AIMC Topic: Laryngeal Neoplasms

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Optical Biopsy: Automated Classification of Airway Endoscopic Findings Using a Convolutional Neural Network.

The Laryngoscope Apr 28, 2020
OBJECTIVES/HYPOTHESIS: Create an autonomous computational system to classify endoscopy findings.
Humans Neural Networks, Computer Laryngoscopy Laryngeal Neoplasms Laryngeal Diseases Larynx Artificial Intelligence Machine Learning Biopsy Image Interpretation, Computer-Assisted
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Machine learning helps identifying volume-confounding effects in radiomics.

Physica medica : PM : an international journal devoted to the applications of physics to medicine and biology : official journal of the Italian Association of Biomedical Physics (AIFB) Feb 20, 2020
Highlighting the risk of biases in radiomics-based models will help improve their quality and increase usage as decision support systems in the clinic. In this study we use machine learning-based methods to identify the presence of volume-confounding...
Software Algorithms Databases, Factual Retrospective Studies Humans Machine Learning Aged, 80 and over Adult Male Middle Aged Tomography, X-Ray Computed Female Carcinoma, Squamous Cell Aged Cluster Analysis Regression Analysis Laryngeal Neoplasms Principal Component Analysis Oropharyngeal Neoplasms Radiometry Decision Support Systems, Clinical Lung Neoplasms Squamous Cell Carcinoma of Head and Neck Carcinoma, Non-Small-Cell Lung
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Automatic Recognition of Laryngoscopic Images Using a Deep-Learning Technique.

The Laryngoscope Feb 18, 2020
OBJECTIVES/HYPOTHESIS: To develop a deep-learning-based computer-aided diagnosis system for distinguishing laryngeal neoplasms (benign, precancerous lesions, and cancer) and improve the clinician-based accuracy of diagnostic assessments of laryngosco...
Laryngeal Neoplasms Otolaryngologists Female Humans Male Retrospective Studies Reproducibility of Results Deep Learning Image Interpretation, Computer-Assisted Adult Laryngoscopy Sensitivity and Specificity
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Computer-aided diagnosis of laryngeal cancer via deep learning based on laryngoscopic images.

EBioMedicine Oct 5, 2019
OBJECTIVE: To develop a deep convolutional neural network (DCNN) that can automatically detect laryngeal cancer (LCA) in laryngoscopic images.
Laryngoscopy Laryngeal Neoplasms Humans Reproducibility of Results Sensitivity and Specificity Diagnosis, Computer-Assisted Deep Learning Image Processing, Computer-Assisted Models, Theoretical
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A Deep Learning Model for Predicting Xerostomia Due to Radiation Therapy for Head and Neck Squamous Cell Carcinoma in the RTOG 0522 Clinical Trial.

International journal of radiation oncology, biology, physics Jun 13, 2019
PURPOSE: Xerostomia commonly occurs in patients who undergo head and neck radiation therapy and can seriously affect patients' quality of life. In this study, we developed a xerostomia prediction model with radiation treatment data using a 3-dimensio...
Hypopharyngeal Neoplasms Pharyngeal Neoplasms Oropharyngeal Neoplasms Submandibular Gland Xerostomia Tomography, X-Ray Computed Radiotherapy, Conformal Radiotherapy, Image-Guided Deep Learning Laryngeal Neoplasms ROC Curve Squamous Cell Carcinoma of Head and Neck Logistic Models Area Under Curve Parotid Gland Radiotherapy Planning, Computer-Assisted Humans Retrospective Studies
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Rapid histology of laryngeal squamous cell carcinoma with deep-learning based stimulated Raman scattering microscopy.

Theranostics Apr 13, 2019
Maximal resection of tumor while preserving the adjacent healthy tissue is particularly important for larynx surgery, hence precise and rapid intraoperative histology of laryngeal tissue is crucial for providing optimal surgical outcomes. We hypothes...
Humans Automation, Laboratory Carcinoma, Squamous Cell Pathology, Surgical Deep Learning Nonlinear Optical Microscopy China Histological Techniques Laryngeal Neoplasms
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Demographic and Symptomatic Features of Voice Disorders and Their Potential Application in Classification Using Machine Learning Algorithms.

Folia phoniatrica et logopaedica : official organ of the International Association of Logopedics and Phoniatrics (IALP) Sep 5, 2018
BACKGROUND: Studies have used questionnaires of dysphonic symptoms to screen voice disorders. This study investigated whether the differential presentation of demographic and symptomatic features can be applied to computerized classification.
Glottis Voice Disorders Laryngeal Neoplasms Voice Quality Symptom Assessment Supervised Machine Learning Male Humans Retrospective Studies Wounds and Injuries Sex Factors Middle Aged Demography Neural Networks, Computer Smoking Female Severity of Illness Index Vocal Cord Paralysis Algorithms Alcohol Drinking Aged Age Factors Adult
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Learning-based classification of informative laryngoscopic frames.

Computer methods and programs in biomedicine Jan 31, 2018
BACKGROUND AND OBJECTIVE: Early-stage diagnosis of laryngeal cancer is of primary importance to reduce patient morbidity. Narrow-band imaging (NBI) endoscopy is commonly used for screening purposes, reducing the risks linked to a biopsy but at the co...
Support Vector Machine Early Detection of Cancer Pattern Recognition, Automated Laryngoscopy Laryngeal Neoplasms Humans Machine Learning Diagnosis, Computer-Assisted
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Letter to the Editor regarding "Robotic or non-robotic transoral laryngectomy".

Head & neck Nov 17, 2017
Cadaver Humans Laryngeal Neoplasms Robotics Laryngectomy Robotic Surgical Procedures
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Reply to Letter to the Editor regarding "Robotic or non-robotic transoral laryngectomy".

Head & neck Nov 17, 2017
Robotics Humans Robotic Surgical Procedures Laryngectomy Cadaver Laryngeal Neoplasms
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