In recent years, brain health has received increasing attention, but conventional acquisition of brain MRI (magnetic resonance imaging) images still suffer from issues such as missing data, artifacts, and high costs, which hinders research and diagnosis. With the application of deep learning in medical image synthesis, low-cost, efficient, and high-quality medical MRI synthesis techniques have become a prominent research focus and have gradually matured. However, traditional methods for synthesizing magnetic resonance imaging (MRI) mostly rely on generative adversarial networks, which require fine-tuning of parameters and learning rates to achieve stringent Nash equilibrium conditions, leading to problems such as gradient explosions and mode collapse. Building upon the latest research in synthetic models DDPM (denoising diffusion probabilistic model), we propose a novel approach for 2D brain MRI image synthesis based on a lightweight denoising diffusion probabilistic model. This method improves the attention module in the denoising diffusion probabilistic model to make it more lightweight. Additionally, we adopt the smooth L1 loss function as a replacement for the traditional mean absolute error (L1 loss) by comparing the error between the 2D brain MRI images with added noise and the real noise for training the model. Finally, we validate the proposed model on the MRI Brain Tumor Classification dataset, demonstrating that it achieves high-quality synthesis results while significantly reducing the parameter count of the DDPM model.

Abstract Stroke can be mainly categorized into hemorrhagic and ischemic stroke. Intracerebral hemorrhage (ICH) is a subtype of hemorrhagic stroke that is caused due to unconstrained bleeding within the parenchyma of the brain. ICH is one of the major conditions that have a high rate of disease and a high rate of death in a given population. Risk factors for ICH emerged to be age, male gender, hypertension, and intake of alcohol in huge quantities. The frequency of ICH is increased where hypertension is mainly untreated. To improve the prognosis and outcomes of an ICH patient, we need to perform emergent evacuation of blood from the brain parenchyma and prevent edema formation while restricting further neuronal damage due to surgical intervention. Evidence-based guidelines exist for ICH and form the basis for a care framework. The pharmaceutical management of ICH from current literature includes an aggressive reduction in blood pressure, tranexamic acid use, and recombinant activated factor VII administration. In addition, advanced imaging, surgical evacuation of ICH, and minimally invasive surgery techniques for hematoma evacuation could provide great benefits to patients with a large ICH.

Abstract The efficiency evaluation of laboratories, as one of the most significant areas of healthcare, plays a key role in the quality of laboratory management. The classic data envelopment analysis (DEA) models have overlooked intermediate products, internal interactions and dealt with analyzing the network within the “Black Box” mode. This results in the loss of important information, and at times, a considerable modification occurs in efficiency results. This article evaluated the efficiency of some selected medical diagnostic laboratories in the city of Tehran according to the network data envelopment analysis (NDEA) approach. We considered a four-stage structure with additional inputs and undesirable outputs. We obtain the labs’ performance over a period of 6 months in 2022 by the NDEA window analysis process. To this aim, a four-stage structure model of three chief medical diagnostic laboratory processes as the pre-test, the test, and the post-test is designed. We considered sustainability criteria (economic, social, and environmental) to appraise the performance of laboratories, thus helping to improve the social, economic, and environmental problems of medical diagnostic laboratories. By using the Delphi viewpoint, the criteria for efficiency evaluation are achieved. The results showed that laboratory unit No. 22 maintained the highest average overall efficiency, since the high accuracy of this unit’s laboratory results had led to many physicians recommending this unit to their patients. We found that the only laboratory unit No. 20 had a decreasing trend, as it is located in an area that abounds with administrative and educational centers. At the beginning of the exam period, then the summer holidays, and finally the wave of end-of-summer trips, a decline occurs in efficiency over the period of six months.

Abstract Background:  Cardiac resynchronization therapy (CRT) has emerged as an effective treatment for heart failure patients with electrical dyssynchrony. However, accurately predicting which patients will respond to CRT remains a challenge. This study explores the application of deep transfer learning techniques to train a predictive model for CRT response.  Methods:  In this study, the short-time Fourier transform (STFT) technique was employed to transform ECG signals into two-dimensional images. A transfer learning approach was then applied to the MIT-BIT ECG database to pre-train a convolutional neural network (CNN) model. The model was fine-tuned to extract relevant features from the ECG images and then tested on our dataset of CRT patients to predict their response.  Results:  Seventy-one CRT patients were enrolled in this study. The transfer learning model achieved an accuracy of 72% in distinguishing responders from non-responders in the local dataset. Furthermore, the model showed good sensitivity (0.78) and specificity (0.79) in identifying CRT responders. The performance of our model outperformed clinic guidelines and traditional machine learning approaches.  Conclusion:  The utilization of ECG images as input and leveraging the power of transfer learning allows for improved accuracy in identifying CRT responders. This approach offers potential for enhancing patient selection and improving the outcomes of CRT.

