Artificial Intelligence for Neuroimaging in Pediatric Cancer

Cancers, Год журнала: 2025, Номер 17(4), С. 622 - 622

Опубликована: Фев. 12, 2025

Background/Objectives: Artificial intelligence (AI) is transforming neuroimaging by enhancing diagnostic precision and treatment planning. However, its applications in pediatric cancer remain limited. This review assesses the current state, potential applications, challenges of AI for cancer, emphasizing unique needs population. Methods: A comprehensive literature was conducted, focusing on AI’s impact through accelerated image acquisition, reduced radiation, improved tumor detection. Key methods include convolutional neural networks segmentation, radiomics characterization, several tools functional imaging. Challenges such as limited datasets, developmental variability, ethical concerns, need explainable models were analyzed. Results: has shown significant to improve imaging quality, reduce scan times, enhance accuracy neuroimaging, resulting segmentation outcome prediction treatment. progress hindered scarcity issues with data sharing, implications applying vulnerable populations. Conclusions: To overcome limitations, future research should focus building robust fostering multi-institutional collaborations developing interpretable that align clinical practice standards. These efforts are essential harnessing full improving outcomes children cancer.

Язык: Английский

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Predicting Surgical Difficulty in Rectal Cancer Surgery: A Systematic Review of Artificial Intelligence Models Applied to Pre-Operative MRI

Cancers, Год журнала: 2025, Номер 17(5), С. 812 - 812

Опубликована: Фев. 26, 2025

Introduction: Following the rapid advances in minimally invasive surgery, there are a multitude of surgical modalities available for resecting rectal cancers. Robotic resections represent current pinnacle approaches. Currently, decisions on modality depend local resources and expertise team. Given limited access to robotic developing tools based pre-operative data that can predict difficulty surgery would streamline efficient utilisation resources. This systematic review aims appraise existing literature artificial intelligence (AI)-driven preoperative MRI analysis prediction identify knowledge gaps promising models warranting further clinical evaluation. Methods: A narrative synthesis were undertaken accordance with PRISMA SWiM guidelines. Systematic searches performed Medline, Embase, CENTRAL Trials register. Studies published between 2012 2024 included where AI was applied imaging adult cancer patients undergoing surgeries, any approach, purpose stratifying difficulty. Data extracted according pre-specified protocol capture study characteristics design; objectives performance outcome metrics summarised. Results: database returned 568 articles, 40 ultimately this review. support assessments identified across eight domains (direct grading, extramural vascular invasion (EMVI), lymph node metastasis (LNM), lymphovascular (LVI), perineural (PNI), T staging, requirement multiple linear stapler firings. For each, at least one model very good (AUC scores >0.80), several showing excellent considerably above threshold. Conclusions: assessment surgeries emerging, progressing development strong many models. These warrant evaluation, which aid personalised approaches ensure adequate

Язык: Английский

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Deep Learning for Ultrasonographic Assessment of Temporomandibular Joint Morphology

Tomography, Год журнала: 2025, Номер 11(3), С. 27 - 27

Опубликована: Фев. 27, 2025

Background: Temporomandibular joint (TMJ) disorders are a significant cause of orofacial pain. Artificial intelligence (AI) has been successfully applied to other imaging modalities but remains underexplored in ultrasonographic evaluations TMJ. Objective: This study aimed develop and validate an AI-driven method for the automatic reproducible measurement TMJ space width from images. Methods: A total 142 images were segmented into three anatomical components: mandibular condyle, space, glenoid fossa. State-of-the-art architectures tested, best-performing 2D Residual U-Net was trained validated against expert annotations. The algorithm based on segmentation proposed, calculating vertical distance between superior-most point condyle its corresponding Results: model achieved high performance (Dice: 0.91 ± 0.08) 0.86 0.09), with notably lower fossa 0.60 0.24), highlighting variability due complex geometry. demonstrated minimal bias, mean difference 0.08 mm absolute error 0.18 compared reference measurements. Conclusions: exhibited potential as reliable tool clinical use, demonstrating accuracy analysis. underscores ability algorithms bridge existing gaps lays foundation broader applications.

Язык: Английский

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GM-CBAM-ResNet: A Lightweight Deep Learning Network for Diagnosis of COVID-19

Journal of Imaging, Год журнала: 2025, Номер 11(3), С. 76 - 76

Опубликована: Март 3, 2025

COVID-19 can cause acute infectious diseases of the respiratory system, and may probably lead to heart damage, which will seriously threaten human health. Electrocardiograms (ECGs) have advantages being low cost, non-invasive, radiation free, is widely used for evaluating health status. In this work, a lightweight deep learning network named GM-CBAM-ResNet proposed diagnosing based on ECG images. constructed by replacing convolution module with Ghost (GM) adding convolutional block attention (CBAM) in residual ResNet. To reveal superiority GM-CBAM-ResNet, other three methods (ResNet, GM-ResNet, CBAM-ResNet) are also analyzed from following aspects: model performance, complexity, interpretability. The performance evaluated using open ‘ECG Images dataset Cardiac Patients’. complexity reflected comparing number parameters. interpretability utilizing Gradient-weighted Class Activation Mapping (Grad-CAM). Parameter statistics indicate that, basis ResNet19, parameters GM-CBAM-ResNet19 reduced 45.4%. Experimental results show under less improves diagnostic accuracy approximately 5% comparison ResNet19. Additionally, analysis shows that CBAM suppress interference grid backgrounds ensure higher lower complexity. This work provides solution rapid accurate COVD-19 images, holds significant practical deployment value.

Язык: Английский

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The rapid evolution of AI in patient education—promise, peril, and the path forward

Baylor University Medical Center Proceedings, Год журнала: 2025, Номер unknown, С. 1 - 2

Опубликована: Март 10, 2025

Язык: Английский

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