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Neck

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Feasibility evaluation of novel AI-based deep-learning contouring algorithm for radiotherapy.

Journal of applied clinical medical physics
PURPOSE: To evaluate the clinical feasibility of the Siemens Healthineers AI-Rad Companion Organs RT VA30A (Organs-RT) auto-contouring algorithm for organs at risk (OARs) of the pelvis, thorax, and head and neck (H&N).

Shortening image registration time using a deep neural network for patient positional verification in radiotherapy.

Physical and engineering sciences in medicine
We sought to accelerate 2D/3D image registration computation time using image synthesis with a deep neural network (DNN) to generate digitally reconstructed radiographic (DRR) images from X-ray flat panel detector (FPD) images. And we explored the fe...

Can deep learning replace histopathological examinations in the differential diagnosis of cervical lymphadenopathy?

European archives of oto-rhino-laryngology : official journal of the European Federation of Oto-Rhino-Laryngological Societies (EUFOS) : affiliated with the German Society for Oto-Rhino-Laryngology - Head and Neck Surgery
INTRODUCTION: We aimed to develop a diagnostic deep learning model using contrast-enhanced CT images and to investigate whether cervical lymphadenopathies can be diagnosed with these deep learning methods without radiologist interpretations and histo...

Current State of Artificial Intelligence in Clinical Applications for Head and Neck MR Imaging.

Magnetic resonance in medical sciences : MRMS : an official journal of Japan Society of Magnetic Resonance in Medicine
Due primarily to the excellent soft tissue contrast depictions provided by MRI, the widespread application of head and neck MRI in clinical practice serves to assess various diseases. Artificial intelligence (AI)-based methodologies, particularly dee...

Contour subregion error detection methodology using deep learning auto-segmentation.

Medical physics
BACKGROUND: Inaccurate manual organ delineation is one of the high-risk failure modes in radiation treatment. Numerous automated contour quality assurance (QA) systems have been developed to assess contour acceptability; however, manual inspection of...

Assessing the accuracy of ChatGPT references in head and neck and ENT disciplines.

European archives of oto-rhino-laryngology : official journal of the European Federation of Oto-Rhino-Laryngological Societies (EUFOS) : affiliated with the German Society for Oto-Rhino-Laryngology - Head and Neck Surgery
PURPOSE: ChatGPT has gained popularity as a web application since its release in 2022. While artificial intelligence (AI) systems' potential in scientific writing is widely discussed, their reliability in reviewing literature and providing accurate r...

Diagnosis with confidence: deep learning for reliable classification of laryngeal dysplasia.

Histopathology
BACKGROUND: Diagnosis of head and neck (HN) squamous dysplasias and carcinomas is critical for patient care, cure, and follow-up. It can be challenging, especially for grading intraepithelial lesions. Despite recent simplification in the last WHO gra...

A statistical deformation model-based data augmentation method for volumetric medical image segmentation.

Medical image analysis
The accurate delineation of organs-at-risk (OARs) is a crucial step in treatment planning during radiotherapy, as it minimizes the potential adverse effects of radiation on surrounding healthy organs. However, manual contouring of OARs in computed to...

Framework for Radiation Oncology Department-wide Evaluation and Implementation of Commercial Artificial Intelligence Autocontouring.

Practical radiation oncology
PURPOSE: Artificial intelligence (AI)-based autocontouring in radiation oncology has potential benefits such as standardization and time savings. However, commercial AI solutions require careful evaluation before clinical integration. We developed a ...