ReviewMedical & biological engineering & computing2019
Advanced computing solutions for analysis of laryngeal disorders.
Review in Medical & biological engineering & computing, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
7 citing papers in PubMed, 13 citations in OpenAlex.
- Mel-DEPTHS: a benchmark dataset for epidermis and tumor segmentation for melanoma staging.Medical & biological engineering & computing · 2026Article
- Clinical Applications of Deep Learning for Glottal Area Segmentation and Glottal Area Waveform Feature Computation.IEEE open journal of engineering in medicine and biology · 2026Article
- Review
- Value of high-speed videoendoscopy as an auxiliary tool in differentiation of benign and malignant unilateral vocal lesions.Journal of cancer research and clinical oncology · 2024Article
- Intelligent speech technologies for transcription, disease diagnosis, and medical equipment interactive control in smart hospitals: A review.Computers in biology and medicine · 2023Review
- Localization and quantification of glottal gaps on deep learning segmentation of vocal folds.Scientific reports · 2023Article
- Cyclist Effort Features: A Novel Technique for Image Texture Characterization Applied to Larynx Cancer Classification in Contact Endoscopy-Narrow Band Imaging.Diagnostics (Basel, Switzerland) · 2021Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
2 authors at 1 institution in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Clinical diagnosis of voice pathologies is performed by analyzing audio, color, shape, and vibration patterns of the laryngeal recordings which are taken with medical imaging devices such as video-laryngostroboscope, direct laryngoscopy, and high-speed videoendoscopes. This paper examines state-of-the-art methods and reveals open issues and problems of computing solutions for analysis and identification of laryngeal disorders. We propose a categorical representation of the most significant applications published so far in terms of their scopes, used methodologies, and achieved results. Laryngeal image/video analysis is discussed in four main categories: segmentation of vocal folds, classification of vocal fold disorders, vocal fold vibration analysis, and vocal fold image stitching. By this study, we reveal new opportunities and potentials of vision-based computerized solutions for evaluation, early diagnosis, and prevention of laryngeal disorders. Graphical abstract.
Indexed as
Identifiers
What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.