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Current Medical Imaging - Online First

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4 results

    • "An Integrated Approach using YOLOv8 and ResNet, SeResNet & Vision Transformer (ViT) Algorithms based on ROI Fracture Prediction in X-ray Images of the Elbow"

      https://doi.org/10.2174/0115734056309890240912054616
      Available online: 02 October 2024
      Introduction:

      In this study, we harnessed three cutting-edge algorithms' capabilities to refine the elbow fracture prediction process through X-ray image analysis. Employing the YOLOv8 (You only look once) algorithm, we first identified Regions of Interest (ROI) within the X-ray images, significantly augmenting fracture prediction accuracy.

      Methods:

      Subsequently, we integrated and compared the ResNet, the SeResNet (Squeeze-and-Excitation Residual Network) ViT (Vision Transformer) algorithms to refine our predictive capabilities. Furthermore, to ensure optimal precision, we implemented a series of meticulous refinements. This included recalibrating ROI regions to enable finer-grained identification of diagnostically significant areas within the X-ray images. Additionally, advanced image enhancement techniques were applied to optimize the X-ray images' visual quality and structural clarity.

      Results:

      These methodological enhancements synergistically contributed to a substantial improvement in the overall accuracy of our fracture predictions. The dataset utilized for training, testing & validation, and comprehensive evaluation exclusively comprised elbow X-ray images, where predicting the fracture with three algorithms: Resnet50; accuracy 0.97, precision 1, recall 0.95, SeResnet50; accuracy 0.97, precision 1, recall 0.95 & ViT-B-16 with high accuracy of 0.99, precision same as the other two algorithms, with a recall of 0.95.

      Conclusion:

      This approach has the potential to increase the precision of diagnoses, lessen the burden of radiologists, easily integrate into current medical imaging systems, and assist clinical decision-making, all of which could lead to better patient care and health outcomes overall.

    • Prenatal Three-Dimensional Ultrasound Diagnosis of Dural Sinus Arteriovenous Malformation: An Unusual Case Report

      https://doi.org/10.2174/0115734056310875240918043257
      Available online: 02 October 2024
      Background:

      Dural sinus arteriovenous malformation is an uncommon intracranial vascular malformation. The affected cases may suffer from severe neurological injury. Prenatal ultrasound has been used to diagnose fetal intracranial vascular abnormality, but prenatal three-dimensional (3D) ultrasound presents a very rare anomaly; an arteriovenous malformation of the dural sinus has not been reported.

      Objective:

      This study aimed to emphasize the diagnostic value of 3D ultrasound in the fetus with dural sinus arteriovenous malformation.

      Case Presentation:

      A 38-year-old woman was referred for targeted fetal ultrasonography at 37 weeks of gestation due to an ultrasound that showed a cystic lesion in the posterior cranial fossa. The fetus demonstrated obvious dilatation of the torcular herophili, bilateral transverse sinuses, and bilateral sigmoid sinuses, appearing as a novel bull's horn sign on 3D ultrasound. After birth, cerebral angiography confirmed the diagnosis of dural arteriovenous fistula (DAVF) in the occipital sinus region.

      Conclusion:

      3D ultrasound is an appealing method for prenatal diagnosis of dural sinus arteriovenous malformation.

    • Whether the Liver-to-Portal Vein Ratio is Applicable for Evaluating the European Society of Gastrointestinal and Abdominal Radiology Hepatobiliary Phase in Gd-EOB-DTPA-Enhanced MRI?

      https://doi.org/10.2174/0115734056318603240910115315
      Available online: 02 October 2024
      Purpose:

      This study aimed to verify whether the Liver-to-portal Ratio (LPR) can assess the adequacy of the Hepatobiliary Phase (HBP) for patients with different liver functions.

      Methods:

      A total of 125 patients were prospectively enrolled in the study and graded into the non-cirrhosis group (45), Child-Pugh A group (40), and Child-Pugh B/C group (40). The LPR on HBP was calculated after eight HBPs were obtained within 5-40 minutes. The adequate HBP was determined according to the 2016 European Society of Gastrointestinal and Abdominal Radiology (ESGAR) consensus statement. The differences in LPR and lesions’ conspicuity between 10-min HBP and adequate HBP were analyzed by paired -test and Wilcoxon signed-rank test, respectively. The chi-square test was used to test the difference in proportion with LPR larger than 1.462 between 10-min HBP and adequate HBP.

      Results:

      The differences in LPR and lesions’ conspicuity between 10-min HBP and adequate HBP were significant in Child-Pugh A and Child-Pugh B/C groups ( < 0.05), except for the non-cirrhosis group ( > 0.05). The differences in proportion with LPR larger than 1.462 between 10-min HBP and adequate HBP were not statistically significant in all groups (all > 0.05).

      Conclusion:

      The adequate HBP obtained according to the 2016 ESGAR consensus statement could provide larger LPR and better lesions’ conspicuity than 10-min HBP, especially for cirrhotic patients; however, the efficacy of using an LPR cutoff of 1.462 as the standard of the adequate HBP may be compromised in patients with cirrhosis.

    • Muscle CT Radiomics is Feasible in the Identification of Gout

      Authors: Ye Zeng, Chunlin Xiang and Gang Wu
      https://doi.org/10.2174/0115734056313937240816070503
      Available online: 02 September 2024
      Objective:

      The aim of this study was to investigate the feasibility of muscle CT radiomics in identifying gout.

      Materials and Methods:

      A total of 30 gout patients and 20 non-gout cases with CT examinations of ankles were analyzed by using the methods of CT radiomics. CT radiomics features of the soleus muscle were extracted using the software of a 3D slicer, and then gout cases and non-gout cases were compared. The radiomics features that were significantly different between the two groups were then processed with machine learning methods. Receiver operating characteristic curve analysis was used to evaluate the diagnostic performance.

      Results:

      Five CT radiomics features were significantly different between gout cases and non-gout cases (P < 0.05). In the logic regression, the AUC, sensitivity, specificity, and accuracy were 0.738, 77% (46/60), 70% (28/40), and 74% (74/100), respectively. In the Random forest, Xgboost, and support vector machine analysis, the accuracy was 0.901, 0.833, and 0.875, respectively.

      Conclusion:

      From this study, it can be concluded that muscle CT radiomics is feasible in identifying gout.

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