Literature DB >> 31368588

Diagnosis and treatment of otomycosis in southern China.

Yongqi Li1, Lifeng He1.   

Abstract

To analyse and discuss the clinical features and pathogenic characteristics, diagnosis and treatment of patients with otomycosis in southern China. Two hundred fifty-six patients from southern China diagnosed with otomycosis were randomly separated into two groups: the drug filling group and drug smearing group. Patients in the drug filling group were first examined and then had the pathogenic secretions in their external auditory canals cleared by otoendoscopy. Then, the local antifungal cream triamcinolone acetonide clotrimazole was injected into the external auditory canal. The same treatment was undertaken 1 week later and repeated once or twice more. Patients in the drug smearing group were also treated by otoendoscopy. Then, they were told to smear their external auditory canals once per day with the antifungal cream. All cases were followed for more than 6 months after the 3- to 4-week treatment. The main symptoms and otoendoscopic examination were used to evaluate the prognosis. Aspergillus was the commonest fungus. The cure rate was 93% in the drug filling group and 81% in the drug smearing group. Otomycosis is very common in southern China, but it lacks characteristic features in its early stages. Once diagnosed, the local lesions in the external auditory canal should be cleared thoroughly using otoendoscopy, and then, the local antifungal cream is injected into external auditory canal. The cure rate can be significantly improved with the foregoing treatment.
© 2019 Blackwell Verlag GmbH.

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Keywords:  diagnosis; external auditory canal; local filling; otomycosis; treatment

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Year:  2019        PMID: 31368588     DOI: 10.1111/myc.12979

Source DB:  PubMed          Journal:  Mycoses        ISSN: 0933-7407            Impact factor:   4.377


  2 in total

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Journal:  Mycopathologia       Date:  2021-03-15       Impact factor: 2.574

2.  Efficient and accurate diagnosis of otomycosis using an ensemble deep-learning model.

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Journal:  Front Mol Biosci       Date:  2022-08-19
  2 in total

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