The Coronavirus disease 2019 (COVID-19) is an infectious disease spreading rapidly and uncontrollably throughout the world. The critical challenge is the rapid detection of Coronavirus infected ...people. The available techniques being utilized are body-temperature measurement, along with anterior nasal swab analysis. However, taking nasal swabs and lab testing are complex, intrusive, and require many resources. Furthermore, the lack of test kits to meet the exceeding cases is also a major limitation. The current challenge is to develop some technology to non-intrusively detect the suspected Coronavirus patients through Artificial Intelligence (AI) techniques such as deep learning (DL). Another challenge to conduct the research on this area is the difficulty of obtaining the dataset due to a limited number of patients giving their consent to participate in the research study. Looking at the efficacy of AI in healthcare systems, it is a great challenge for the researchers to develop an AI algorithm that can help health professionals and government officials automatically identify and isolate people with Coronavirus symptoms. Hence, this paper proposes a novel method CoVIRNet (COVID Inception-ResNet model), which utilizes the chest X-rays to diagnose the COVID-19 patients automatically. The proposed algorithm has different inception residual blocks that cater to information by using different depths feature maps at different scales, with the various layers. The features are concatenated at each proposed classification block, using the average-pooling layer, and concatenated features are passed to the fully connected layer. The efficient proposed deep-learning blocks used different regularization techniques to minimize the overfitting due to the small COVID-19 dataset. The multiscale features are extracted at different levels of the proposed deep-learning model and then embedded into various machine-learning models to validate the combination of deep-learning and machine-learning models. The proposed CoVIR-Net model achieved 95.7% accuracy, and the CoVIR-Net feature extractor with random-forest classifier produced 97.29% accuracy, which is the highest, as compared to existing state-of-the-art deep-learning methods. The proposed model would be an automatic solution for the assessment and classification of COVID-19. We predict that the proposed method will demonstrate an outstanding performance as compared to the state-of-the-art techniques being used currently.
The COVID-19 pandemic has enhanced the adoption of virtual learning after the urgent suspension of traditional teaching. Different online learning strategies were established to face this learning ...crisis. The present descriptive cross-sectional study was conducted to reveal the different digital procedures implemented by the College of Medicine at Qassim University for better student performance and achievement.
The switch into distance-based learning was managed by the digitalization committee. Multiple online workshops were conducted to the staff and students about the value and procedures of such a shift. New procedures for online problem-based learning (PBL) sessions were designed. Students’ satisfaction was recorded regarding the efficiency of live streaming educational activities and online assessment.
The students were satisfied with the overall shift into this collaborative e-learning environment and the new successful procedures of virtual PBL sessions. The digital learning tools facilitated the performance of the students and their peer sharing of knowledge. The role of informatics computer technologies was evident in promoting the students, research skills, and technical competencies.
The present work elaborated on the procedures and privileges of the transformation into digitalized learning, particularly the PBL sessions, which were appreciated by the students and staff. It recommended the adoption of future online theoretical courses as well as the development of informatics computer technologies.
Objective: To investigate the medical students’ performance with and perception towards different multimedia medical imaging tools.
Method: The cross-sectional study was conducted at the College of ...Medicine, Qassim University, Saudi Arabia, from 2019 to 2020, and comprised third year undergraduate medical students during the academic year 2019-2020. The students were divided into tow groups. Those receiving multimedia-enhanced problem-based learning sessions were in intervention group A, while those receiving traditional problem-based learning sessions were in control group B. Scores of the students in the formative assessment at the end of the sessions were compared between the groups. Students’ satisfaction survey was also conducted online and analysed. Data was analysed using SPSS 21.
Result: Of the 130 medical students, 75(57.7%) were males and 55(42.3%) were females. A significant increase in the mean scores was observed for both male and female students in group A compared to those in group B (p<0.05). The perception survey was filled up by 100(77%) students, and open-ended comments were obtained from 88(88%) of them. Overall, 69(74%) subjects expressed satisfaction with the multimedia-enhanced problem-based learning sessions.
