Article Per Year (5 Year)
p-Index From 2020 - 2025
0.444
P-Index
This Author published in this journals
All Journal Narra J Indonesian Journal of Case Reports
Enitan, Seyi Samson
Unknown Affiliation
Biochemistry, Genetics & Molecular Biology Health Professions Immunology & microbiology Medicine & Pharmacology Neuroscience Public Health

Published : 2 Documents Claim Missing Document
Claim Missing Document
Check
Articles

Found 2 Documents
Search

 1 
Attitude towards Zika among frontline physicians in a dengue-endemic country: A preliminary cross-sectional study in Indonesia Yufika, Amanda; Anwar, Samsul; Maulana, Reza; Wahyuniati, Nur; Ramadana, Rizki R.; Ikram, Ikram; Mudatsir, Mudatsir; Utomo, Prattama S.; Te, Haypheng; Enitan, Seyi Samson; Sirinam, Salin; Müller, Ruth; Setiawan, Abdul Malik
Narra J Vol. 1 No. 1 (2021): April 2021
Publisher : Narra Sains Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52225/narraj.v1i1.32

Abstract

In dengue-endemic countries such as Indonesia, Zika may be misdiagnosed as dengue, leading to underestimates of Zika disease and less foreknowledge of pregnancy-related complications such as microcephaly. Objective: To assess the attitudes of frontline physicians in a dengue-endemic country toward testing for Zika infection among patients with dengue-like illnesses. Methods: A cross-sectional online survey was conducted among general practitioners (GPs) in Indonesia. The survey assessed their attitude and also collected sociodemographic data, characteristics of their medical education, professional background, and workplace, and exposure to Zika cases. A two-step logistic regression analysis was used to assess possible variables associated with these attitudes. Results: A total of 370 GPs were included in the final analysis of which 70.8% had good attitude. Unadjusted analyses suggested that GPs who were 30 years old or older and those who had medical experience five years or longer had lower odds of having a positive attitude compared to those who aged younger than 30 years and those who had medical experience less than five years, OR: 0.58; 95%CI: 0.37, 0.91 and OR: 0.55; 95%CI: 0.35, 0.86, respectively. No explanatory variable was associated with attitude in the fully adjusted model. Conclusion: Our findings point to younger GPs with a shorter medical experience being more likely to consider testing for Zika infection among their patients presenting with dengue-like illnesses. Strategic initiatives may be needed to enhance older or longer-experienced physicians' capacity in diagnosing Zika infection.
Explainable Artificial Intelligence in Medical Imaging: A Case Study on Enhancing Lung Cancer Detection through CT Images Noviandy, Teuku Rizky; Maulana, Aga; Zulfikar, Teuku; Rusyana, Asep; Enitan, Seyi Samson; Idroes, Rinaldi
Indonesian Journal of Case Reports Vol. 2 No. 1 (2024): June 2024
Publisher : Heca Sentra Analitika

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.60084/ijcr.v2i1.150

Abstract

This study tackles the pressing challenge of lung cancer detection, the foremost cause of cancer-related mortality worldwide, hindered by late detection and diagnostic limitations. Aiming to improve early detection rates and diagnostic reliability, we propose an approach integrating Deep Convolutional Neural Networks (DCNN) with Explainable Artificial Intelligence (XAI) techniques, specifically focusing on the Residual Network (ResNet) architecture and Gradient-weighted Class Activation Mapping (Grad-CAM). Utilizing a dataset of 1,000 CT scans, categorized into normal, non-cancerous, and three types of lung cancer images, we adapted the ResNet50 model through transfer learning and fine-tuning for enhanced specificity in lung cancer subtype detection. Our methodology demonstrated the modified ResNet50 model's effectiveness, significantly outperforming the original architecture in accuracy (91.11%), precision (91.66%), sensitivity (91.11%), specificity (96.63%), and F1-score (91.10%). The inclusion of Grad-CAM provided insightful visual explanations for the model's predictions, fostering transparency and trust in computer-assisted diagnostics. The study highlights the potential of combining DCNN with XAI to advance lung cancer detection, suggesting future research should expand dataset diversity and explore multimodal data integration for broader applicability and improved diagnostic capabilities.
Co-Authors Abdul Malik Setiawan Asep Rusyana Elfrida Ratnawati Maulana, Aga Mudatsir Mudatsir Müller, Ruth Nur Wahyuniati, Nur Ramadana, Rizki R. Reza Maulana Rinaldi Idroes Samsul Anwar Sirinam, Salin Te, Haypheng Teuku Rizky Noviandy Teuku Zulfikar Utomo, Prattama S. Yufika, Amanda