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Hepatocellular carcinoma (HCC) stands as the most prevalent form of primary liver cancer, predominantly affecting patients with chronic liver diseases such as hepatitis B or C-induced cirrhosis. Diagnosis typically involves blood tests (assessing liver functions and HCC biomarkers), imaging procedures such as Computed Tomography (CT) and Magnetic Resonance Imaging (MRI), and liver biopsies requiring the removal of liver tissue for laboratory analysis. However, these diagnostic methods either entail lengthy lab processes, require expensive imaging equipment, or involve invasive techniques like liver biopsies. Hence, there exists a crucial need for rapid, cost-effective, and noninvasive techniques to characterize HCC, whether in serum or tissue samples. In this study, we developed a spiral sensor implemented on a printed circuit board (PCB) technology that utilizes impedance spectroscopy and applied it to 24 tissues and sera samples as proof of concept. This newly devised circuit has successfully characterized HCC and normal tissue and serum samples. Utilizing the distinct dielectric properties between HCC cells and serum samples versus the normal samples across a specific frequency range, the differentiation between normal and HCC samples is achieved. Moreover, the sensor effectively characterizes two HCC grades and distinguishes cirrhotic/non-cirrhotic samples from tissue specimens. In addition, the sensor distinguishes cirrhotic/non-cirrhotic samples from serum specimens. This pioneering study introduces Electrical Impedance Spectroscopy (EIS) spiral sensor for diagnosing HCC and liver cirrhosis in clinical serum—an innovative, low-cost, rapid (< 2 min), and precise PCB-based technology without elaborate sample preparation, offering a novel non-labeled screening approach for disease staging and liver conditions.

An accurate tumour segmentation in brain images is a complicated task due to the complext structure and irregular shape of the tumour. In this letter, our contribution is twofold: (1) a lightweight brain tumour segmentation network (LBTS‐Net) is proposed for a fast yet accurate brain tumour segmentation; (2) transfer learning is integrated within the LBTS‐Net to fine‐tune the network and achieve a robust tumour segmentation. To the best of knowledge, this work is amongst the first in the literature which proposes a lightweight and tailored convolution neural network for brain tumour segmentation. The proposed model is based on the VGG architecture in which the number of convolution filters is cut to half in the first layer and the depth‐wise convolution is employed to lighten the VGG‐16 and VGG‐19 networks. Also, the original pixel‐labels in the LBTS‐Net are replaced by the new tumour labels in order to form the classification layer. Experimental results on the BRATS2015 database and comparisons with the state‐of‐the‐art methods confirmed the robustness of the proposed method achieving a global accuracy and a Dice score of 98.11% and 91%, respectively, while being much more computationally efficient due to containing almost half the number of parameters as in the standard VGG network.

Analyzing rock and underground layers is known as drill core lithology. The extracted core sample helps not only in exploring the core properties but also reveals the lithology of the entire surrounding area. Automating rock identification from drill cuttings is a key element for efficient reservoir characterization, replacing the current subjective and time-consuming manual process. The recent advancements in computer hardware and deep learning technology have enabled the automatic classification of various applications, and lithology is not an exception. This work aims to design an automated method for rock image classification using deep learning technologies. A novel CNN (Convolution Neural Network) is proposed for lithology classification in addition to thorough comparison with benchmark CNN models. The proposed CNN model has the advantageous of having very low complexity while maintaining high accuracy. Experimental results on rock mages taken from the “digitalrocksportal” database demonstrate the ability of the proposed method to classify three classes, carbonate, sandstone and shale rocks, with high accuracy, and comparisons with related work demonstrated the efficiency of the proposed model, with more than 98% saving in parameters.

