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All Journal Berkala Ilmu Kesehatan Kulit dan Kelamin Narra J Journal of Medical Science; Jurnal Ilmu Medis Rumah Sakit Umum Daerah dr. Zainoel Abidin, Banda Aceh Media Dermato-Venereologica Indonesiana Journal of General Procedural Dermatology and Venereology Indonesia Heca Journal of Applied Sciences Malacca Pharmaceutics Indonesian Journal of Case Reports Journal of Innovative and Creativity
Earlia, Nanda
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Agriculture, Biological Sciences & Forestry Biochemistry, Genetics & Molecular Biology Chemistry Dentistry Earth & Planetary Sciences Education Engineering Health Professions Immunology & microbiology Languange, Linguistic, Communication & Media Mathematics Medicine & Pharmacology Neuroscience Nursing Public Health Social Sciences Other

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Journal : Narra J

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A fatal case of Harlequin ichthyosis: Experience from low-resource setting Vella, Vella; Maulida, Mimi; Earlia, Nanda; Hidayati, Arie; Handriani, Risna; Gondokaryono, Srie P.; Dwiyana, Reiva F.; Doris , Ezigbo E.; Pradistha , Aldilla; Bulqiah, Mikyal
Narra J Vol. 3 No. 3 (2023): December 2023
Publisher : Narra Sains Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52225/narra.v3i3.302

Abstract

Harlequin ichthyosis is a severe and fatal presentation of ichthyosis with an autosomal recessive inheritance. Infants with Harlequin ichthyosis have a high mortality rate, and a dismal prognosis; therefore the majority of neonates die shortly after birth from infection, heat loss, dehydration, electrolytic imbalances, or respiratory distress. The aim of this case report was to present a fatal case of Harlequin ichthyosis with no family history of any inherited skin disorder. A 3-day-old baby was presented to the emergency room with congenital abnormalities at birth, fissured hyperkeratotic skin, and thick yellow plates of scales. The parents had no history of consanguineous marriage, no relevant past medical history, and no family history of the same condition. The patient was unwell, pulse 162 times/minute, respiratory rate 48 times/minute, and axillary temperature 36.9oC. APGAR score was 8 in the 1st minute and 9 in the 5th minute. Based on the typical clinical appearance, the patient was diagnosed with Harlequin ichthyosis. Due to a lack of facility, a mutation analysis was not carried out. The patient was then transferred to the neonatal intensive care unit (NICU) and treated in a humidified incubator and medicated with intravenous antibiotics (ampicillin sulbactam 125 mg/12 hour and gentamicin 13 mg/24 hour), topically fusidic acid and mild emollients. A central venous catheter was used for intravenous access. The poor prognosis resulted in the patient dying at the age of 5-day-old. This case highlights that prenatal diagnosis is critical for early detection and disease prevention. Mutation screening for the ABCA12 gene is suggested for consanguinity marriages and with a history of ichthyosis.
Evaluation of atopic dermatitis severity using artificial intelligence Maulana, Aga; Noviandy, Teuku R.; Suhendra, Rivansyah; Earlia, Nanda; Bulqiah, Mikyal; Idroes, Ghazi M.; Niode, Nurdjannah J.; Sofyan, Hizir; Subianto, Muhammad; Idroes, Rinaldi
Narra J Vol. 3 No. 3 (2023): December 2023
Publisher : Narra Sains Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52225/narra.v3i3.511

Abstract

Atopic dermatitis is a prevalent and persistent chronic inflammatory skin disorder that poses significant challenges when it comes to accurately assessing its severity. The aim of this study was to evaluate deep learning models for automated atopic dermatitis severity scoring using a dataset of Aceh ethnicity individuals in Indonesia. The dataset of clinical images was collected from 250 patients at Dr. Zainoel Abidin Hospital, Banda Aceh, Indonesia and labeled by dermatologists as mild, moderate, severe, or none. Five pre-trained convolutional neural networks (CNN) architectures were evaluated: ResNet50, VGGNet19, MobileNetV3, MnasNet, and EfficientNetB0. The evaluation metrics, including accuracy, precision, sensitivity, specificity, and F1-score, were employed to assess the models. Among the models, ResNet50 emerged as the most proficient, demonstrating an accuracy of 89.8%, precision of 90.00%, sensitivity of 89.80%, specificity of 96.60%, and an F1-score of 89.85%. These results highlight the potential of incorporating advanced, data-driven models into the field of dermatology. These models can serve as invaluable tools to assist dermatologists in making early and precise assessments of atopic dermatitis severity and therefore improve patient care and outcomes.
Psoriasis severity assessment: Optimizing diagnostic models with deep learning Maulana, Aga; Noviandy, Teuku R.; Suhendra, Rivansyah; Earlia, Nanda; Prakoeswa, Cita RS.; Kairupan, Tara S.; Idroes, Ghifari M.; Subianto, Muhammad; Idroes, Rinaldi
Narra J Vol. 4 No. 3 (2024): December 2024
Publisher : Narra Sains Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52225/narra.v4i3.1512

