2
views
0
recommends
+1 Recommend
0 collections
    0
    shares
      • Record: found
      • Abstract: found
      • Article: found
      Is Open Access

      Treatment of Fracture of the Calcaneus via Bone Axial X-Ray Image-Based Minimally Invasive Approach

      research-article

      Read this article at

      Bookmark
          There is no author summary for this article yet. Authors can add summaries to their articles on ScienceOpen to make them more accessible to a non-specialist audience.

          Abstract

          To discuss the values of two bone axial X-ray image-based minimally invasive approach surgeries in the diagnosis and treatment of fracture of the calcaneus, 80 patients diagnosed with fracture of the calcaneus by bone axial X-ray examination were selected and divided equally into the minimally invasive longitudinal approach (MILA) group (40 cases) and the sinus tarsal approach (STA) group (40 cases). Besides, the duration of operation, the incidence of complications, the time-to-start weight training, and the American Orthopaedic Foot and Ankle Society (AOFAS) foot function scoring system between the patients in the two groups were compared. The results showed that the duration of operation and incidence of complications among the patients in the MILA group (42.87 ± 5.12 minutes, 20%) were both superior to those among the patients in the STA group (60.43 ± 7.31 minutes, 32.5%). The time-to-start weight training in the MILA group was 5.2 weeks, which was obviously shorter than that in the STA group (5.7 weeks). The difference in AOFAS scores between the two groups was not significant. The walking pavement score in the MILA group (4.2 ± 0.37 points) was slightly higher than that in the STA group (3.3 ± 0.45 points), and the differences demonstrated statistical meaning ( P < 0.05). To sum up, the bone axial X-ray image is an essential examination method of diagnosing fracture of the calcaneus. The two minimally invasive methods both showed good clinical therapeutic effects. The operation of MILA was relatively shorter with fewer complications and is worthy of being promoted as an effective treatment method of fracture of the calcaneus.

          Related collections

          Most cited references21

          • Record: found
          • Abstract: found
          • Article: found
          Is Open Access

          Fuzzy System Based Medical Image Processing for Brain Disease Prediction

          The present work aims to explore the performance of fuzzy system-based medical image processing for predicting the brain disease. The imaging mechanism of NMR (Nuclear Magnetic Resonance) and the complexity of human brain tissues cause the brain MRI (Magnetic Resonance Imaging) images to present varying degrees of noise, weak boundaries, and artifacts. Hence, improvements are made over the fuzzy clustering algorithm. A brain image processing and brain disease diagnosis prediction model is designed based on improved fuzzy clustering and HPU-Net (Hybrid Pyramid U-Net Model for Brain Tumor Segmentation) to ensure the model safety performance. Brain MRI images collected from a Hospital, are employed in simulation experiments to validate the performance of the proposed algorithm. Moreover, CNN (Convolutional Neural Network), RNN (Recurrent Neural Network), FCM (Fuzzy C-Means), LDCFCM (Local Density Clustering Fuzzy C-Means), and AFCM (Adaptive Fuzzy C-Means) are included in simulation experiments for performance comparison. Results demonstrate that the proposed algorithm has more nodes, lower energy consumption, and more stable changes than other models under the same conditions. Regarding the overall network performance, the proposed algorithm can complete the data transmission tasks the fastest, basically maintaining at about 4.5 s on average, which performs remarkably better than other models. A further prediction performance analysis reveals that the proposed algorithm provides the highest prediction accuracy for the Whole Tumor under DSC (Dice Similarity Coefficient), reaching 0.936. Besides, its Jaccard coefficient is 0.845, proving its superior segmentation accuracy over other models. In a word, the proposed algorithm can provide higher accuracy, a more apparent denoising effect, and the best segmentation and recognition effect than other models while ensuring energy consumption. The results can provide an experimental basis for the feature recognition and predictive diagnosis of brain images.
            Bookmark
            • Record: found
            • Abstract: not found
            • Article: not found
            Is Open Access

            Medical image fusion method by deep learning

              Bookmark
              • Record: found
              • Abstract: not found
              • Article: not found

              Analysis of healthcare big data

                Bookmark

                Author and article information

                Contributors
                Journal
                Comput Math Methods Med
                Comput Math Methods Med
                cmmm
                Computational and Mathematical Methods in Medicine
                Hindawi
                1748-670X
                1748-6718
                2022
                1 July 2022
                : 2022
                : 3012589
                Affiliations
                1Department of Orthopedics, The Second Affiliated Hospital (Jiande Branch), Zhejiang University School of Medicine, Jiande, Hangzhou, 311600 Zhejiang, China
                2Department of Orthopedics, The Second Affiliated Hospital of Zhejiang University School of Medicine, Hangzhou, 311000 Zhejiang, China
                Author notes

                Academic Editor: Ahmed Faeq Hussein

                Author information
                https://orcid.org/0000-0002-6221-2485
                https://orcid.org/0000-0003-4298-6413
                https://orcid.org/0000-0003-3283-7369
                https://orcid.org/0000-0002-9644-5033
                https://orcid.org/0000-0002-2218-6753
                https://orcid.org/0000-0002-8369-1518
                Article
                10.1155/2022/3012589
                9270132
                1e2189ba-47db-467e-a028-11581fe7decd
                Copyright © 2022 Jie Xiao et al.

                This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

                History
                : 24 April 2022
                : 9 June 2022
                : 13 June 2022
                Categories
                Research Article

                Applied mathematics
                Applied mathematics

                Comments

                Comment on this article