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Kavak ÖT, Gündüz Ş, Vural C, Enver N. Artificial intelligence based diagnosis of sulcus: assesment of videostroboscopy via deep learning. Eur Arch Otorhinolaryngol 2024:10.1007/s00405-024-08801-y. [PMID: 39001913 DOI: 10.1007/s00405-024-08801-y] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/27/2024] [Accepted: 06/19/2024] [Indexed: 07/15/2024]
Abstract
PURPOSE To develop a convolutional neural network (CNN)-based model for classifying videostroboscopic images of patients with sulcus, benign vocal fold (VF) lesions, and healthy VFs to improve clinicians' accuracy in diagnosis during videostroboscopies when evaluating sulcus. MATERIALS AND METHODS Videostroboscopies of 433 individuals who were diagnosed with sulcus (91), who were diagnosed with benign VF diseases (i.e., polyp, nodule, papilloma, cyst, or pseudocyst [311]), or who were healthy (33) were analyzed. After extracting 91,159 frames from videostroboscopies, a CNN-based model was created and tested. The healthy and sulcus groups underwent binary classification. In the second phase of the study, benign VF lesions were added to the training set, and multiclassification was executed across all groups. The proposed CNN-based model results were compared with five laryngology experts' assessments. RESULTS In the binary classification phase, the CNN-based model achieved 98% accuracy, 98% recall, 97% precision, and a 97% F1 score for classifying sulcus and healthy VFs. During the multiclassification phase, when evaluated on a subset of frames encompassing all included groups, the CNN-based model demonstrated greater accuracy when compared with that of the five laryngologists (%76 versus 72%, 68%, 72%, 63%, and 72%). CONCLUSION The utilization of a CNN-based model serves as a significant aid in the diagnosis of sulcus, a VF disease that presents notable challenges in the diagnostic process. Further research could be undertaken to assess the practicality of implementing this approach in real-time application in clinical practice.
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Affiliation(s)
- Ömer Tarık Kavak
- Department of Otorhinolaryngology, Marmara University Faculty of Medicine, Pendik Training and Research Hospital, Fevzi Çakmak Muhsin Yazıcıoğlu Street, İstanbul, 34899, Turkey.
| | - Şevket Gündüz
- VRLab Academy, 32 Willoughby Rd, Harringay Ladder, London, N8 0JG, UK
| | - Cabir Vural
- Marmara University Faculty of Engineering, Electrical and Electronics Engineering, Başıbüyük, RTE Campus, İstanbul, 34854, Turkey
| | - Necati Enver
- Department of Otorhinolaryngology, Marmara University Faculty of Medicine, Pendik Training and Research Hospital, Fevzi Çakmak Muhsin Yazıcıoğlu Street, İstanbul, 34899, Turkey
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Edalati S, Vasan V, Cheng CP, Patel Z, Govindaraj S, Iloreta AM. Can GPT-4 revolutionize otolaryngology? Navigating opportunities and ethical considerations. Am J Otolaryngol 2024; 45:104303. [PMID: 38678799 DOI: 10.1016/j.amjoto.2024.104303] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/10/2024] [Accepted: 04/14/2024] [Indexed: 05/01/2024]
Abstract
Otolaryngologists can enhance workflow efficiency, provide better patient care, and advance medical research and education by integrating artificial intelligence (AI) into their practices. GPT-4 technology is a revolutionary and contemporary example of AI that may apply to otolaryngology. The knowledge of otolaryngologists should be supplemented, not replaced when using GPT-4 to make critical medical decisions and provide individualized patient care. In our thorough examination, we explore the potential uses of the groundbreaking GPT-4 technology in the field of otolaryngology, covering aspects such as potential outcomes and technical boundaries. Additionally, we delve into the intricate and intellectually challenging dilemmas that emerge when incorporating GPT-4 into otolaryngology, considering the ethical considerations inherent in its implementation. Our stance is that GPT-4 has the potential to be very helpful. Its capabilities, which include aid in clinical decision-making, patient care, and administrative job automation, present exciting possibilities for enhancing patient outcomes, boosting the efficiency of healthcare delivery, and enhancing patient experiences. Even though there are still certain obstacles and limitations, the progress made so far shows that GPT-4 can be a valuable tool for modern medicine. GPT-4 may play a more significant role in clinical practice as technology develops, helping medical professionals deliver high-quality care tailored to every patient's unique needs.