Conclusions: Radiological and pathological images enhanced the students’ understanding, interaction and critical thinking during problem-based learning sessions.
Key Words: Radiological images, PBL sessions, Medical students, Qassim University, Medical imaging.
A healthy 49-year-old nonsmoker lady, who was found to have an incidental finding of a lung lesion on a chest X-ray. A Chest CT scan was performed and revealed left upper lobe, 1.5 cm solitary nodule ...with ground glass borders that highly suspicious for Bronchioloalveolar carcinoma and warranted further investigation to rule out malignancy. The FDG PET and/or CT scan was performed for staging and further evaluation and it displayed avidity of the nodule with a standardized uptake value (SUV) of 6.2, no abnormal uptake elsewhere in the body. CT guided biopsy was arranged and the histopathology result revealed eosinophilic pneumonia.
Carpal tunnel syndrome (CTS) is a common condition characterized by compression of the median nerve (MN) within the carpal tunnel. Accurate diagnosis and assessment of CTS severity are crucial for ...appropriate management decisions. This study aimed to investigate the combined diagnostic utility of B-mode ultrasound (US) and shear wave elastography (SWE) for assessing the severity of CTS in comparison to electrodiagnostic tests (EDT).
This prospective observational study was conducted over 9-month periods at a tertiary care hospital. A total of 48 patients (36 females, 12 males; mean age 44 ± 10.9 years; age range 28-57 years) with clinically suspected CTS were enrolled. All patients underwent EDT, US, and SWE. Based on the EDT results, CTS cases were categorized into four groups: mild, moderate, severe, and negative. The cross-sectional area (CSA) and elasticity (E) of the MN were measured at the tunnel inlet (CSAu and Eu) and pronator quadratus region (CSAo and Eo). The differences (CSAu-CSAo and Eu-Eo) were calculated. The primary outcomes were the diagnostic performance of CSAu, CSAu-CSAo, Eu, and Eu-Eo in differentiating moderate/severe from mild/negative CTS compared to EDT findings. Secondary outcomes included a correlation of US/SWE parameters with EDT grades and between each other. ANOVA, correlation, regression, and receiver operating characteristic (ROC) curve analyses were performed.
CSAu and CSAu-CSAo increased progressively with worsening CTS severity. E measurements were significantly higher in moderate-to-severe CTS compared to mild or negative cases. The combined metric of CSAu-CSAo at a 5 mm threshold exhibited enhanced performance, with a higher sensitivity (83.3%), specificity (100%), and area under the curve (AUC) (0.98), surpassing the results of CSAu when used independently. Similarly, the SWE measurements indicated that Eu-Eo at a 56.1kPa cutoff achieved an AUC of 0.95, with a sensitivity of 93.3% and specificity of 94.4%, outperforming the metrics for Eu when used alone, which had an AUC of 0.93, with identical sensitivity and specificity values (93.3% and 94.4%, respectively).
The integration of ultrasound, shear wave elastography, and electrodiagnostic tests provides a comprehensive approach to evaluate anatomical and neurological changes and guide management decisions for CTS.
A healthy 52-year-old man started to have few minutes spells of palpitation, tachycardia, hot flashes, and chest tightness. He had a lab work-up for carcinoid, including 5-hydroxyindoleacetic acid ...(5-HIAA), which was negative. Months later, his symptoms became worse which warranted further investigation to exclude carcinoid disease. Gallium-68 DOTATATE positron emission tomography combined with computed tomography (PET/CT) scanning was performed to assess the patient for carcinoid tumor. It showed foci of radiotracer avidity in the thoracic and lumber spine. Magnetic resonance imaging (MRI) for the vertebral lesions showed atypical hemangioma. Subsequently, follow-up serum chromogranin A testing was negative.