Background Musculoskeletal disorders are common health problems worldwide. Several factors cause these symptoms, including ergonomics and other individual considerations. Computer users are prone to repetitive strain injuries that increase the risk of developing musculoskeletal symptoms (MSS). Radiologists are susceptible to developing MSS because they work long hours analysing medical images on computers in an increasingly digitalised field. This study aimed to identify the prevalence of MSS among Saudi radiologists and the associated risk factors. Methods This study was a cross-sectional, non-interventional, self-administered online survey. The study was conducted on 814 Saudi radiologists from various regions in Saudi Arabia. The study's outcome was the presence of MSS in any body region that limited participation in routine activities over the previous 12 months. The results were descriptively examined using binary logistic regression analysis to estimate the odds ratio (OR) of participants who had disabling MSS in the previous 12 months. All university, public, and private radiologists received an online survey containing questions about work surroundings, workload (e.g., spent at a computer workstation), and demographic characteristics. Results The prevalence of MSS among the radiologists was 87.7%. Most of the participants (82%) were younger than 40 years of age. Radiography and computed tomography were the most common imaging modalities that caused MSS (53.4% and 26.8%, respectively). The most common symptoms were neck pain (59.3%) and lower back pain (57.1%). After adjustment, age, years of experience, and part-time employment were significantly associated with increased MSS (OR = .219, 95% CI = .057–.836; OR = .235, 95% CI = 087–.634; and OR = 2.673, 95% CI = 1.434–4.981, respectively). Women were more likely to report MSS than males (OR = 2.12, 95% CI = 1.327–3.377). Conclusions MSS are common among Saudi radiologists, with neck pain and lower back pain being the most frequently reported symptoms. Gender, age, years of experience, type of imaging modality, and employment status were the most common associated risk factors for developing MSS. These findings are vital for the development of interventional plans to reduce the prevalence of musculoskeletal complaints in clinical radiologists.

Breast cancer has the highest fatality for women compared with other types of cancer. Generally, early diagnosis of cancer is crucial to increase the chances of successful treatment. Early diagnosis is possible through physical examination, screening, and obtaining a biopsy of the dubious area. In essence, utilizing histopathology slides of biopsies is more efficient than using typical screening methods. Nevertheless, the diagnosing process is still tiresome and is prone to human error during slide preparation, such as when dyeing and imaging. Therefore, a novel method is proposed for diagnosing breast cancer into benign or malignant in a magnification‐specific binary (MSB) classification. Besides, the introduced method classifies each type into four subclasses in a magnification‐specific multi‐category (MSM) fashion. The proposed method involves normalizing the hematoxylin and eosin stains to enhance colour separation and contrast. Then, two types of novel features—deep and shallow features—are extracted using two deep structure networks based on DenseNet and Xception. Finally, a multi‐classifier method based on the maximum value is utilized to achieve the best performance. The proposed method is evaluated using the BreakHis histopathology data set, and the results in terms of diagnostic accuracy are promising, achieving 99% and 92% in terms of MSB and MSM, respectively, compared with recent state‐of‐the‐art methods reported in the survey conducted by Benhammou  on the BreakHis data set using deep learning and texture‐based models.

In the face of increasing flood risks intensified by climate change, accurate flood inundation mapping is pivotal for effective disaster management. This study introduces a novel explainable deep learning architecture designed to generate precise flood inundation maps from diverse satellite data sources. A comprehensive evaluation of the proposed model is conducted, comparing it with state-of-the-art models across various fusion configurations of Multispectral Optical and Synthetic Aperture Radar (SAR) images. The proposed model consistently outperforms other models across both Sentinel-1 and Sentinel-2 images, achieving an Intersection Over Union (IOU) of 0.5862 and 0.7031, respectively. Furthermore, analysis of the different fusion combinations reveals that the use of Sentinel-1 in combination with RGB, NIR, and SWIR achieves the highest IOU of 0.7053 and that the inclusion of the SWIR band has the greatest positive impact on the results. Gradient-weighted class activation mapping is employed to provide insights into its decision-making processes, enhancing transparency and interpretability. This research contributes significantly to the field of flood inundation mapping, offering an efficient model suitable for diverse applications. This study not only advances flood inundation mapping but also provides a valuable tool for improved understanding of deep learning decision-making in this area, ultimately contributing to improved disaster management strategies.