Abstract

Psoriasis is a chronic skin condition with challenges in the accurate assessment of its severity due to subtle differences between severity levels. The aim of this study was to evaluate deep learning models for automated classification of psoriasis severity. A dataset containing 1,546 clinical images was subjected to pre-processing techniques, including cropping and applying noise reduction through median filtering. The dataset was categorized into four severity classes: none, mild, moderate, and severe, based on the Psoriasis Area and Severity Index (PASI). It was split into 1,082 images for training (70%) and 463 images for validation and testing (30%). Five modified deep convolutional neural networks (DCNN) were evaluated, including ResNet50, VGGNet19, MobileNetV3, MnasNet, and EfficientNetB0. The data were validated based on accuracy, precision, sensitivity, specificity, and F1-score, which were weighted to reflect class representation; Pairwise McNemar's test, Cochran's Q test, Cohen’s Kappa, and Post-hoc test were performed on the model performance, where overall accuracy and balanced accuracy were determined. Findings revealed that among the five deep learning models, ResNet50 emerged as the optimum model with an accuracy of 92.50% (95%CI: 91.2–93.8%). The precision, sensitivity, specificity, and F1-score of this model were found to be 93.10%, 92.50%, 97.37%, and 92.68%, respectively. In conclusion, ResNet50 has the potential to provide consistent and objective assessments of psoriasis severity, which could aid dermatologists in timely diagnoses and treatment planning. Further clinical validation and model refinement remain required.
Co-Authors . Zulfan Abdurrazaq Abdurrazaq, Abdurrazaq Agustyananda, Dedi Alghani, Fajri Amelia Sari Asnawi, Vella asrizal, cynthia wahyu Athira, Athira Azzahra, Shelda Syifa Badiri, Istanul Bulqiah, Mikyala Dinillah, Teuku Muhammad Muizzy Doris , Ezigbo E. Dwiyana, Reiva F. Elfa Wirdani Fitri Endang Lukitaningsih Farsa, Bianda Sabrina Fitria Fitria Gondokaryono, Srie P. Handriani, Risna Hanum, Faridah Hayati, Rima hidayati, Arie Hizir Sofyan Husdayanti, Noviana Ibrahim, Teuku Maulana Malik Idroes, Ghazi M. Idroes, Ghifari M. Ikram, Tubagus Pasca Faiz Ismida, Fitri Dewi Kairupan, Tara S. Liana, Mahda Rizki Maghfirah, Karamina Maulana, Aga Maulida, Mimi Maulidina, Rizka Yuliani Menul Ayu Umborowati, Menul Ayu Mikyal Bulqiah, Mikyal Muhammad Subianto Murtadha, Raihan Nabilah, Nada Niode, Nurdjannah Jane Noviandy, Teuku R. NUR FAJRINA Nurdjannah J. Niode Pradistha , Aldilla Pradistha, Aldilla Prakoeswa, Cita RS. Rahmatillah, Muhammad Zahrul Ramadhani, Cut Sukma Razaq, Abdur Reno Keumalazia Kamarlis Rinaldi Idroes Salim, Fitria Sari, Ninda Sasmita, Novi Reandy SITTI HAJAR Suhendra, Rivansyah Sulamsih Sri Budini, Sulamsih Sri Suryadi Suryadi Tasrif, Aqil Yuniawan Teuku Rizky Noviandy Vella Vella, Vella Vemulen, Dara Avinda Wahyu Lestari Wisesa, Wizurai Yoo, Soo-Ji Zakaria, Sri Rezeki Zuhra, Sofi Alfia