Current Issue : April-June Volume : 2025 Issue Number : 2 Articles : 5 Articles
This paper reviews the main research on Optical Coherence Tomography (OCT), focusing on the progress and advancements made by researchers over the past three decades in its methods and medical imaging applications. By analyzing existing studies and developments, this review aims to provide a foundation for future research in the field....
The project discusses the development of a deep learning model to detect osteoporosis from dental panoramic X-Ray images. It provides an in-depth understanding of human bone structure, osteoporosis, its symptoms, causes, prevalence, and risk factors. The project also explains bone density measurement using dual-energy X-ray absorptiometry (DEXA) and the application of artificial intelligence (AI) and machine learning (ML) in medical imaging. The study uses panoramic dental X-rays to evaluate AI technology in dental imaging and classification of mandible inferior cortical based on Klemetti and Kolmakow criteria. The model architecture consists of convolutional, pooling, fully connected, ReLU, and Softmax layers. Dropout and early stopping are added to the model. The training process uses the train-test approach with 100 epochs and a batch size of 32, and performance evaluation measures such as accuracy, sensitivity, specificity, and F1-score are used to assess the classifier’s performance. The findings and methodology provide a comprehensive understanding of the application of deep learning in the detection of osteoporosis from dental panoramic X-Ray images, and the study demonstrates a robust approach to implementing AI in medical imaging for osteoporosis detection....
Background/Objectives: The prevalence of bronchiectasis is increasing globally, and early detection using chest imaging has been encouraged to improve its prognosis. However, the sensitivity of a chest X-ray as a screening tool remains unclear. This study examined the chest X-ray features predictive of bronchiectasis. Methods: We retrospectively reviewed the chest X-rays of patients with bronchiectasis diagnosed using high-resolution computed tomography who visited our institute from January 2013 to March 2020. Patients with cardiac pacemakers, lung cancer, and interstitial pneumonia, which might bias the detection of bronchiectasis, were excluded. Two respiratory physicians independently determined the presence or absence of potential features reflecting bronchiectasis, including a vague cardiac silhouette on chest X-rays. Results: The study enrolled 130 patients, including 72 women (55.4%), with a mean age of 72 years. The features observed on chest X-rays included granular shadows (88.5%, n = 115), vague cardiac silhouettes (48.5%, n = 64), nodular shadows (45.4%, n = 59), a tram-track appearance (35.4%, n = 46), pleural thickening (26.9%, n = 35), vague diaphragm silhouettes (25.4%, n = 33), and a ring sign (24.6%, n = 32). The kappa values for these features were 0.271, 0.344, 0.646, 0.256, 0.312, 0.514, and 0.376, respectively. Conclusions: Although traditional chest X-ray features believed to reflect bronchiectasis, such as the tram-track appearance or ring sign, were not frequently seen, vague cardiac silhouettes and granular shadows had high positivity rates, indicating their potential utility for bronchiectasis screening. However, the interobserver concordance rates were unsatisfactory....
Background Conventional hip joint MRI scans necessitate lengthy scan durations, posing challenges for patient comfort and clinical efficiency. Previously, accelerated imaging techniques were constrained by a trade-off between noise and resolution. Leveraging deep learning-based reconstruction (DLR) holds the potential to mitigate scan time without compromising image quality. Methods We enrolled a cohort of sixty patients who underwent DL-MRI, conventional MRI, and No-DL MRI examinations to evaluate image quality. Key metrics considered in the assessment included scan duration, overall image quality, quantitative assessments of Relative Signal-to-Noise Ratio (rSNR), Relative Contrast-to-Noise Ratio (rCNR), and diagnostic efficacy. Two experienced radiologists independently assessed image quality using a 5-point scale (5 indicating the highest quality). To gauge interobserver agreement for the assessed pathologies across image sets, we employed weighted kappa statistics. Additionally, the Wilcoxon signed rank test was employed to compare image quality and quantitative rSNR and rCNR measurements. Results Scan time was significantly reduced with DL-MRI and represented an approximate 66.5% reduction. DL-MRI consistently exhibited superior image quality in both coronal T2WI and axial T2WI when compared to both conventional MRI (p < 0.01) and No-DL-MRI (p < 0.01). Interobserver agreement was robust, with kappa values exceeding 0.735. For rSNR data, coronal fat-saturated(FS) T2WI and axial FS T2WI in DL-MRI consistently outperformed No-DL-MRI, with statistical significance (p < 0.01) observed in all cases. Similarly, rCNR data revealed significant improvements (p < 0.01) in coronal FS T2WI of DL-MRI when compared to No-DL-MRI. Importantly, our findings indicated that DL-MRI demonstrated diagnostic performance comparable to conventional MRI. Conclusion Integrating deep learning-based reconstruction methods into standard clinical workflows has the potential to the promise of accelerating image acquisition, enhancing image clarity, and increasing patient throughput, thereby optimizing diagnostic efficiency....
Objective This study aims to investigate the predictive effectiveness of bedside lung ultrasound score (LUS) in conjunction with rapid shallow breathing index (RSBI) and oxygenation index (P/F ratio) for weaning pediatric patients from mechanical ventilation. Methods This was a retrospective study. Eighty-two critically ill pediatric patients, who were admitted to the Pediatric Intensive Care Unit (PICU) and underwent mechanical ventilation from January 2023 to April 2024, were enrolled in this study. Prior to weaning, all patients underwent bedside LUS, with concurrent measurements of their RSBI and P/F ratio. Patients were followed up for weaning outcomes and categorized into successful and failed weaning groups based on these outcomes. Differences in clinical baseline data, LUS scores, RSBI and P/F ratios between the two groups were compared. The predictive value of LUS scores, RSBI and P/F ratios for weaning outcomes was assessed using receiver operating characteristic (ROC) curves and the area under the curve (AUC). Results Out of the 82 subjects, 73 (89.02%) successfully weaned, while 9 (10.98%) failed. No statistically significant differences were observed in age, gender, BMI, and respiratory failure-related comorbidities between the successful and failed weaning groups (P > 0.05). Compared to the successful weaning group, the failed weaning group exhibited longer hospital and intubation durations, higher LUS and RSBI, and lower P/F ratios, with statistically significant differences (P < 0.05). An LUS score ≥ 15.5 was identified as the optimal cutoff for predicting weaning failure, with superior predictive power compared to RSBI and P/F ratios. The combined use of LUS, RSBI and P/F ratios for predicting weaning outcomes yielded a larger area under the curve, indicating higher predictive efficacy. Conclusion The LUS demonstrates a high predictive value for the weaning outcomes of pediatric patients on mechanical ventilation....
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