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Affiliation(s)
- Shaun Edalati
- Department of Otolaryngology-Head and Neck Surgery, Icahn School of Medicine at Mount Sinai, New York, NY, USA.
| | - Vikram Vasan
- Department of Otolaryngology-Head and Neck Surgery, Icahn School of Medicine at Mount Sinai, New York, NY, USA
| | - Christopher P Cheng
- Department of Otolaryngology-Head and Neck Surgery, Icahn School of Medicine at Mount Sinai, New York, NY, USA
| | - Zara Patel
- Department of Otolaryngology-Head & Neck Surgery, Stanford University School of Medicine, Stanford, CA, USA
| | - Satish Govindaraj
- Department of Otolaryngology-Head and Neck Surgery, Icahn School of Medicine at Mount Sinai, New York, NY, USA
| | - Alfred Marc Iloreta
- Department of Otolaryngology-Head and Neck Surgery, Icahn School of Medicine at Mount Sinai, New York, NY, USA
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3
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Oliver J, Alapati R, Lee J, Bur A. Artificial Intelligence in Head and Neck Surgery. Otolaryngol Clin North Am 2024:S0030-6665(24)00070-7. [PMID: 38910064 DOI: 10.1016/j.otc.2024.05.001] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 06/25/2024]
Abstract
This article explores artificial intelligence's (AI's) role in otolaryngology for head and neck cancer diagnosis and management. It highlights AI's potential in pattern recognition for early cancer detection, prognostication, and treatment planning, primarily through image analysis using clinical, endoscopic, and histopathologic images. Radiomics is also discussed at length, as well as the many ways that radiologic image analysis can be utilized, including for diagnosis, lymph node metastasis prediction, and evaluation of treatment response. The study highlights AI's promise and limitations, underlining the need for clinician-data scientist collaboration to enhance head and neck cancer care.
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Affiliation(s)
- Jamie Oliver
- Department of Otolaryngology-Head and Neck Surgery, University of Kansas School of Medicine, 3901 Rainbow Boulevard M.S. 3010, Kansas City, KS, USA
| | - Rahul Alapati
- Department of Otolaryngology-Head and Neck Surgery, University of Kansas School of Medicine, 3901 Rainbow Boulevard M.S. 3010, Kansas City, KS, USA
| | - Jason Lee
- Department of Otolaryngology-Head and Neck Surgery, University of Kansas School of Medicine, 3901 Rainbow Boulevard M.S. 3010, Kansas City, KS, USA
| | - Andrés Bur
- Department of Otolaryngology-Head and Neck Surgery, University of Kansas School of Medicine, 3901 Rainbow Boulevard M.S. 3010, Kansas City, KS, USA.
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Kuo HC, Hsieh YP, Tseng HH, Wang CT, Fang SH, Tsao Y. Toward Real-World Voice Disorder Classification. IEEE Trans Biomed Eng 2023; 70:2922-2932. [PMID: 37099463 DOI: 10.1109/tbme.2023.3270532] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 04/27/2023]
Abstract
OBJECTIVE Voice disorders significantly compromise individuals' ability to speak in their daily lives. Without early diagnosis and treatment, these disorders may deteriorate drastically. Thus, automatic classification systems at home are desirable for people who are inaccessible to clinical disease assessments. However, the performance of such systems may be weakened due to the constrained resources and domain mismatch between the clinical data and noisy real-world data. METHODS This study develops a compact and domain-robust voice disorder classification system to identify the utterances of health, neoplasm, and benign structural diseases. Our proposed system utilizes a feature extractor model composed of factorized convolutional neural networks and subsequently deploys domain adversarial training to reconcile the domain mismatch by extracting domain-invariant features. RESULTS The results show that the unweighted average recall in the noisy real-world domain improved by 13% and remained at 80% in the clinic domain with only slight degradation. The domain mismatch was effectively eliminated. Moreover, the proposed system reduced the usage of both memory and computation by over 73.9%. CONCLUSION By deploying factorized convolutional neural networks and domain adversarial training, domain-invariant features can be derived for voice disorder classification with limited resources. The promising results confirm that the proposed system can significantly reduce resource consumption and improve classification accuracy by considering the domain mismatch. SIGNIFICANCE To the best of our knowledge, this is the first study that jointly considers real-world model compression and noise-robustness issues in voice disorder classification. The proposed system is intended for application to embedded systems with limited resources.
