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AI-Driven Innovations for Early Sepsis Detection by Combining Predictive Accuracy With Blood Count Analysis in an Emergency Setting: Retrospective Study.
J Med Internet Res
January 2025
Division of Clinical Pathology, Department of Pathology, Tri-Service General Hospital, National Defense Medical Center, Taipei, Taiwan.
Background: Sepsis, a critical global health challenge, accounted for approximately 20% of worldwide deaths in 2017. Although the Sequential Organ Failure Assessment (SOFA) score standardizes the diagnosis of organ dysfunction, early sepsis detection remains challenging due to its insidious symptoms. Current diagnostic methods, including clinical assessments and laboratory tests, frequently lack the speed and specificity needed for timely intervention, particularly in vulnerable populations such as older adults, intensive care unit (ICU) patients, and those with compromised immune systems.
View Article and Find Full Text PDFDelta-Radiomics Using Machine Learning Classifiers With Auxiliary Data Sets to Predict Disease Progression During Magnetic Resonance-Guided Radiotherapy in Adrenal Metastases.
JCO Clin Cancer Inform
January 2025
Machine Learning Department, H. Lee Moffit Cancer Center and Research Institute, Tampa, FL.
Purpose: Adaptive radiotherapy accounts for interfractional anatomic changes. We hypothesize that changes in the gross tumor volumes identified during daily scans could be analyzed using delta-radiomics to predict disease progression events. We evaluated whether an auxiliary data set could improve prediction performance.
View Article and Find Full Text PDFRecurrent Thymic Carcinoma Treated With Median Sternotomy, Innominate Vein Replacement for Superior Vena Cava, and Iodide Implantation: A Case Report and Review of the Literature.
Cancer Rep (Hoboken)
January 2025
Department of Thoracic Surgery, Affiliated Hospital of Zunyi Medical College, Zunyi, People's Republic of China.
Background: Neuroendocrine tumors of the thymus (NETT) are rare and malignant tumors that arise in the anterior mediastinum. These tumors can exhibit aggressive behavior and may involve surrounding critical structures, such as the superior vena cava. This case contributes to the literature by presenting a recurrent thymic carcinoma with invasion of major blood vessels, including the superior vena cava, and the complexities involved in its surgical management.
View Article and Find Full Text PDFBreast radiation therapy fluence painting with multi-agent deep reinforcement learning.
Med Phys
January 2025
Department of Radiation Oncology, Duke University, North Carolina, USA.
Background: The electronic compensation (ECOMP) technique for breast radiation therapy provides excellent dose conformity and homogeneity. However, the manual fluence painting process presents a challenge for efficient clinical operation.
Purpose: To facilitate the clinical treatment planning automation of breast radiation therapy, we utilized reinforcement learning (RL) to develop an auto-planning tool that iteratively edits the fluence maps under the guidance of clinically relevant objectives.
A New Deep Learning-Based Method for Automated Identification of Thoracic Lymph Node Stations in Endobronchial Ultrasound (EBUS): A Proof-of-Concept Study.
J Imaging
January 2025
Clinic of Medicine, Nord-Trøndelag Hospital Trust, Levanger Hospital, 7601 Levanger, Norway.
Endobronchial ultrasound-guided transbronchial needle aspiration (EBUS-TBNA) is a cornerstone in minimally invasive thoracic lymph node sampling. In lung cancer staging, precise assessment of lymph node position is crucial for clinical decision-making. This study aimed to demonstrate a new deep learning method to classify thoracic lymph nodes based on their anatomical location using EBUS images.
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