Türk Yoğun Bakım Derneği, 2. Uluslararası Sepsis Sempozyumu, 12 Eylül 2026, İstanbul
https://yogunbakim.org.tr/sepsis2026/index.html#tb__home
Saturday, July 18, 2026
Friday, June 26, 2026
Automated estimation of drain output in postoperative patients using deep learning on clinical images
Sci Rep. 2026 Apr 24;16(1):18923. doi: 10.1038/s41598-026-49452-9.
Mehmet Eren Yuksel, Ali Çelikkaya, Seniha Esen Erdem, Melih Akıncı
Abstract: Postoperative drains are essential components of care in general surgery and intensive care units, where accurate monitoring of drain output is critical for detecting complications such as hemorrhage, anastomotic leakage, or infection. Despite its importance, output measurement is still performed manually, which is time-consuming, exposes staff to biohazardous fluids, and is prone to documentation errors. In this study, we present a deep learning based automated system for estimating postoperative drain output from clinical images of Jackson-Pratt drains and drainage bags collected under real hospital conditions. The dataset includes a wide range of effluents, such as blood, gastric content, and serous fluid, representing the visual variability encountered in daily clinical practice. The proposed pipeline combines object detection and semantic segmentation to localize drains and mark out fluid boundaries, enabling precise and contact-free volume estimation of the fluids inside the drainage bags. Our system achieved high segmentation accuracy, with Intersection over Union (IoU) scores of approximately 0.99 and clinically acceptable mean absolute errors (5.1 mL for Jackson-Pratt drains and 43.6 mL for drainage bags). Robustness analyses demonstrated consistent performance across varying lighting conditions and viewing angles commonly encountered at the bedside. This work introduces a novel approach for automated drain output measurement validated on real patient data. By reducing staff workload and minimizing exposure to potentially infectious fluids, our system has the potential to improve both patient safety and occupational safety in surgical and intensive care unit (ICU) workflows, and also supports early detection of postoperative complications in surgical and intensive care settings. Our data and code are available at https://zenodo.org/records/17599824 .
Saturday, June 20, 2026
Postoperative Care and AI Monitoring
Book: Beyond The Knife, AI's Role in Surgical Evolution
Ala Elcircevi, Ahmet Serdar Karaca
Chapter 4: Postoperative Care and AI Monitoring
Mehmet Eren Yüksel
ISBN: 978-625-5746-87-0
Saturday, May 09, 2026
2. Ulusal Nuh Naci Yazgan Sağlık Bilimleri Kongresi 29-30 Nisan 2026
https://www.nnysaglikkongresi.org/
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3. Albümin ESPEN 2025’te Beslenmenin Göstergesi Değildir, Prognostik Bir Belirteçtir
4. Sirozda Gece Atıştırmaları Kas Yıkımını ve Hepatik
Ensefalopatiyi Azaltır
5. Pilonidal Hastalıkta Fenol Tedavisine Alternatif Olarak Gümüş Nitrat: Yasal Kısıtlamaların Olduğu Ülkeler İçin Pratik Bir Yaklaşım
Saturday, March 07, 2026
Tuesday, March 03, 2026
2. Ulusal Genel Dahiliye Kongresi 11-14 Haziran 2026
1. Kardiyak Arrest Sonrası Tanısal Yaklaşımda Değişim: ST Elevasyonu Olmayan Hastanızın Tüm Vücut Bilgisayarlı Tomografisini Çektirdiniz Mi?
Cantürk Kaya, Mehmet Eren Yüksel
2. Karaciğer Transplantasyonu İçin Hastaların Transplantasyon Merkezlerine Zamanında Sevk Edilmesi: 2025 AASLD–AST Kılavuzundaki İlkelerin Öğretilmesi
Cantürk Kaya, Mehmet Eren Yüksel
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