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Kumar S P, B P. Optical Flow Glottovibrogram for the examination of vocal fold pathology. ANNUAL INTERNATIONAL CONFERENCE OF THE IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. ANNUAL INTERNATIONAL CONFERENCE 2023; 2023:1-4. [PMID: 38083520 DOI: 10.1109/embc40787.2023.10340075] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/18/2023]
Abstract
Laryngeal high-speed video endoscopy is performed to examine the cycles of vocal fold vibrations in detail and to diagnose voice abnormalities. One of the recent image processing techniques for visualizing vocal fold vibration is optical flow-based playbacks, which include optical flow kymograms (OFKG) for local dynamics, optical flow glottovibrogram (OFGVG) and glottal optical flow waveforms (GOFW) for global dynamics. In recent times, various optical flow computing algorithms have been developed. In this paper, we used four well-known optical flow algorithms Horn Schunk, Lucas Kanade, Gunnar Farneback, and TVL1 to construct the optical flow playbacks. The proposed playback reliability is examined by comparing them to traditional representations such as Phonovibrogram (PVG). Since PVG and OFGVG are interconnected, a comparison study was carried out to better comprehend their interaction.Clinical Relevance- Both OFGVG and PVG add to the precision of interpreting pathological conditions by offering complementary information to the conventional spatiotemporal representations.
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Suvvari TK. The Role of Artificial Intelligence in Diagnosis and Management of Laryngeal Disorders. EAR, NOSE & THROAT JOURNAL 2023:1455613231175053. [PMID: 37194691 DOI: 10.1177/01455613231175053] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 05/18/2023] Open
Affiliation(s)
- Tarun Kumar Suvvari
- Rangaraya Medical College, Kakinada, India
- Squad Medicine and Research, Andhra Pradesh, India
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Bensoussan Y, Vanstrum EB, Johns MM, Rameau A. Artificial Intelligence and Laryngeal Cancer: From Screening to Prognosis: A State of the Art Review. Otolaryngol Head Neck Surg 2023; 168:319-329. [PMID: 35787073 DOI: 10.1177/01945998221110839] [Citation(s) in RCA: 8] [Impact Index Per Article: 8.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/14/2022] [Accepted: 06/13/2022] [Indexed: 11/16/2022]
Abstract
OBJECTIVE This state of the art review aims to examine contemporary advances in applications of artificial intelligence (AI) to the screening, detection, management, and prognostication of laryngeal cancer (LC). DATA SOURCES Four bibliographic databases were searched: PubMed, EMBASE, Cochrane, and IEEE. REVIEW METHODS A structured review of the current literature (up to January 2022) was performed. Search terms related to topics of AI in LC were identified and queried by 2 independent reviewers. Citations of selected studies and review articles were also evaluated to ensure comprehensiveness. CONCLUSIONS AI applications in LC have encompassed a variety of data modalities, including radiomics, genomics, acoustics, clinical data, and videomics, to support screening, diagnosis, therapeutic decision making, and prognosis. However, most studies remain at the proof-of-concept level, as AI algorithms are trained on single-institution databases with limited data sets and a single data modality. IMPLICATIONS FOR PRACTICE AI algorithms in LC will need to be trained on large multi-institutional data sets and integrate multimodal data for optimal performance and clinical utility from screening to prognosis. Out of the data types reviewed, genomics has the most potential to provide generalizable models thanks to available large multi-institutional open access genomic data sets. Voice acoustic data represent an inexpensive and accurate biomarker, which is easy and noninvasive to capture, offering a unique opportunity for screening and monitoring of LA, especially in low-resource settings.
