AIMC Topic: Aged

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Deep Learning Segmentation of Infiltrative and Enhancing Cellular Tumor at Pre- and Posttreatment Multishell Diffusion MRI of Glioblastoma.

Radiology. Artificial intelligence
Purpose To develop and validate a deep learning (DL) method to detect and segment enhancing and nonenhancing cellular tumor on pre- and posttreatment MRI scans in patients with glioblastoma and to predict overall survival (OS) and progression-free su...

Predicting lymph node recurrence in cT1-2N0 tongue squamous cell carcinoma: collaboration between artificial intelligence and pathologists.

The journal of pathology. Clinical research
Researchers have attempted to identify the factors involved in lymph node recurrence in cT1-2N0 tongue squamous cell carcinoma (SCC). However, studies combining histopathological and clinicopathological information in prediction models are limited. W...

Artificial Intelligence for Diagnosis in Otologic Patients: Is It Ready to Be Your Doctor?

Otology & neurotology : official publication of the American Otological Society, American Neurotology Society [and] European Academy of Otology and Neurotology
OBJECTIVE: Investigate the precision of language-model artificial intelligence (AI) in diagnosing conditions by contrasting its predictions with diagnoses made by board-certified otologic/neurotologic surgeons using patient-described symptoms.

External Testing of a Deep Learning Model to Estimate Biologic Age Using Chest Radiographs.

Radiology. Artificial intelligence
Purpose To assess the prognostic value of a deep learning-based chest radiographic age (hereafter, CXR-Age) model in a large external test cohort of Asian individuals. Materials and Methods This single-center, retrospective study included chest radio...

Open Access Data and Deep Learning for Cardiac Device Identification on Standard DICOM and Smartphone-based Chest Radiographs.

Radiology. Artificial intelligence
Purpose To develop and evaluate a publicly available deep learning model for segmenting and classifying cardiac implantable electronic devices (CIEDs) on Digital Imaging and Communications in Medicine (DICOM) and smartphone-based chest radiographs. M...

Deep Learning to Detect Intracranial Hemorrhage in a National Teleradiology Program and the Impact on Interpretation Time.

Radiology. Artificial intelligence
The diagnostic performance of an artificial intelligence (AI) clinical decision support solution for acute intracranial hemorrhage (ICH) detection was assessed in a large teleradiology practice. The impact on radiologist read times and system efficie...

Enhancing Care for Older Adults and Dementia Patients With Large Language Models: Proceedings of the National Institute on Aging-Artificial Intelligence & Technology Collaboratory for Aging Research Symposium.

The journals of gerontology. Series A, Biological sciences and medical sciences
Large Language Models (LLMs) stand on the brink of reshaping the field of aging and dementia care, challenging the one-size-fits-all paradigm with their capacity for precision medicine and individualized treatment strategies. The "Large Pre-Trained M...

Development and validation of machine learning models for predicting HER2-zero and HER2-low breast cancers.

The British journal of radiology
OBJECTIVES: To develop and validate machine learning models for human epidermal growth factor receptor 2 (HER2)-zero and HER2-low using MRI features pre-neoadjuvant therapy (NAT).