MANAGEMENT OF SEVERE XEROSTOMIA AND ORAL CANDIDIASIS IN PATIENT WITH VALVULAR HEART DISEASE: A CASE REPORT

Background: Valvular heart disease is a heart valve disorder that needs complex multiple medications by administering certain drugs that cannot be replaced with other drugs because of different mechanisms of action. Beta-blockers and Angiotensin-Converting Enzyme inhibitors are drugs of choice for valvular heart disease, with diuretics and antipsychotics can cause xerostomia. Valvular heart disease patient who has a severe infection or sepsis needs long-term antibiotic treatment. Xerostomia and long-term antibiotic treatment are predisposing factors for oral candidiasis. Objective: to discuss oral candidiasis and severe xerostomia because of multiple medications in valvular heart disease patients. Case: A 58-year-old male was referred from Cardiology and Vascular Medicine Department with a chief complaint of sore tongue and pain at swallowing since 3 days ago with dry sensation of the mouth. Extraoral examination revealed dry and exfoliative lips, intraoral examination revealed fissured and lobulated tongue and white plaques could be scraped off leaving erythematous area oropharynx and tongue. The diagnoses were oropharyngeal candidiasis and severe xerostomia score of 8 according to the Chalacombe scale. Case Management: Patient was treated with nystatin, chlorine dioxide, 0.12 % chlorhexidine digluconate mouthwash, and vaseline album. Oral candidiasis was disappeared on the 22nd day of treatment. Conclusion: Xerostomia and oral candidiasis in patients with valvular heart disease require appropriate therapy, more intensive monitoring by considering the patient's general condition, and interprofessional team collaboration in the therapy of the main disease. Keywords: Oral candidiasis, Valvular heart disease, Xerostomia

Use of a Pre-Trained Neural Network for Automatic Classification of Arterial Doppler Flow Waveforms: A Proof of Concept

Background: Arterial Doppler flow waveform analysis is a tool recommended for the management of lower extremity peripheral arterial disease (PAD). To standardize the waveform analysis, classifications have been proposed. Neural networks have shown a great ability to categorize data. The aim of the present study was to use an existing neural network to evaluate the potential for categorization of arterial Doppler flow waveforms according to a commonly used classification. Methods: The Pareto efficient ResNet-101 (ResNet-101) neural network was chosen to categorize 424 images of arterial Doppler flow waveforms according to the Simplified Saint-Bonnet classification. As a reference, the inter-operator variability between two trained vascular medicine physicians was also assessed. Accuracy was expressed in percentage, and agreement was assessed using Cohen’s Kappa coefficient. Results: After retraining, ResNet-101 was able to categorize waveforms with 83.7 ± 4.6% accuracy resulting in a kappa coefficient of 0.79 (0.75–0.83) (CI 95%), compared with a kappa coefficient of 0.83 (0.79–0.87) (CI 95%) between the two physicians. Conclusion: This study suggests that the use of transfer learning on a pre-trained neural network is feasible for the automatic classification of images of arterial Doppler flow waveforms.