Multidisciplinary Evaluation of an AI-Based Pneumothorax Detection Model: Clinical Comparison with Physicians in Edge and Cloud Environments.
Yazarlar (2)
Ismail Dal
Kastamonu Üniversitesi, Türkiye
Kastamonu Üniversitesi, Türkiye
Dr. Öğr. Üyesi Hasan Burak KAYA Kastamonu Üniversitesi, Türkiye
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Özgün Makale
(SSCI, AHCI, SCI, SCI-Exp dergilerinde yayınlanan tam makale)
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| Dergi Adı | Journal of Multidisciplinary Healthcare (Q2) | ||
| Dergi ISSN | 1178-2390 Dergi Bilgileri (2025) | ||
| Dergi Tarandığı Indeksler | SCI-Expanded | ||
| Makale Dili | İngilizce | Basım Tarihi | 07-2025 |
| Kabul Tarihi | – | Yayınlanma Tarihi | 01-07-2025 |
| Cilt / Sayı / Sayfa | 18 / 1 / 4099–4111 | DOI | 10.2147/JMDH.S535405 |
| Makale Linki | https://doi.org/10.2147/JMDH.S535405 | ||
| UAK Araştırma Alanları |
Acil Tıp
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| Özet |
| BackgroundAccurate and timely detection of pneumothorax on chest radiographs is critical in emergency and critical care settings. While subtle cases remain challenging for clinicians, artificial intelligence (AI) offers promise as a diagnostic aid. This retrospective diagnostic accuracy study evaluates a deep learning model developed using Google Cloud Vertex AI for pneumothorax detection on chest X-rays.MethodsA total of 152 anonymized frontal chest radiographs (76 pneumothorax, 76 normal), confirmed by computed tomography (CT), were collected from a single center between 2023 and 2024. The median patient age was 50 years (range: 18–95), with 67.1% male. The AI model was trained using AutoML Vision and evaluated in both cloud and edge deployment environments. Diagnostic accuracy metrics—including sensitivity, specificity, and F1 score—were compared with those of 15 physicians from four … |
| Anahtar Kelimeler |
| artificial intelligence | clinical decision support systems | cloud computing | multidisciplinary communication | pneumothorax diagnosis |
BM Sürdürülebilir Kalkınma Amaçları
| Atıf Sayıları | |
| Web of Science | 2 |
| Scopus | 3 |
| Google Scholar | 6 |