Background: The diagnostic performance of numerous clinical specimens to diagnose COVID-19 through RT-PCR techniques is very important, and the test result outcome is still unclear. This review aimed to analyze the diagnostic performance of clinical samples for COVID-19 detection by RT-PCR through a systematic literature review process. Methodology: A compressive literature search was performed in PubMed/Medline, Scopus, Embase, and Cochrane Library from inception to November 2022. A snowball search on Google, Google Scholar, Research Gate, and MedRxiv, as well as bibliographic research, was performed to identify any other relevant articles. Observational studies that assessed the clinical usefulness of the RT-PCR technique in different human samples for the detection or screening of COVID-19 among patients or patient samples were considered for this review. The primary outcomes considered were sensitivity and specificity, while parameters such as positive predictive value (PPV), negative predictive value (NPV), and kappa coefficient were considered secondary outcomes. Results: A total of 85 studies out of 10,213 non-duplicate records were included for the systematic review, of which 69 articles were considered for the meta-analysis. The meta-analysis indicated better pooled sensitivity with the nasopharyngeal swab (NPS) than saliva (91.06% vs. 76.70%) and was comparable with the combined NPS/oropharyngeal swab (OPS; 92%). Nevertheless, specificity was observed to be better with saliva (98.27%) than the combined NPS/OPS (98.08%) and NPS (95.57%). The other parameters were comparable among different samples. The respiratory samples and throat samples showed a promising result relative to other specimens. The sensitivity and specificity of samples such as nasopharyngeal swabs, saliva, combined nasopharyngeal/oropharyngeal, respiratory, sputum, broncho aspirate, throat swab, gargle, serum, and the mixed sample were found to be 91.06%, 76.70%, 92.00%, 99.44%, 86%, 96%, 94.4%, 95.3%, 73.63%, and above 98; and 95.57%, 98.27%, 98.08%, 100%, 37%, 100%, 100%, 97.6%, and above 97, respectively. Conclusions: NPS was observed to have relatively better sensitivity, but not specificity when compared with other clinical specimens. Head-to-head comparisons between the different samples and the time of sample collection are warranted to strengthen this evidence.

Preeclampsia is associated with the incidence of common fetal problems including intra-uterine growth restriction (IUGR), premature delivery oligohydramnios, placental abruption, fetal discomfort, and intrauterine fetal death. Pregnant women are not well-informed about preeclampsia, including its symptoms, risk factors, and consequences. The aim of the current study is to evaluate the awareness of preeclampsia and its associated factors among women in the Al Baha region, Saudi Arabia. An observational cross-sectional design was employed to assess the awareness of preeclampsia and its associated factors among women in the Al Baha region of Saudi Arabia. Data was collected from April 2023 to September 2023. A questionnaire was designed to gather information on participants' sociodemographic characteristics (such as age, educational level, and residency) and their awareness of preeclampsia, including knowledge about signs/symptoms, risk factors, and complications. In the current study, we included 485 pregnant women. The majority of participants were aged 40 years or older (37.5%), followed by those aged 35-39 (20.4%). Among the participants, 70.9% confirmed that they had heard about pre-eclampsia before. The most common signs and symptoms were high blood pressure (47.4%), increased protein in urine (40.2%), continuous headache (39.2%), and vomiting/nausea (40.0%). Participants demonstrated awareness of obesity (29.7%), diabetes mellitus (35.5%), chronic hypertension (47.0%), and chronic kidney disease (31.3%) as major risk factors. Participants were aware of potential risks such as kidney disorders (34.6%), heart disorders (23.7%), and preterm delivery (50.9%). The analysis reveals that younger participants below 20 years old (3.3%) and lower educational levels (5.6%) had lower awareness of preeclampsia compared to older age groups. The findings of this study highlight a reasonable level of preeclampsia awareness and knowledge among Saudi Arabian women residing in the Al Baha region. While the majority of participants were familiar with preeclampsia, there were significant knowledge gaps regarding the precise symptoms, risk factors, and consequences of the condition.