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Affiliation(s)
- Yael Bensoussan
- Department of Otolaryngology-Head and Neck Surgery, University of South Florida, Tampa, Florida, USA
| | - Erik B Vanstrum
- Keck School of Medicine, University of Southern California, Los Angeles, California, USA
| | - Michael M Johns
- Department of Otolaryngology-Head and Neck Surgery, University of Southern California, Los Angeles, California, USA
| | - Anaïs Rameau
- Department of Otolaryngology-Head and Neck Surgery, Sean Parker Institute for the Voice, Weill Cornell Medical College, New York, New York, USA
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Pedersen M, Larsen CF, Madsen B, Eeg M. Localization and quantification of glottal gaps on deep learning segmentation of vocal folds. Sci Rep 2023; 13:878. [PMID: 36650265 PMCID: PMC9845318 DOI: 10.1038/s41598-023-27980-y] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/01/2022] [Accepted: 01/11/2023] [Indexed: 01/19/2023] Open
Abstract
The entire glottis has mostly been the focus in the tracking of the vocal folds, both manually and automatically. From a treatment point of view, the various regions of the glottis are of specific interest. The aim of the study was to test if it was possible to supplement an existing convolutional neural network (CNN) with post-network calculations for the localization and quantification of posterior glottal gaps during phonation, usable for vocal fold function analysis of e.g. laryngopharyngeal reflux findings. 30 subjects/videos with insufficient closure in the rear glottal area and 20 normal subjects/videos were selected from our database, recorded with a commercial high-speed video setup (HSV with 4000 frames per second), and segmented with an open-source CNN for validating voice function. We made post-network calculations to localize and quantify the 10% and 50% distance lines from the rear part of the glottis. The results showed a significant difference using the algorithm at the 10% line distance between the two groups of p < 0.0001 and no difference at 50%. These novel results show that it is possible to use post-network calculations on CNNs for the localization and quantification of posterior glottal gaps.
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Mathews S, Dham R, Dutta A, Jose A. Computational Intelligence in Otorhinolaryngology. JOURNAL OF MARINE MEDICAL SOCIETY 2023. [DOI: 10.4103/jmms.jmms_159_22] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/19/2023] Open
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Sahoo PK, Mishra S, Panigrahi R, Bhoi AK, Barsocchi P. An Improvised Deep-Learning-Based Mask R-CNN Model for Laryngeal Cancer Detection Using CT Images. SENSORS (BASEL, SWITZERLAND) 2022; 22:s22228834. [PMID: 36433430 PMCID: PMC9697116 DOI: 10.3390/s22228834] [Citation(s) in RCA: 7] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 10/05/2022] [Revised: 11/08/2022] [Accepted: 11/10/2022] [Indexed: 05/30/2023]
Abstract
Recently, laryngeal cancer cases have increased drastically across the globe. Accurate treatment for laryngeal cancer is intricate, especially in the later stages. This type of cancer is an intricate malignancy inside the head and neck area of patients. In recent years, diverse diagnosis approaches and tools have been developed by researchers for helping clinical experts to identify laryngeal cancer effectively. However, these existing tools and approaches have diverse issues related to performance constraints such as lower accuracy in the identification of laryngeal cancer in the initial stage, more computational complexity, and large time consumption in patient screening. In this paper, the authors present a novel and enhanced deep-learning-based Mask R-CNN model for the identification of laryngeal cancer and its related symptoms by utilizing diverse image datasets and CT images in real time. Furthermore, our suggested model is capable of capturing and detecting minor malignancies of the larynx portion in a significant and faster manner in the real-time screening of patients, and it saves time for the clinicians, allowing for more patient screening every day. The outcome of the suggested model is enhanced and pragmatic and obtained an accuracy of 98.99%, precision of 98.99%, F1 score of 97.99%, and recall of 96.79% on the ImageNet dataset. Several studies have been performed in recent years on laryngeal cancer detection by using diverse approaches from researchers. For the future, there are vigorous opportunities for further research to investigate new approaches for laryngeal cancer detection by utilizing diverse and large dataset images.
