Abstract Definitive diagnosis of Craniosynostosis (CS) with computed tomography (CT) is readily available, however, exposure to ionizing radiation is often a hard stop for parents and practitioners. Lowering head CT radiation exposure helps mitigate risks and improves diagnostic utilization. The purpose of the study is to quantify radiation exposure from head CT in patients with CS using a ‘new’ (ultra-low dose) protocol; compare prior standard CT protocol; summarize published reports on cumulative radiation doses from pediatric head CT scans utilizing other low-dose protocols. A retrospective study was conducted on patients undergoing surgical correction of CS, aged less than 2 years, between August 2014 and February 2022. Cumulative effective dose (CED) in mSv was calculated, descriptive statistics were performed, and mean ± SD was reported. A literature search was conducted describing cumulative radiation exposure from head CT in pediatric patients and analyzed for ionizing radiation measurements. Forty-four patients met inclusion criteria: 17 females and 27 males. Patients who obtained head CT using the ‘New’ protocol resulted in lower CED exposure of 0.32 mSv ± 0.07 compared to the prior standard protocol at 5.25 mSv ± 2.79 ( p < 0.0001). Five studies specifically investigated the reduction of ionizing radiation from CT scans in patients with CS via the utilization of low-dose CT protocols. These studies displayed overall CED values ranging from 0.015 mSv to 0.77 mSv. Our new CT protocol resulted in 94% reduction of ionizing radiation. Ultra-low dose CT protocols provide similar diagnostic data without loss of bone differentiation in CS and can be easily incorporated into the workflow of a children’s hospital.

Abstract Cloud computing, machine learning, the Internet of Things, deep learning, and artificial intelligence are used in a variety of areas, including healthcare, transportation, smart cities, and agriculture, to create beneficial results for a variety of challenges in today’s world. This paper focuses on one of these applications in the cloud computing and IoMT domains. Several sensors were implanted in the human body to gather patient-specific information, such as body measurements temp deviations, and many other factors that contribute to changes in blood cells that develop into malignant cells. The major goal of this project is to create a cancer prediction system that uses the IoT to extract information from blood results in order to determine whether they are normal or abnormal. Furthermore, the findings of cancer patients’ blood tests are encrypted and saved in the cloud for quick access by a doctor or healthcare worker through the Internet to handle patient data in a secure manner. The AES technique is used for encryption and decryption in order to offer authentication and security when dealing with cancer patients. Because all of the required cancer treatment information is stored on the cloud, the main focus is on properly handling healthcare data for patients while they are away from home. Using virtual machines, the work completion time is decreased from 450 to 170 min. Simulations are used to test the proposed model’s performance, and the results show that it outperforms alternative options significantly.

Abstract To save patients’ lives, it is important to go for an early diagnosis of intracranial hemorrhage (ICH). For diagnosing ICH, the widely used method is non-contrast computed tomography (NCCT). It has fast acquisition and availability in medical emergency facilities. To predict hematoma progression and mortality, it is important to estimate the volume of intracranial hemorrhage. Radiologists can manually delineate the ICH region to estimate the hematoma volume. This process takes time and undergoes inter-rater variability. In this research paper, we develop and discuss a fine segmentation model and a coarse model for intracranial hemorrhage segmentations. Basically, two different models are discussed for intracranial hemorrhage segmentation. We trained a 2DDensNet in the first model for coarse segmentation and cascaded the coarse segmentation mask output in the fine segmentation model along with input training samples. A nnUNet model is trained in the second fine stage and will use the segmentation labels of the coarse model with true labels for intracranial hemorrhage segmentation. An optimal performance for intracranial hemorrhage segmentation solution is obtained.

Abstract Problem : There is a need for effective and non-invasive techniques for early cancer detection to improve treatment outcomes and patient care. Motivation : This research explores the potential of thermal imaging as a non-invasive technique for cancer detection. Aim : The aim of this study is to investigate thermal imaging as a valuable tool for early cancer detection and its potential to enhance treatment outcomes and patient care. Methodology : The paper discusses the principles of thermal imaging, its advantages and limitations, and its application to various types of cancer. It also presents a review of recent studies in the field. Main results : The findings suggest that thermal imaging holds promise as a valuable tool for early cancer detection. Further impact of those results : The potential application of thermal imaging in cancer detection could lead to improved treatment outcomes and enhance overall patient care. The article also highlights the challenges and future prospects of thermal imaging in this domain.

