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| http://dx.doi.org/10.1016/j.vgie.2024.08.007 | DOI Listing |
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Why implementing machine learning algorithms in the clinic is not a plug-and-play solution: a simulation study of a machine learning algorithm for acute leukaemia subtype diagnosis.
EBioMedicine
December 2024
Department of Haematology & Stem Cell Transplantation, West German Cancer Center, University Hospital Essen, Essen, Germany; Laboratory for Clinical Research and Real-World Evidence, Institute for Artificial Intelligence in Medicine, University Hospital Essen, Essen, Germany. Electronic address:
Article Synopsis
- The study evaluated the performance of the AI-PAL machine learning algorithm for diagnosing acute leukaemia when implemented in a real-world clinical setting at the University Hospital Essen.
- Results showed that the AI-PAL algorithm performed significantly worse in the clinical simulation compared to previous results, with key performance metrics falling below the expected levels.
- The findings highlight the necessity for local validation and potential recalibration of machine learning models before their use in clinical settings to ensure they are reliable and safe for patient care.
Raman Spectral Feature Enhancement Framework for Complex Multiclassification Tasks.
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State Key Laboratory of Optical Fiber and Cable Manufacture Technology, Guangdong Key Laboratory of Integrated Optoelectronics Intellisense, Department of EEE, Southern University of Science and Technology, Shenzhen 518055, China.
Raman spectroscopy enables label-free clinical diagnosis in a single step. However, identifying an individual carrying a specific disease from people with a multi-disease background is challenging. To address this, we developed a Raman spectral implicit feature augmentation with a Raman Intersection, Union, and Subtraction augmentation strategy (RIUS).
View Article and Find Full Text PDFNot Another Dual Attention UNet Transformer (NNDA-UNETR): a plug-and-play parallel dual attention block in U-Net with enhanced residual blocks for medical image segmentation.
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Tiktok Inc., San Jose, CA, USA.
Background: Medical image segmentation is crucial for clinical diagnostics and treatment planning. Recently, hybrid models often neglect the local modeling capabilities of Transformers for medical image segmentation, despite the complementary nature of local information from both convolutional neural networks (CNNs) and transformers. This limitation is particularly problematic in multi-organ segmentation, where organs are closely adhered, and accurate delineation is essential.
View Article and Find Full Text PDFPlug and play: vascular plug therapy for refractory colocutaneous fistula.
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Division of Gastroenterology, Department of Medicine, UMass Chan Medical School, Worcester, Massachusetts, USA.
Re-Parameterization After Pruning: Lightweight Algorithm Based on UAV Remote Sensing Target Detection.
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December 2024
School of Automation & Information Engineering, Sichuan University of Science & Engineering, Yibin 644000, China.
Lightweight object detection algorithms play a paramount role in unmanned aerial vehicles (UAVs) remote sensing. However, UAV remote sensing requires target detection algorithms to have higher inference speeds and greater accuracy in detection. At present, most lightweight object detection algorithms have achieved fast inference speed, but their detection precision is not satisfactory.
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