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| http://dx.doi.org/10.1148/ryct.220297 | DOI Listing |
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Artificial intelligence-based biomarkers for treatment decisions in oncology.
Trends Cancer
January 2025
Else Kroener Fresenius Center for Digital Health, Medical Faculty Carl Gustav Carus, Dresden University of Technology (TUD), Dresden, Germany; Department of Medicine I, University Hospital Dresden, Dresden, Germany; Medical Oncology, National Center for Tumor Diseases (NCT), University Hospital Heidelberg, Heidelberg, Germany. Electronic address:
The development of new therapeutic strategies such as immune checkpoint inhibitors (ICIs) and targeted therapies has increased the complexity of the treatment landscape for solid tumors. At the current rate of annual FDA approvals, the potential treatment options could increase by tenfold over the next 5 years. The cost of personalized medicine technologies limits its accessibility, thus increasing socioeconomic disparities in the treated population.
View Article and Find Full Text PDFExploring Dance as a Therapeutic Approach for Parkinson Disease Through the Social Robotics for Active and Healthy Ageing (SI-Robotics): Results From a Technical Feasibility Study.
JMIR Aging
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Scientific Direction, IRCCS INRCA, Via Santa Margherita 5, Ancona, 60124, Italy, 39 0718004767.
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View Article and Find Full Text PDFAssessment of using transfer learning with different classifiers in hypodontia diagnosis.
BMC Oral Health
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Pediatric Dentistry Department, Faculty of Dentistry, Başkent University, 06490, Ankara, Turkey.
Background: Hypodontia is the absence of one or more teeth in the primary or permanent dentition during development, and radiographic imaging is the most common method of diagnosis. However, in recent years, artificial intelligence-based decision support systems have been employed to make highly accurate diagnoses. The aim of this study was to classify single premolar agenesis, multiple premolar agenesis, and without tooth agenesis using various artificial intelligence approaches.
View Article and Find Full Text PDFPhantom evaluation of feasibility and applicability of artificial intelligence based pulmonary nodule detection in chest radiographs.
Medicine (Baltimore)
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Department of Radiology, Kantonsspital Baden, affiliated Hospital for Research and Teaching of the Faculty of Medicine of the University of Zurich, Baden, Switzerland.
The aim of our study was to evaluate the specific performance of an artificial intelligence (AI) algorithm for lung nodule detection in chest radiography for a larger number of nodules of different sizes and densities using a standardized phantom approach. A total of 450 nodules with varying density (d1 to d3) and size (3, 5, 8, 10 and 12 mm) were inserted in a Lungman phantom at various locations. Radiographic images with varying projections were acquired and processed using the AI algorithm for nodule detection.
View Article and Find Full Text PDFA deep learning modular ECG approach for cardiologist assisted adjudication of atrial fibrillation and atrial flutter episodes.
Heart Rhythm O2
December 2024
Cardiology Department, Bichat Hospital, Paris, France.
Background: Detection of atrial tachyarrhythmias (ATA) on long-term electrocardiogram (ECG) recordings is a prerequisite to reduce ATA-related adverse events. However, the burden of editing massive ECG data is not sustainable. Deep learning (DL) algorithms provide improved performances on resting ECG databases.
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