3 results about "Vestibular aqueduct" patented technology

The invention belongs to the technical field of traditional Chinese medicines, and particularly relates to a traditional Chinese medicine composition for preventing or treating progressive hearing loss of large vestibular aqueduct syndrome as well as a preparation method and application of the traditional Chinese medicine composition. The traditional Chinese medicine composition is prepared from fructus psoraleae, herba cistanche, rhizoma drynariae, poria peel, polyporus umbellatus, cortex mori radicis, plantain herb, radix salviae miltiorrhizae, ligusticum wallichii, caulis spatholobi and honey-fried licorice root. The traditional Chinese medicine composition disclosed by the invention has the effects of tonifying kidney and strengthening primordial qi, and tonifying spleen and promoting urination, and is mainly used for preventing or treating patients with progressive hearing loss caused by large vestibular aqueduct syndrome. Compared with western medicine glucocorticoid, the traditional Chinese medicine composition has no adverse reaction, the health risk is reduced, and the treatment tolerance and compliance of patients are improved. A standardized scheme for preventing or treating progressive hearing loss of large vestibular aqueduct syndrome by traditional Chinese medicine is formed, clinical popularization is facilitated, more patients are benefited, a reference basis is provided for subsequent research, and related medical research is promoted.

The invention relates to the technical field of medical assistance, and discloses a large vestibular aqueduct syndrome prediction model construction method and a large vestibular aqueduct syndrome prediction method.The method comprises the steps that a medical history information data set, a hearing examination data set, an iconography examination data set and a genetic examination data set are obtained, based on the medical history information dataset, the hearing examination dataset, the iconography examination dataset and the genetic examination dataset, features related to the large vestibular aqueduct syndrome are extracted respectively, and feature datasets are obtained; and training a machine learning model based on the feature data set, and obtaining a large vestibular aqueduct syndrome prediction model after training is completed. The constructed large vestibular aqueduct syndrome prediction model can provide diagnosis help for subjects who have not clear diagnosis of the large vestibular aqueduct syndrome yet, reduce missed diagnosis and misdiagnosis of the large vestibular aqueduct syndrome caused by insufficient experience of clinicians, and can also provide diagnosis assistance for subjects who have already diagnosed as the large vestibular aqueduct syndrome. A prediction of hearing loss progression is provided.