In urolithiasis, urinary calculi are formed in the urinary system. Stone development does not initially result in any symptoms, but later renal colic, flank pain, hematuria, obstruction of urine flow, and/or hydronephrosis may indicate renal stone disease. In addition to age, gender, ethnicity, and local climate, urolithiasis can be caused by several other factors. The prevalence and recurrence rate of kidney stone disease is rising globally, while few effective treatment options currently exist. Between June and October 2022, a cross-sectional study was conducted. An electronic questionnaire subdivided into three categories was used to determine the prevalence and identify the factors that increase the likelihood of developing urolithiasis among the population in Bisha. The collected data were reviewed and analyzed via IBM Corp. Released 2012. IBM SPSS Statistics for Windows, Version 21.0. Armonk, NY: IBM Corp. A total of 1,002 participants filled out the questionnaire. The age of the participants ranged from 18 to over 60 years, with an average age of 26.1 ± 13.9 years. There were 451 female participants (45%), and 927 (92.5%) were Saudis. According to the participants' body mass index, 98 (9.8%) were underweight, 388 (38.7%) were normal weight, 300 (29.9%) were overweight, and 216 (21.6%) were obese. The total number of participants with urolithiasis was 161 (16.1%), and 420 (41.9%) had a family history of renal stones. Urolithiasis was found to be significantly associated with family history, smoking, diabetes, hypertension, hyperthyroidism, gout, and chronic kidney disease. Older age and female gender were also associated with the risk of having urolithiasis. This study found urolithiasis to be highly prevalent among the Bisha population. In terms of risk factors, body mass index, smoking, and diabetes were the most significant. Based on the findings of this study, the authors recommend more public education regarding urolithiasis and its risk factors, emphasizing the importance of preventing the disease and the ways of treating urolithiasis through medical campaigns and social media.

BackgroundType 2 diabetes is a chronic condition that affects the way the body processes blood sugar (glucose). This issue is of considerable importance in the field of public health, as it has a global impact on a substantial number of individuals. The primary emphasis in the management of type 2 diabetes is centered around achieving glycemic control, implementing lifestyle adjustments, and employing pharmaceutical therapies as preventive measures or for the purpose of managing problems that may arise as a result of the disease.AimThis research aimed to investigate the prevalence of sleep-belated issues among individuals diagnosed with type 2 diabetes.MethodologyA total of 230 participants with type 2 diabetes patients of primary healthcare in Abha city whose age is ≥18 years were included in the study. The data collection process involved the distribution of a self-administered questionnaire that assessed various aspects of sleep disturbances, including difficulties in falling asleep, waking up during the night, excessive daytime sleepiness, and restless legs or leg muscle cramps. The questionnaire also collected demographic information and data on potential risk factors such as alcohol consumption, caffeine consumption, and smoking/tobacco product use. Data analysis was conducted using chi-square tests and significance levels were set at p < 0.05.ResultsThe findings revealed a prevalence of sleep disturbances among individuals with type 2 diabetes. Difficulties in falling asleep and waking up during the night were reported by a substantial proportion of participants, and a notable number experienced excessive daytime sleepiness. Restless legs or leg muscle cramps that interrupted sleep were experienced occasionally by 16.5% and frequently by 8.7% of the participants. The study also found a significant association between the presence of sleep problems and lower sleep quality ratings. However, no significant associations were found between sleep disturbances and the duration of type 2 diabetes or the examined risk factors.ConclusionThe findings from this study emphasize the detrimental effects of sleep disturbances on sleep quality and suggest that improving sleep quality can positively influence the overall health and well-being of individuals with type 2 diabetes.