AIMC Topic: Tomography, X-Ray Computed
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The role of deep learning in diagnostic imaging of spondyloarthropathies: a systematic review.
European radiology
Dec 10, 2024
AIM: Diagnostic imaging is an integral part of identifying spondyloarthropathies (SpA), yet the interpretation of these images can be challenging. This review evaluated the use of deep learning models to enhance the diagnostic accuracy of SpA imaging...
Spectrum of errors in nodule detection and characterization using machine learning: A pictorial essay.
Current problems in diagnostic radiology
Dec 10, 2024
In academic and research settings, computer-aided nodule detection software has been shown to increase accuracy, efficiency, and throughput. However, radiologists need to be familiar with the spectrum of errors that can occur when these algorithms ar...
Machine Learning-Based Prediction for In-Hospital Mortality After Acute Intracerebral Hemorrhage Using Real-World Clinical and Image Data.
Journal of the American Heart Association
Dec 10, 2024
BACKGROUND: Machine learning (ML) techniques are widely employed across various domains to achieve accurate predictions. This study assessed the effectiveness of ML in predicting early mortality risk among patients with acute intracerebral hemorrhage...
Evaluation of the clinical utility of lateral cephalometry reconstructed from computed tomography extracted by artificial intelligence.
Journal of cranio-maxillo-facial surgery : official publication of the European Association for Cranio-Maxillo-Facial Surgery
Dec 9, 2024
This study assessed the accuracy and reliability of artificial intelligence (AI)-reconstructed images of two-dimensional (2D) lateral cephalometric analyses of facial computed tomography (CT) images, which is widely used for the diagnosis of craniofa...
Comparative study of DCNN and image processing based classification of chest X-rays for identification of COVID-19 patients using fine-tuning.
Journal of medical engineering & technology
Dec 9, 2024
The conventional detection of COVID-19 by evaluating the CT scan images is tiresome, often experiences high inter-observer variability and uncertainty issues. This work proposes the automatic detection and classification of COVID-19 by analysing the ...
Radiomics-based machine learning for automated detection of Pneumothorax in CT scans.
PloS one
Dec 9, 2024
The increasing complexity of diagnostic imaging often leads to misinterpretations and diagnostic errors, particularly in critical conditions such as pneumothorax. This study addresses the pressing need for improved diagnostic accuracy in CT scans by ...
[Application of artificial intelligence-based organ contouring software and comparison of results in treatment planning for radiotherapy].
Orvosi hetilap
Dec 8, 2024
Comparing fully automated AI body composition biomarkers at differing virtual monoenergetic levels using dual-energy CT.
Abdominal radiology (New York)
Dec 7, 2024
PURPOSE: To investigate the behavior of artificial intelligence (AI) CT-based body composition biomarkers at different virtual monoenergetic imaging (VMI) levels using dual-energy CT (DECT).
Radiomics for differential diagnosis of Bosniak II-IV renal masses via CT imaging.
BMC cancer
Dec 6, 2024
RATIONALE AND OBJECTIVES: The management of complex renal cysts is guided by the Bosniak classification system, which may be inadequate for risk stratification of patients to determine the appropriate intervention. Radiomics models based on CT imagin...
Self-supervised learning improves robustness of deep learning lung tumor segmentation models to CT imaging differences.
Medical physics
Dec 5, 2024
BACKGROUND: Self-supervised learning (SSL) is an approach to extract useful feature representations from unlabeled data, and enable fine-tuning on downstream tasks with limited labeled examples. Self-pretraining is a SSL approach that uses curated do...
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