AIMC Topic: Sensitivity and Specificity

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Accuracy of an nnUNet Neural Network for the Automatic Segmentation of Intracranial Aneurysms, Their Parent Vessels, and Major Cerebral Arteries from MRI-TOF.

AJNR. American journal of neuroradiology May 2, 2025
BACKGROUND AND PURPOSE: The automatic recognition of intracraial aneurysms by means of machine-learning algorithms represents a new frontier for diagnostic and therapeutic goals. Yet, the current algorithms focus solely on the aneurysms and not on th...
Aged Adult Sensitivity and Specificity Male Middle Aged Image Interpretation, Computer-Assisted Magnetic Resonance Angiography Intracranial Aneurysm Cerebral Arteries Neural Networks, Computer Algorithms Retrospective Studies Female Reproducibility of Results Humans
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Artificial intelligence for early detection of lung cancer in GPs' clinical notes: a retrospective observational cohort study.

The British journal of general practice : the journal of the Royal College of General Practitioners May 2, 2025
BACKGROUND: The journey of >80% of patients diagnosed with lung cancer starts in general practice. About 75% of patients are diagnosed when it is at an advanced stage (3 or 4), leading to >80% mortality within 1 year at present. The long-term data in...
Early Detection of Cancer Sensitivity and Specificity Natural Language Processing Humans Netherlands Algorithms Artificial Intelligence Male Retrospective Studies General Practice Machine Learning Middle Aged Electronic Health Records Risk Assessment Female Lung Neoplasms Aged
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Predicting Gene Comutation of EGFR and TP53 by Radiomics and Deep Learning in Patients With Lung Adenocarcinomas.

Journal of thoracic imaging May 1, 2025
PURPOSE: This study was designed to construct progressive binary classification models based on radiomics and deep learning to predict the presence of epidermal growth factor receptor ( EGFR ) and TP53 mutations and to assess the models' capacities t...
Humans Lung Deep Learning Lung Neoplasms Adult Mutation Sensitivity and Specificity Retrospective Studies Tumor Suppressor Protein p53 ErbB Receptors Radiomics Tomography, X-Ray Computed Adenocarcinoma of Lung Male Female Middle Aged Reproducibility of Results Aged
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Detection of Pulmonary Nodules on Ultra-low Dose Chest Computed Tomography With Deep-learning Image Reconstruction Algorithm.

Journal of thoracic imaging May 1, 2025
PURPOSE: To evaluate the accuracy of ultra-low dose (ULD) chest computed tomography (CT), with a radiation exposure equivalent to a 2-view chest x-ray, for pulmonary nodule detection using deep learning image reconstruction (DLIR).
Lung Neoplasms Multiple Pulmonary Nodules Solitary Pulmonary Nodule Radiographic Image Interpretation, Computer-Assisted Radiation Dosage Radiography, Thoracic Algorithms Cross-Sectional Studies Prospective Studies Female Male Tomography, X-Ray Computed Aged Middle Aged Lung Aged, 80 and over Humans Deep Learning Adult Sensitivity and Specificity
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Tumor grade-titude: XGBoost radiomics paves the way for RCC classification.

European journal of radiology Apr 29, 2025
This study aimed to develop and evaluate a non-invasive XGBoost-based machine learning model using radiomic features extracted from pre-treatment CT images to differentiate grade 4 renal cell carcinoma (RCC) from lower-grade tumours. A total of 102 R...
Radiomics Male Middle Aged Contrast Media Neoplasm Grading Reproducibility of Results Aged Aged, 80 and over Adult Sensitivity and Specificity Radiographic Image Interpretation, Computer-Assisted Kidney Neoplasms Carcinoma, Renal Cell Boosting Machine Learning Algorithms Humans Machine Learning Tomography, X-Ray Computed Female
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Clinical Validation of a Noninvasive Multi-Omics Method for Multicancer Early Detection in Retrospective and Prospective Cohorts.

The Journal of molecular diagnostics : JMD Apr 29, 2025
Recent studies highlight the promise of blood-based multicancer early detection (MCED) tests for identifying asymptomatic patients with cancer. However, most focus on a single cancer hallmark, thus limiting effectiveness because of cancer's heterogen...
Algorithms Prospective Studies Retrospective Studies Female Aged Adult Humans Genomics Multiomics Early Detection of Cancer Whole Genome Sequencing Sensitivity and Specificity Biomarkers, Tumor Male Middle Aged Neoplasms
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Detection of pathologic complete response using deep neural network-based endoscopic evaluation in patients with esophageal cancer receiving neoadjuvant chemotherapy: a nationwide multicenter retrospective study from 46 Japanese esophageal centers.

Esophagus : official journal of the Japan Esophageal Society Apr 28, 2025
BACKGROUND: Detecting pathological complete response (pCR) preoperatively facilitated a non-surgical approach after neoadjuvant chemotherapy (NAC). We previously developed a deep neural network-based endoscopic evaluation to determine pCR preoperativ...
Esophagoscopy Pathologic Complete Response Predictive Value of Tests Esophageal Neoplasms Japan Esophageal Squamous Cell Carcinoma Chemotherapy, Adjuvant Neural Networks, Computer Retrospective Studies Female Aged Sensitivity and Specificity Male Middle Aged Humans Deep Learning Treatment Outcome Neoadjuvant Therapy
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Deep learning-assisted detection of meniscus and anterior cruciate ligament combined tears in adult knee magnetic resonance imaging: a crossover study with arthroscopy correlation.

International orthopaedics Apr 28, 2025
AIM: We aimed to compare the diagnostic performance of physicians in the detection of arthroscopically confirmed meniscus and anterior cruciate ligament (ACL) tears on knee magnetic resonance imaging (MRI), with and without assistance from a deep lea...
Humans Female Young Adult Magnetic Resonance Imaging Cross-Over Studies Arthroscopy Tibial Meniscus Injuries Anterior Cruciate Ligament Injuries Anterior Cruciate Ligament Reconstruction Adult Sensitivity and Specificity Male Middle Aged Deep Learning
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A deep learning-based multimodal medical imaging model for breast cancer screening.

Scientific reports Apr 26, 2025
In existing breast cancer prediction research, most models rely solely on a single type of imaging data, which limits their performance. To overcome this limitation, the present study explores breast cancer prediction models based on multimodal medic...
Mammography Breast Neoplasms Humans Multimodal Imaging Middle Aged Female Area Under Curve Deep Learning Aged ROC Curve Adult Early Detection of Cancer Sensitivity and Specificity
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Prediction of significant congenital heart disease in infants and children using continuous wavelet transform and deep convolutional neural network with 12-lead electrocardiogram.

BMC pediatrics Apr 24, 2025
BACKGROUND: Congenital heart disease (CHD) affects approximately 1% of newborns and is a leading cause of mortality in early childhood. Despite the importance of early detection, current screening methods, such as pulse oximetry and auscultation, hav...
Wavelet Analysis Heart Defects, Congenital Humans Neural Networks, Computer Retrospective Studies Infant Female Deep Learning Infant, Newborn Convolutional Neural Networks Sensitivity and Specificity Child, Preschool Male Electrocardiography
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