Abstract Publications overestimating the medical and ecological sequels of a slight anthropogenic increase in the radiation background have been reviewed recently with examples of different organs and pathological conditions. The overestimation contributed to the strangulation of atomic energy. The use of nuclear energy for electricity production is on the agenda today due to the increasing energy needs of humankind. Apparently, certain scientific writers acted in the interests of fossil fuel producers. Health risks and environmental damage are maximal for coal and oil, lower for natural gas, and much lower for atomic energy. This letter is an addition to previously published materials, this time focused on studies of cataracts in radiation-exposed populations in Russia. Selection and self-selection bias are of particular significance. Apparently, the self-reporting rate correlates with dose estimates and/or with professional awareness about radiation-related risks among nuclear workers or radiologic technologists, the latter being associated with their work experience/duration and hence with the accumulated dose. Individuals informed of their higher doses would more often seek medical advice and receive more attention from medics. As a result, lens opacities are diagnosed in exposed people earlier than in the general population. This explains dose-effect correlations proven for the incidence of cataracts but not for the frequency of cataract surgeries. Along the same lines, various pathological conditions are more often detected in exposed people. Ideological bias and the trimming of statistics have not been unusual in the Russian medical sciences. It is known that ionizing radiation causes cataracts; however, threshold levels associated with risks are understudied. In particular, thresholds for chronic and fractionated exposures are uncertain and may be underestimated.

Abstract This study addresses the global health challenge of rectal cancer, aiming to assess the effectiveness of neoadjuvant chemoradiotherapy and its implications for treatment outcomes in 100 retrospectively analyzed patients. The cohort underwent neoadjuvant chemoradiotherapy followed by sphincter-sparing surgery, with recorded parameters including demographics, tumor stage, treatment protocol, and surgical outcomes. Results indicated tumor reduction in 80% of patients, with a 15% complication rate for sphincter-sparing surgery. Pathological examination underscored neoadjuvant treatment’s impact on tumor regression and reduced lymph node metastasis. In conclusion, the study emphasizes the demonstrated efficacy of neoadjuvant chemoradiotherapy in rectal cancer treatment, advocating for a comprehensive approach to managing this condition.

Abstract The growth of computer power is crucial for the development of contemporary information technologies. Artificial intelligence is a powerful instrument for every aspect of contemporary science, the economy, and society as a whole. Further growth in computing potential opens new prospects for biomedicine and healthcare. The promising works on quantum computing make it possible to increase computing power exponentially. While conventional computing relies on the formula with 2 n bits, the simplified vision of quantum computer power is 2 N , where N is a number of logical qubits. With thousandfold or more improvements in computing performance, there will be realistic options for quick protein, genes and other organic molecules 3D fold discoveries, empowering pharmaceutics and biomedical research. Personalized blockchain-based healthcare will become a reality. Medical imaging and instant healthcare data analysis will significantly speed up diagnostics and treatment control. Biomedical digital twin usage will give useful tools to any healthcare practitioner, with options for intraoperative AR and VR micro-manipulations. Nanoscale intrabody bots will be instantly customized and AI-controlled. The smart environment will be enriched with multiple sensors and actuators, giving real control of the air, water, food, and physical health factors. All these possibilities are quickly achievable only in the case of realistic quantum computing options. Even with the ability to reach this stage, there will be questions for the stability of post-quantum society: privacy, ethical issues, and quantum computing control uncertainty. General solutions to these queries will give clues for post-quantum healthcare.

