AIMC Topic: Neck

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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...

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).

Delineation of clinical target volume and organs at risk in cervical cancer radiotherapy by deep learning networks.

Medical physics
PURPOSE: Delineation of the clinical target volume (CTV) and organs-at-risk (OARs) is important in cervical cancer radiotherapy. But it is generally labor-intensive, time-consuming, and subjective. This paper proposes a parallel-path attention fusion...

A comparative study of deep learning-based knowledge-based planning methods for 3D dose distribution prediction of head and neck.

Journal of applied clinical medical physics
PURPOSE: In this paper, we compare four novel knowledge-based planning (KBP) algorithms using deep learning to predict three-dimensional (3D) dose distributions of head and neck plans using the same patients' dataset and quantitative assessment metri...

Head and neck reconstruction in the vessel depleted neck using robot-assisted harvesting of the internal mammary vessels.

The British journal of oral & maxillofacial surgery
We report a novel technique of robot-assisted harvesting of the internal mammary vessels to provide effective recipient vessels in a patient with bilateral vessel depleted neck (VDN). A 44-year-old with a Notani grade III osteoradionecrosis (ORN) of ...

A clinical and time savings evaluation of a deep learning automatic contouring algorithm.

Medical dosimetry : official journal of the American Association of Medical Dosimetrists
Automatic contouring algorithms may streamline clinical workflows by reducing normal organ-at-risk (OAR) contouring time. Here we report the first comprehensive quantitative and qualitative evaluation, along with time savings assessment for a prototy...

Sagittal intervertebral rotational motion: a deep learning-based measurement on flexion-neutral-extension cervical lateral radiographs.

BMC musculoskeletal disorders
BACKGROUND: The analysis of sagittal intervertebral rotational motion (SIRM) can provide important information for the evaluation of cervical diseases. Deep learning has been widely used in spinal parameter measurements, however, there are few invest...

Initial experience of a deep learning application for the differentiation of Kikuchi-Fujimoto's disease from tuberculous lymphadenitis on neck CECT.

Scientific reports
Neck contrast-enhanced CT (CECT) is a routine tool used to evaluate patients with cervical lymphadenopathy. This study aimed to evaluate the ability of convolutional neural networks (CNNs) to classify Kikuchi-Fujimoto's disease (KD) and cervical tube...

Incidentalomas in chest CT.

The British journal of radiology
Advances in imaging technology have dramatically increased the resolution of CT and improved detection of disease; these advances also have led to an increase in incidentalomas or incidental findings that often do not represent significant disease. I...

Development of a radiomics and machine learning model for predicting occult cervical lymph node metastasis in patients with tongue cancer.

Oral surgery, oral medicine, oral pathology and oral radiology
OBJECTIVE: We aimed to develop a predictive model for occult cervical lymph node metastasis in patients with tongue cancer using radiomics and machine learning from pretreatment contrast-enhanced computed tomography.