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Affiliation(s)
- Pravat Kumar Sahoo
- School of Computer Engineering, KIIT Deemed to be University, Bhubaneswar 751024, India
| | - Sushruta Mishra
- School of Computer Engineering, KIIT Deemed to be University, Bhubaneswar 751024, India
| | - Ranjit Panigrahi
- Department of Computer Applications, Sikkim Manipal Institute of Technology, Sikkim Manipal University, Majitar, Rangpo 737136, India
| | - Akash Kumar Bhoi
- KIET Group of Institutions, Delhi-NCR, Ghaziabad 201206, India
- Directorate of Research, Sikkim Manipal University, Gangtok 737102, India
- Institute of Information Science and Technologies, National Research Council, 56124 Pisa, Italy
| | - Paolo Barsocchi
- Institute of Information Science and Technologies, National Research Council, 56124 Pisa, Italy
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Comparison of convolutional neural networks for classification of vocal fold nodules from high-speed video images. Eur Arch Otorhinolaryngol 2022; 280:2365-2371. [PMID: 36357609 DOI: 10.1007/s00405-022-07736-6] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/07/2022] [Accepted: 10/29/2022] [Indexed: 11/12/2022]
Abstract
OBJECTIVES Deep learning is in this study used through convolutional neural networks (CNN) to the determination of vocal fold nodules. Through high-speed video (HSV) images and computer-assisted tools, a comparison of convolutional neural network models and their accuracy will be presented. METHODS The data have been collected by an Ear Nose Throat (ENT) specialist with a 90° rigid scope in the years from 2007 to 2019, where 15.732 high-speed videos have been collected from 7909 patients. A total of 4000 images have been carefully selected, 2000 images were of normal vocal folds and 2000 images were of vocal folds with varying degrees of vocal fold nodules. These images were then split into training-, validation-, and testing-data set, for use with a CNN model with 5 layers (CNN5) and compared to other models: VGG19, MobileNetV2, and Inception-ResNetV2. To compare the neural network models, the following evaluation metrics have been calculated: accuracy, sensitivity, specificity, precision, and negative predictive values. RESULTS All the trained CNN models have shown high accuracy when applied to the test set. The accuracy is 97.75%, 83.5%, 91.5%, and 89.75%, for CNN5, VGG19, MobileNetV2, and InceptionResNetV2, respectively. CONCLUSIONS Precision was identified as the most relevant performance metric for a study that focuses on the classification of vocal fold nodules. The highest performing model was MobilNetV2 with a precision of 97.7%. The average accuracy across all 4 neural networks was 90.63% showing that neural networks can be used for classifying vocal fold nodules in a clinical setting.
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Compton EC, Cruz T, Andreassen M, Beveridge S, Bosch D, Randall DR, Livingstone D. Developing an Artificial Intelligence Tool to Predict Vocal Cord Pathology in Primary Care Settings. Laryngoscope 2022. [PMID: 36226791 DOI: 10.1002/lary.30432] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/23/2022] [Revised: 08/16/2022] [Accepted: 09/09/2022] [Indexed: 11/12/2022]
Abstract
OBJECTIVES Diagnostic tools for voice disorders are lacking for primary care physicians. Artificial intelligence (AI) tools may add to the armamentarium for physicians, decreasing the time to diagnosis and limiting the burden of dysphonia. METHODS Voice recordings of patients were collected from 2019 to 2021 using smartphones. The Saarbruecken dataset was included for comparison. Audio files were converted to mel-spectrograms using TensorFlow. Diagnostic categories were created to group pathology, including neurological and muscular disorders, inflammatory, mass lesions, and normal. The samples were further separated into sustained/a/and the rainbow passage. RESULTS Two hundred three prospective samples and 1131 samples were used from the Saarbruecken database. The AI detected abnormal pathology with an F1-score of 98%. The artificial neural network (ANN) differentiated key pathologies, including unilateral paralysis, laryngitis, adductor spasmodic dysphonia (ADSD), mass lesions, and normal samples with 39%-87% F-1 scores. The Calgary database models had higher F-1 scores in a head-to-head comparison to the Saarbruecken and combined datasets (87% vs. 58% and 50%). The AI outperformed otolaryngologists using a standardized test set of recordings (83% compared to 55% ± 15%). CONCLUSION An AI tool was created to differentiate pathology by individual or categorical diagnosis with high evaluation metrics. Prospective data should be collected in a controlled fashion to reduce intrinsic variability between recordings. Multi-center data collaborations are imperative to increase the prediction capability of AI tools for detecting vocal cord pathology. We provide proof-of-concept for an AI tool to assist primary care physicians in managing dysphonic patients. LEVEL OF EVIDENCE 3 Laryngoscope, 2022.
