Artificial Intelligence in Alzheimer's Disease Diagnosis and Prognosis Using PET-MRI: A Narrative Review of High-Impact Literature Post-Tauvid Approval.

Artificial intelligence (AI) is reshaping neuroimaging workflows for Alzheimer's disease (AD) diagnosis, particularly through PET and MRI analysis advances. Since the FDA approval of Tauvid, a PET tracer targeting tau pathology, there has been a notable increase in studies applying AI to neuroimaging data. This narrative review synthesizes recent, high-impact literature to highlight clinically relevant AI applications in AD imaging.

AI-Based Classification of Mild Cognitive Impairment and Cognitively Normal Patients.

: Mild Cognitive Impairment (MCI) represents an intermediate stage between normal cognitive aging and Alzheimer's Disease (AD). Early and accurate identification of MCI is crucial for implementing interventions that may delay or prevent further cognitive decline. This study aims to develop a machine learning-based model for differentiating between Cognitively Normal (CN) individuals and MCI patients using data from the Alzheimer's Disease Neuroimaging Initiative (ADNI).

A Narrative Review of Theranostics in Neuro-Oncology: Advancing Brain Tumor Diagnosis and Treatment Through Nuclear Medicine and Artificial Intelligence.

This narrative review explores the integration of theranostics and artificial intelligence (AI) in neuro-oncology, addressing the urgent need for improved diagnostic and treatment strategies for brain tumors, including gliomas, meningiomas, and pediatric central nervous system neoplasms. A comprehensive literature search was conducted through PubMed, Scopus, and Embase for articles published between January 2020 and May 2025, focusing on recent clinical and preclinical advancements in personalized neuro-oncology. The review synthesizes evidence on novel theranostic agents-such as Lu-177-based radiopharmaceuticals, CXCR4-targeted PET tracers, and multifunctional nanoparticles-and highlights the role of AI in enhancing tumor detection, segmentation, and treatment planning through advanced imaging analysis, radiogenomics, and predictive modeling.

Gastric Emptying Scintigraphy Protocol Optimization Using Machine Learning for the Detection of Delayed Gastric Emptying.

Purpose: The purpose of this study is to examine the feasibility of a machine learning (ML) system for optimizing a gastric emptying scintigraphy (GES) protocol for the detection of delayed gastric emptying (GE), which is considered a primary indication for the diagnosis of gastroparesis.

Methods: An ML model was developed using the JADBio AutoML artificial intelligence (AI) platform. This model employs the percent GE at various imaging time points following the ingestion of a standardized radiolabeled meal to predict normal versus delayed GE at the conclusion of the 4 h GES study.

Leveraging Programmatic Collaboration for a Radiopharmaceutical Clinic.

Radiation oncologists, radiopharmacists, nuclear medicine physicians, and medical oncologists have seen a renewed clinical interest in radiopharmaceuticals for the curative or the palliative treatment of cancer. To allow for the discovery and the clinical advancement of targeted radiopharmaceuticals, these stakeholders have reformed their trial efforts and remodeled their facilities to accommodate the obligations of a program centered upon radioactive investigational drug products. Now considered informally as drugs and not beam radiotherapy, radiopharmaceuticals can be more easily studied in the traditional clinical trial enterprise ranging from phase 0-I to phase III studies.

An Artificial Intelligence System for Optimizing Radioactive Iodine Therapy Dosimetry.

Thyroid cancer, specifically differentiated thyroid carcinoma (DTC), is one of the most prevalent endocrine malignancies worldwide. Radioactive iodine therapy (RAIT) using I-131 has been a standard-of-care approach for DTC due to its ability to ablate remnant thyroid disease following surgery, thus reducing the risk of recurrence. It is also used for the treatment of iodine-avid metastases.

Optimal timing of imaging in gastric emptying scintigraphy for the detection of delayed emptying.

Objective: The purpose of this study was to evaluate a standard 4-h imaging protocol for gastric emptying scintigraphy (GES) in detecting delayed gastric emptying (GE).

Subjects And Methods: Gamma camera imaging was performed in the anterior and posterior views at 0, 0.5, 1, 1.

Analysis of the effects of injecting drug use and HIV-1 infection on 18F-FDG PET brain metabolism.

Unlabelled: Injecting drug use (IDU) is a major risk factor for contracting HIV-1 infection. Both HIV and IDU are neurotoxic, and their coexistence may lead to increased dysfunction of brain metabolic processes. The objective of this research was to investigate the effects of HIV-1 infection and IDU on (18)F-FDG PET brain metabolism.