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Assessment of using transfer learning with different classifiers in hypodontia diagnosis.

BMC Oral Health

January 2025

Pediatric Dentistry Department, Faculty of Dentistry, Başkent University, 06490, Ankara, Turkey.

Background: Hypodontia is the absence of one or more teeth in the primary or permanent dentition during development, and radiographic imaging is the most common method of diagnosis. However, in recent years, artificial intelligence-based decision support systems have been employed to make highly accurate diagnoses. The aim of this study was to classify single premolar agenesis, multiple premolar agenesis, and without tooth agenesis using various artificial intelligence approaches.

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Smart devices are enabled via the Internet of Things (IoT) and are connected in an uninterrupted world. These connected devices pose a challenge to cybersecurity systems due attacks in network communications. Such attacks have continued to threaten the operation of systems and end-users.

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Aim: This study aimed to identify the heterogeneous trajectories of frailty and determine the predictors of distinct trajectories in patients with heart failure.

Design: A longitudinal study.

Methods: A total of 253 patients with heart failure were recruited at the cardiology department of a tertiary hospital between February and December 2023.

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PET-CT-based host metabolic (PETMet) features are associated with pathologic response in gastroesophageal adenocarcinoma.

Eur J Surg Oncol

January 2025

Division of Surgical Oncology, Department of Surgery, Northwell Health, New Hyde Park, NY, USA; Gastric and Mixed Tumor Service, Department of Surgery, Memorial Sloan Kettering Cancer Center, New York, NY, USA. Electronic address:

Background: F-FDG PET-CT-based host metabolic (PETMet) profiling of non-tumor tissue is a novel approach to incorporate the patient-specific response to cancer into clinical algorithms.

Materials And Methods: A prospectively maintained institutional database of gastroesophageal cancer patients was queried for pretreatment PET-CTs, demographics, and clinicopathologic variables. F-FDG PET avidity was measured in 9 non-tumor tissue types (liver, spleen, 4 muscles, 3 fat locations).

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Machine learning prediction model for oral mucositis risk in head and neck radiotherapy: a preliminary study.

Support Care Cancer

January 2025

Oral Diagnosis Department, Faculdade de Odontolodia de Piracicaba, Universidade de Campinas (UNICAMP), Piracicaba, São Paulo, Brazil.

Purpose: Oral mucositis (OM) reflects a complex interplay of several risk factors. Machine learning (ML) is a promising frontier in science, capable of processing dense information. This study aims to assess the performance of ML in predicting OM risk in patients undergoing head and neck radiotherapy.

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