Abstract Background: Lung cancer is the highest deadliest disease and second largest disease being diagnosed worldwide. In the age of precision medicine, determining a patient’s genetic status is critical. Finding the percentage of gene mutation of a particular biomarker will help in targeted therapy of a patient at an early stage. Objective: Histopathology images are larger in size which needs to be converted into smaller tiles for the computational purpose. Deep Learning Techniques could be applied on this huge number of histopathological images to derive the probability of gene mutation occurrence in predictive and prognostic biomarkers of lung cancer. Methods: In this work, a deep learning convolutional neural network (CNN) model (InceptionV3) is trained on histopathology images obtained from The Cancer Genome Atlas (TCGA) to accurately predict the mutated genes in lung adenocarcinoma. The convolutional neural network-based model predicts 10 major genetic mutations percentage, i.e., EGFR, FAT1, FAT4, KEAP1, KRAS, LRP1B, NF1, SETBP1, STK11, TP53. Results: InceptionV3 predicted the probability of gene mutation from the histopathology images and categorized the genes as predictive and prognostic. InceptionV3 yielded an accuracy of 82.36% and cross entropy of 37.62%. Conclusion: InceptionV3 was trained on histopathology images to predict gene mutations with an accuracy of 82%. Prediction of gene mutations with different CNN models like AlexNet and ResNet can be explored further.

Abstract This comprehensive investigation and meta-analysis explored the effectiveness and safety of stereotactic radiotherapy and microsurgery in treating vestibular schwannomas. A thorough review of pertinent studies published from 2004 to 2023 was undertaken, examining the outcomes of both Gamma Knife radiosurgery and linear accelerator-based stereotactic irradiation. The primary focus was on assessing tumor control rates, hearing preservation, quality of life, and the long-term impact of treatment. The results suggest that stereotactic radiotherapy holds considerable promise as a well-tolerated treatment option for managing vestibular schwannomas. It demonstrates favorable tumor control rates, the potential to preserve hearing, and a positive influence on patients’ overall well-being. However, the study also emphasizes the importance of vigilant monitoring and assessment due to the challenges associated with tumor pseudo-progression. Further investigation and prospective studies are necessary to refine treatment protocols and validate the presented conclusions.

Abstract The fusion of healthcare and information technology in eHealth informatics has rapidly evolved in recent years, presenting transformative possibilities for healthcare delivery. This article explores advancements, challenges, and emerging trends in this dynamic field, including telemedicine, wearable devices, Artificial intelligence, and data analytics. Despite promising developments such as predictive healthcare and personalized medicine, challenges like data security, interoperability, and ethical concerns must be addressed. Looking forward, the integration of genomics, Virtual Reality, and Argumented Reality is expected to reshape healthcare practices, emphasizing the importance of understanding and navigating these dynamics for an efficient, accessible, and patient-focused healthcare landscape.

Abstract Background: While oxygen extraction fraction (OEF) reflects the underlying variations in cerebral brain oxygen metabolism, tissue voxels having elevated volume fraction of blood vessel network with deoxygenated blood, will apparently contribute to higher cerebral venous blood volume fraction (CVBVF). This Case report examines the difference in intra and peri-tumoral topographical patterns of OEF and CVBVF in cases of a meningioma tumor (Case-I) and a low- grade glioma (Case-II). Methods: Using a “static dephasing regime” BOLD model, we use the BOLD signal model containing parameters representing OEF and CVBVF. For each voxel in the region of interest, the parameters are solved by non-linearly fitting the signal model using paired differences between logarithms of the measured echo signal after inhomogeneity correction. Results: OEF and CVBVF maps in Case-I reveals an interesting phenomenon in the peritumoral parenchyma showing reduced OEF and increased CVBVF levels. The uniformly low CVBVF and elevated OEF in Case-II indicates that even with less density of vasculature, the region extracts higher amount of oxygen. Conclusion: While topographic mapping using qBOLD revealed elevated levels of intra-tumoral OEF for both cases, the pattern of CVBVF variation was uniformly low in Case-II.