Automated Detection of Patent Ductus Arteriosus in Pediatric Patients Using Doppler Ultrasonography Videos Based on a Transformer Model [0.03%]
基于变压器模型的婴儿患者肺动脉导管未闭的多普勒超声视频自动化检测方法
Wenjing Hong,Xiaodong Xu,Jiajun Yuan et al.
Wenjing Hong et al.
Patent ductus arteriosus (PDA) is a common congenital heart defect that requires timely and accurate detection to guide clinical management. Although deep learning has shown considerable promise in medical imaging, its application to echoca...
Sümeyye Çelik,Alican Kuran,Kerem Kayabay et al.
Sümeyye Çelik et al.
This study aimed to develop a deep learning-based method for the automatic detection and counting of fungiform papillae (FP) on the dorsal surface of the human tongue. FP density and morphology may serve as biomarkers for taste function and...
Spinal Cord Radiomics-Driven Machine Learning Predicts Meaningful Clinical Improvement After Surgery for Degenerative Cervical Myelopathy: A Pilot Study [0.03%]
脊髓影像组学驱动的机器学习预测退变性颈椎病手术后有意义的临床改善：一项试点研究
Ramesh M Arnest,Kevin M Koch,Matthew D Budde et al.
Ramesh M Arnest et al.
A prospective observational cohort study. To determine whether machine learning models using radiomic features derived from preoperative MRI, clinical variables, or their combination can predict achievement of the minimum clinically importa...
S-MEOD: A Novel Evaluation Metric for Frame-Based Medical Object Detection [0.03%]
基于帧的医学对象检测的新评估指标S-MEOD
Isaac Honarmand Rad,Seyedreza Taghizadeh
Isaac Honarmand Rad
Traditional metrics such as precision, recall, mean Average Precision (mAP), and F-score are widely used to evaluate object detection models. However, in some frame-based medical scenarios, these metrics often fail to capture the true effec...
Evaluating Large Language Models for Turkish Emergency CT Impression Drafting: Quality, Critical Omissions, and Readability [0.03%]
评估大型语言模型在土耳其急诊CT印象草稿中的质量、关键遗漏和可读性评价
Halil Tekdemir,Esra Çıvgın,Şebnem Akpınar et al.
Halil Tekdemir et al.
The purpose of the study is to compare large language models (LLMs) for drafting Turkish emergency CT impression text and to quantify quality, critical omission risk, and readability across anatomical regions. In this retrospective observat...
Performance of an Artificial Intelligence-Based Automated System for Identifying Primary and Permanent Teeth in Mixed Dentition Panoramic Radiographs [0.03%]
基于人工智能的混合牙列全景片中乳牙和恒牙自动识别系统的性能研究
Everton Flaiban,Elaine Dinardi Barioni,Lana Ferreira Santos et al.
Everton Flaiban et al.
This study aimed to assess the diagnostic performance of the Brazilian-developed artificial intelligence system DIO Inteligência® for automatic detection and classification of primary and permanent teeth in panoramic radiographs of patien...
Yasin Shokrollahi,Jose Colmenarez,Wenxi Liu et al.
Yasin Shokrollahi et al.
Artificial intelligence (AI) has catalyzed revolutionary changes across various sectors, notably in healthcare. In particular, generative AI-led by diffusion models and transformer architectures-has enabled significant breakthroughs in medi...
Comparative Clinical Evaluation of "Memory-Efficient" Synthetic 3D Generative Adversarial Networks (GAN) Head-to-Head to State of Art: Results on Computed Tomography of the Chest [0.03%]
"记忆高效型"合成三维生成对抗网络(GAN)的临床比较评估：胸部CT结果与最先进方法对比研究
Mahshid Shiri,Chandra Bortolotto,Alessandro Bruno et al.
Mahshid Shiri et al.
Generative adversarial networks (GANs) are increasingly used to generate synthetic medical images, addressing the critical shortage of annotated data for training artificial intelligence (AI) systems. This study introduces conditional rando...
Feasibility of No-Code Deep Learning for Diagnosing Bone Metastasis in Bone Scans: A Comparative Study of Teachable Machine and ResNet [0.03%]
无代码深度学习诊断骨扫描中骨转移的可行性：Teachable Machine和ResNet的比较研究
Sehyun Pak,Ji Young Woo,Ik Yang et al.
Sehyun Pak et al.
This study explored the feasibility of developing a model that can diagnose positive and negative bone metastasis from bone scan images using Teachable Machine by Google, a no-code AI platform that does not require programming skills or a G...
Vision Transformer-Based Segmentation of Abdominal Subcutaneous and Visceral Fat on MRI [0.03%]
基于视觉变换的腹部皮下和内脏脂肪MRI分割
Sara Hosseinzadeh Kassani,Kavya Patel,Paul K Commean et al.
Sara Hosseinzadeh Kassani et al.
The purpose of this study is to validate a deep learning-based vision transformer for automated quantification and segmentation of abdominal adipose tissue from T1-weighted MRI. This study included abdominal T1 MRI volumes from 107 particip...