Abstract Objective: This study investigates the efficacy and safety of epidural infiltration with drugs and an oxygen-ozone mixture for treating cervicobrachialgia due to disc-radicular conflict or on a degenerative basis, utilizing both retrospective analysis and direct visualization techniques. Methods: A retrospective study involving 10 patients treated with epidural infiltrations of an oxygen-ozone mixture and cortisone was conducted. The procedures were performed under CT guidance to ensure precise delivery and to monitor the diffusion of the injected substances. Pain levels were assessed using the Numerical Rating Scale (NRS) and treatment efficacy was evaluated based on symptom relief and reduction in NSAID intake. Results: Significant pain reduction was observed post-treatment, with median NRS scores decreasing from 9 (baseline) to 2 (follow-up), and a significant decrease in on-demand NSAID intake. Only one minor complication of a headache was reported. The study also demonstrated the ability of ozone to diffuse through the epidural adipose tissue, potentially enhancing treatment efficacy. Conclusion: The combined use of an oxygen-ozone mixture and cortisone for epidural infiltration is an effective and safe treatment for cervicobrachialgia, offering significant pain relief and minimizing the risk associated with traditional epidural injections. This technique presents a viable non-surgical option for patients suffering from disc-radicular conflict or degenerative conditions.

Abstract In this study, we utilized a convolutional neural network (CNN) trained on microscopic images encompassing the SARS-CoV-2 virus, the protozoan parasite “ plasmodium falciparum ” (causing of malaria in humans), the bacterium “ vibrio cholerae ” (which produces the cholera disease) and non-infected samples (healthy persons) to effectively classify and predict epidemics. The findings showed promising results in both classification and prediction tasks. We quantitatively compared the obtained results by using CNN with those attained employing the support vector machine. Notably, the accuracy in prediction reached 97.5% when using convolutional neural network algorithms.

Abstract This paper provides a comprehensive review of SURF (speeded up robust features) feature descriptor, commonly used technique for image feature extraction. The SURF algorithm has obtained significant popularity because to its robustness, efficiency, and invariance to various image transformations. In this paper, an in-depth analysis of the underlying principles of SURF, its key components, and its use in computer vision tasks such as object recognition, image matching, and 3D reconstruction are proposed. Furthermore, we discuss recent advancements and variations of the SURF algorithm and compare it with other popular feature descriptors. Through this review, the aim is to provide a clear understanding of the SURF feature descriptor and its significance in the area of computer vision.

Abstract Healthcare mobile applications satisfy different aims by frequently exploiting the built-in features found in smart devices. The accessibility of cloud computing upgrades the extra room, whereby substances can be stored on external servers and obtained directly from mobile devices. In this study, we use cloud computing in the mobile healthcare model to reduce the waste of time in crisis healthcare once an accident occurs and the patient operates the application. Then, the mobile application determines the patient’s location and allows him to book the closest medical center or expert in some crisis cases. Once the patient makes a reservation, he will request help from the medical center. This process includes pre-registering a patient online at a medical center to save time on patient registration. The E-Health model allows patients to review their data and the experiences of each specialist or medical center, book appointments, and seek medical advice.

Abstract Medicare, a major healthcare program under the Centers for Medicare & Medicaid Services (CMS) has extended telemedicine services within several states in the US for different specialties for which it reimburses in order to establish a qualitative and accessible healthcare system. In parallel, it has been seen that teleradiology services by American Board Certified radiologists based offshore can significantly supplement healthcare delivery in the US by mitigating the shortage of radiologists and enhance outcomes of patient care especially for after-hours emergency work. Teleradiology can help workflow by improving workload distribution, lowering the cost of reporting, shortening turn-around-time for reports, and improving quality of life for staff. The aim of the article is to provide perspective on Medicare reimbursement of offshore telereporting services. We submit that due to its value proposition and contribution to healthcare, offshore telereporting by American Board Certified Radiologists is worthy of Medicare reimbursement and should be re-evaluated for its credits.

Abstract The current state of the art of health-related quality of life (HRQoL) and quality of life in the animal health industry highlights the limitations of existing methodologies and the potential of artificial intelligence (AI) to overcome these limitations. AI has the potential to revolutionize many aspects of healthcare, including HRQoL assessment, leading to more efficient and accurate measurement and personalized medicine. AI in psychometrics can improve cognitive and behavioral assessments and lead to new insights into animal reactions and perceptions. A proof of concept (POC) study is used to assess the feasibility of an AI-based solution. In the next decade, AI-based HRQoL instruments in veterinary medicine are expected to emerge and become widely distributed, making them easily accessible for practical use in daily practice.