Computer-Aided Diagnosis of Duchenne Muscular Dystrophy Based on Texture Pattern Recognition on Ultrasound Images Using Unsupervised Clustering Algorithms and Deep Learning

OBJECTIVE

The feasibility of using deep learning in ultrasound imaging to predict the ambulatory status of patients with Duchenne muscular dystrophy (DMD) was previously explored for the first time. The present study further used clustering algorithms for the texture reconstruction of ultrasound images of DMD data sets and analyzed the difference in echo intensity between disease stages.

METHODS

k-means (Kms) and fuzzy c-means (FCM) clustering algorithms were used to reconstruct the DMD data-set textures. Each image was reconstructed using seven texture-feature categories, six of which were used as the primary analysis items. The task of automatically identifying the ambulatory function and DMD severity was performed by establishing a machine-learning model.

RESULTS

The experimental results indicated that the Gaussian Naïve Bayes and k-nearest neighbors classification models achieved an accuracy of 86.78% in ambulatory function classification. The decision-tree model achieved an identification accuracy of 83.80% in severity classification. A deep convolutional neural network model was established as the main structure of the deep-learning model while automatic auxiliary interpretation tasks of ambulatory function and severity were performed, and data augmentation was used to improve the recognition performance of the trained model. Both the visual geometry group (VGG)-16 and VGG-19 models achieved 98.53% accuracy in ambulatory-function classification. The VGG-19 model achieved 92.64% accuracy in severity classification.

CONCLUSION

Regarding the overall results, the Kms and FCM clustering algorithms were used in this study to reconstruct the characteristic texture of the gastrocnemius muscle group in DMD, which was indeed helpful in quantitatively analyzing the deterioration of the gastrocnemius muscle group in patients with DMD at different stages. Subsequent combination of machine-learning and deep-learning technologies can automatically and accurately assist in identifying DMD symptoms and tracking DMD deterioration for long-term observation.

Water-based sol-gel synthesis of hydroxyapatite: process development

Hydroxyapatite (HA) ceramics were synthesized using a sol-gel route with triethyl phosphite and calcium nitrate as phosphorus and calcium precursors, respectively. Two solvents, water and anhydrous ethanol, were used as diluting media for HA sol preparation. The sols were stable and no gelling occurred in ambient environment for over 5 days. The sols became a white gel only after removal of the solvents at 60 degrees C. X-ray diffraction showed that apatitic structure first appeared at a temperature as low as 350 degrees C. The crystal size and the HA content in both gels increase with increasing calcination temperature. The type of initial diluting media (i.e., water vs. anhydrous ethanol) did not affect the microstructural evolution and crystallinity of the resulting HA ceramic. The ethanol-based sol dip-coated onto a Ti substrate, followed by calcination at 450 degrees C, was found to be porous with pore size ranging from 0.3 to 1 microm. This morphology is beneficial to the circulation of physiological fluid when the coating is used for biomedical applications. The satisfactory adhesion between the coating and substrate suggests its suitability for load-bearing uses.