This report delineates the clinical progression of a renal oncocytoma in a 38-year-old female, initially asymptomatic, over a three-year follow-up period following her treatment for papillary thyroid ...cancer. The timeline of this case is as follows: In 2016, the patient was treated with total thyroidectomy and I131 for thyroid cancer. During an annual follow-up, an incidental renal mass was detected via FDG PET/CT, initially characterized as a benign, non-FDG-avid renal oncocytoma. Over two years, this lesion demonstrated a remarkable increase in FDG uptake and a slight growth in size, coupled with new I131 uptake in subsequent scans. These findings led to a reassessment of the diagnosis, initially suggesting a potential small renal cell carcinoma (RCC). Histopathological analysis eventually confirmed the diagnosis of oncocytoma. This case is notable for the tumor's unusual metabolic evolution and the challenges it posed in differential diagnosis.
Malignant pleural effusion (MPE) is a manifestation of advanced cancer that requires a prompt and accurate diagnosis. Ultrasonography (US) and computed tomography (CT) are valuable imaging techniques ...for evaluating pleural effusions; however, their relative predictive ability for a malignant origin remains debatable. This prospective study aimed to compare chest US with CT findings as predictors of malignancy in patients with undiagnosed exudative pleural effusion. Fifty-four adults with undiagnosed exudative pleural effusions underwent comprehensive clinical evaluation including chest US, CT, and histopathologic biopsy. Blinded radiologists evaluated the US and CT images for features suggestive of malignancy, based on predefined criteria. Diagnostic performance measures were calculated using histopathology as a reference standard. Of the 54 patients, 33 (61.1%) had MPEs confirmed on biopsy. No significant differences between US and CT were found in detecting parietal pleural abnormalities, lung lesions, chest wall invasion, or liver metastasis. US outperformed CT in identifying diaphragmatic pleural thickening ≥10 mm (33.3% vs. 6.1%,
< 0.001) and nodularity (45.5% vs. 3%,
< 0.001), whereas CT was superior for mediastinal thickening (48.5% vs. 15.2%,
= 0.002). For diagnosing MPE, diaphragmatic nodularity detected by US had 45.5% sensitivity and 100% specificity, whereas CT mediastinal thickening had 48.5% sensitivity and 90.5% specificity. Both US and CT demonstrate reasonable diagnostic performance for detecting MPE, with particular imaging findings favoring a malignant origin. US may be advantageous for evaluating diaphragmatic pleural involvement, whereas CT is more sensitive to mediastinal abnormalities.
Understanding the consistency of pituitary macroadenomas is crucial for neurosurgeons planning surgery. This retrospective study aimed to evaluate the utility of diffusion-weighted imaging (DWI) and ...the apparent diffusion coefficient (ADC) as non-invasive imaging modalities for predicting the consistency of pituitary macroadenomas. This could contribute to appropriate surgical planning and therefore reduce the likelihood of incomplete resections. The study included 45 patients with pathologically confirmed pituitary macroadenomas. Conventional MRI sequences, DWIs, ADC maps, and pre- and post-contrast MRIs were performed. Two neuroradiologists assessed all of the images. Neurosurgeons assessed the consistency of the tumor macroscopically, and histopathologists examined it microscopically. The MRI findings were compared with postoperative data. According to the operative data, macroadenomas were divided into the two following categories based on their consistency: aspirable (n = 27) and non-aspirable tumors (n = 18). A statistically significant difference in DWI findings was found when comparing macroadenomas of different consistencies (
< 0.001). Most aspirable macroadenomas (66.7%) were hyperintense according to DWI and hypointense on ADC maps, whereas most non-aspirable macroadenomas (83.3%) were hypointense for DWI and hyperintense on ADC maps. At a cut-off value of 0.63 × 10
mm
/s, the ADC showed a sensitivity of 85.7% and a specificity of 75% for the detection of non-aspirable macroadenomas (AUC, 0.946). The study concluded that DWI should be routinely performed in conjunction with ADC measurements in the preoperative evaluation of pituitary macroadenomas. This approach may aid in surgical planning, ensure that appropriate techniques are utilized, and reduce the risk of incomplete resection.
PDF