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Affiliation(s)
- Evan C Compton
- Section of Otolaryngology-Head and Neck Surgery, Department of Surgery, Cumming School of Medicine, University of Calgary, Calgary, Alberta, Canada
| | - Tim Cruz
- Department of Data Science and Analytics, Faculty of Science, University of Calgary, Calgary, Alberta, Canada
| | - Meri Andreassen
- Section of Otolaryngology-Head and Neck Surgery, Calgary Voice Program, Alberta Health Services, Calgary, Alberta, Canada
| | - Shari Beveridge
- Section of Otolaryngology-Head and Neck Surgery, Calgary Voice Program, Alberta Health Services, Calgary, Alberta, Canada
| | - Doug Bosch
- Section of Otolaryngology-Head and Neck Surgery, Department of Surgery, Cumming School of Medicine, University of Calgary, Calgary, Alberta, Canada
| | - Derrick R Randall
- Section of Otolaryngology-Head and Neck Surgery, Department of Surgery, Cumming School of Medicine, University of Calgary, Calgary, Alberta, Canada
| | - Devon Livingstone
- Section of Otolaryngology-Head and Neck Surgery, Department of Surgery, Cumming School of Medicine, University of Calgary, Calgary, Alberta, Canada
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George MM, Tolley NS. AIM in Otolaryngology and Head and Neck Surgery. Artif Intell Med 2022. [DOI: 10.1007/978-3-030-64573-1_198] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/09/2022]
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A roadmap of six different pathways to improve survival in laryngeal cancer patients. Curr Opin Otolaryngol Head Neck Surg 2021; 29:65-78. [PMID: 33337612 DOI: 10.1097/moo.0000000000000684] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/24/2022]
Abstract
PURPOSE OF REVIEW Laryngeal cancer continues to require improvement in earlier stage diagnosis and better imaging delineation of disease, and hence 'more evidence-based' selection of treatment, as recent evidence suggests that related mortality, in the last decades, has not significantly decreased worldwide. Even though the reasons are not fully understood, there persists an urgency for a review and development of future strategies to embrace such clinical and diagnostic challenges from a political, societal, as well as scientific and clinical points of view. RECENT FINDINGS This review of the published literature suggests that survival improvement in laryngeal cancer may be achieved by fuelling and combining at least some or all of six targeted agendas: documentation of disease global incidence and national burden monitoring; development and implementation of high-quality cancer registries; education on risk factors and hazardous habits associated with laryngeal cancer for the general population; active modification of proven at-risk population lifestyles; centralization of treatment; and use of machine learning of gathered 'big data' and their integration into approaches for the optimization of prevention and treatments strategies. SUMMARY Laryngeal cancer should be tackled on several fronts, commencing with disease monitoring and prevention, up to treatment optimisation. Available modern resources offer the possibility to generate significant advances in laryngeal cancer management. However, each nation needs to develop a comprehensive approach, which is an essential prerequisite to obtain meaningful improvement on results.
