AIMC Topic: Tomography, X-Ray Computed
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Advanced Three-Dimensional Assessment and Planning for Hallux Valgus.
Foot and ankle clinics
Dec 30, 2024
The article discusses advanced three-dimensional evaluation of hallux valgus deformity using weightbearing computed tomography. Conventional two-dimensional radiographs fall short in assessing the complexity of hallux valgus deformities, whereas weig...
Towards safe and reliable deep learning for lung nodule malignancy estimation using out-of-distribution detection.
Computers in biology and medicine
Dec 30, 2024
Artificial Intelligence (AI) models may fail or suffer from reduced performance when applied to unseen data that differs from the training data distribution, referred to as dataset shift. Automatic detection of out-of-distribution (OOD) data contribu...
Radiomics for differentiating adenocarcinoma and squamous cell carcinoma in non-small cell lung cancer beyond nodule morphology in chest CT.
Scientific reports
Dec 30, 2024
Distinguishing between primary adenocarcinoma (AC) and squamous cell carcinoma (SCC) within non-small cell lung cancer (NSCLC) tumours holds significant management implications. We assessed the performance of radiomics-based models in distinguishing ...
Deep learning on CT scans to predict checkpoint inhibitor treatment outcomes in advanced melanoma.
Scientific reports
Dec 30, 2024
Immune checkpoint inhibitor (ICI) treatment has proven successful for advanced melanoma, but is associated with potentially severe toxicity and high costs. Accurate biomarkers for response are lacking. The present work is the first to investigate the...
Generation of deep learning based virtual non-contrast CT using dual-layer dual-energy CT and its application to planning CT for radiotherapy.
PloS one
Dec 30, 2024
This paper presents a novel approach for generating virtual non-contrast planning computed tomography (VNC-pCT) images from contrast-enhanced planning CT (CE-pCT) scans using a deep learning model. Unlike previous studies, which often lacked sufficie...
Advancing brain tumor detection and classification in Low-Dose CT images using the innovative multi-layered deep neural network model.
Technology and health care : official journal of the European Society for Engineering and Medicine
Dec 29, 2024
BackgroundEffective brain tumour therapy and better patient outcomes depend on early tumour diagnosis. Accurate diagnosis can be hampered by traditional imaging techniques' frequent struggles with low resolution and noise, especially in Low Dose CT s...
Feasibility of AI as first reader in the 4-IN-THE-LUNG-RUN lung cancer screening trial: impact on negative-misclassifications and clinical referral rate.
European journal of cancer (Oxford, England : 1990)
Dec 28, 2024
BACKGROUND: Lung cancer screening (LCS) with low-dose CT (LDCT) reduces lung-cancer-related mortality in high-risk individuals. AI can potentially reduce radiologist workload as first-read-filter by ruling-out negative cases. The feasibility of AI as...
CDUNeXt: efficient ossification segmentation with large kernel and dual cross gate attention.
Scientific reports
Dec 28, 2024
Ossification of the ligamentum flavum (OLF) is the main causative factor of spinal stenosis, but how to accurately and efficiently identify the ossification region is a clinical pain point and an urgent problem to be solved. Currently, we can only re...
Multimodal Deep Learning Fusing Clinical and Radiomics Scores for Prediction of Early-Stage Lung Adenocarcinoma Lymph Node Metastasis.
Academic radiology
Dec 27, 2024
RATIONALE AND OBJECTIVES: To develop and validate a multimodal deep learning (DL) model based on computed tomography (CT) images and clinical knowledge to predict lymph node metastasis (LNM) in early lung adenocarcinoma.
Development and external validation of a multi-task feature fusion network for CTV segmentation in cervical cancer radiotherapy.
Radiotherapy and oncology : journal of the European Society for Therapeutic Radiology and Oncology
Dec 27, 2024
BACKGROUND AND PURPOSE: Accurate segmentation of the clinical target volume (CTV) is essential to deliver an effective radiation dose to tumor tissues in cervical cancer radiotherapy. Also, although automated CTV segmentation can reduce oncologists' ...
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