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George MM, Tolley NS. AIM in Otolaryngology and Head & Neck Surgery. Artif Intell Med 2021. [DOI: 10.1007/978-3-030-58080-3_198-1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/24/2022]
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16
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Bensoussan Y, Pinto J, Crowson M, Walden PR, Rudzicz F, Johns M. Deep Learning for Voice Gender Identification: Proof-of-concept for Gender-Affirming Voice Care. Laryngoscope 2020; 131:E1611-E1615. [PMID: 33219707 DOI: 10.1002/lary.29281] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/23/2020] [Revised: 11/04/2020] [Accepted: 11/06/2020] [Indexed: 11/08/2022]
Abstract
OBJECTIVES/HYPOTHESIS The need for gender-affirming voice care has been increasing in the transgender population in the last decade. Currently, objective treatment outcome measurements are lacking to assess the success of these interventions. This study uses neural network models to predict binary gender from short audio samples of "male" and "female" voices. This preliminary work is a proof-of-concept for further work to develop an AI-assisted treatment outcome measure for gender-affirming voice care. STUDY DESIGN Retrospective cohort study. METHODS Two hundred seventy-eight voices from male and female speakers from the Perceptual Voice Qualities Database were used to train a deep neural network to classify voices as male or female. Each audio sample was mapped to the frequency domain using Mel spectrograms. To optimize model performance, we performed 10-fold cross validation of the entire dataset. The dataset was split into 80% training, 10% validation, and 10% test. RESULTS Overall accuracy of 92% was obtained, both when considering the accuracy per spectrum and per patient metric. The accuracy of the model was higher for recognizing female voices (F1 score of 0.94) compared to male voices (F1 score of 0.87). CONCLUSIONS This proof of concept study shows promising performance for further development of an AI-assisted tool to provide objective treatment outcome measurements for gender affirming voice care. LEVEL OF EVIDENCE 3 Laryngoscope, 131:E1611-E1615, 2021.
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Affiliation(s)
- Yael Bensoussan
- Caruso Department of Otolaryngology - HNS, University of Southern California, Los Angeles, California, U.S.A
| | - Jeremy Pinto
- Mila, Quebec Artificial Intelligence Institute, Montreal, Quebec, Canada
| | - Matthew Crowson
- Harvard Department of Otolaryngology - HNS, Massachusetts Eye and Ear Infirmary, Boston, Massachusetts, U.S.A
| | - Patrick R Walden
- Department of Communication Sciences and Disorders, St. John's University, Queens, New York, U.S.A
| | - Frank Rudzicz
- Vector Institute for Artificial Intelligence, Toronto, Ontario, Canada
| | - Michael Johns
- Caruso Department of Otolaryngology - Head and Neck Surgery, University of Southern California, Los Angeles, California, U.S.A
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Cho WK, Choi SH. Comparison of Convolutional Neural Network Models for Determination of Vocal Fold Normality in Laryngoscopic Images. J Voice 2020; 36:590-598. [PMID: 32873430 DOI: 10.1016/j.jvoice.2020.08.003] [Citation(s) in RCA: 12] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/01/2020] [Revised: 08/04/2020] [Accepted: 08/04/2020] [Indexed: 01/02/2023]
Abstract
OBJECTIVES Deep learning using convolutional neural networks (CNNs) is widely used in medical imaging research. This study was performed to investigate if vocal fold normality in laryngoscopic images can be determined by CNN-based deep learning and to compare accuracy of CNN models and explore the feasibility of application of deep learning on laryngoscopy. METHODS Laryngoscopy videos were screen-captured and each image was cropped to include abducted vocal fold regions. A total of 2216 image (899 normal, 1317 abnormal) were allocated to training, validation, and test sets. Augmentation of training sets was used to train a constructed CNN model with six layers (CNN6), VGG16, Inception V3, and Xception models. Trained models were applied to the test set; for each model, receiver operating characteristic curves and cutoff values were obtained. Sensitivity, specificity, positive predictive value, negative predictive value, and accuracy were calculated. The best model was employed in video-streams and localization of features was attempted using Grad-CAM. RESULTS All of the trained models showed high area under the receiver operating characteristic curve and the most discriminative cutoff levels of probability of normality were determined to be 35.6%, 61.8%, 13.5%, 39.7% for CNN6, VGG16, Inception V3, and Xception models, respectively. Accuracy of the CNN models selecting normal and abnormal vocal folds in the test set was 82.3%, 99.7%, 99.1%, and 83.8%, respectively. CONCLUSION All four models showed acceptable diagnostic accuracy. Performance of VGG16 and Inception V3 was better than the simple CNN6 model and the recently published Xception model. Real-time classification with a combination of the VGG16 model, OpenCV, and Grad-CAM on a video stream showed the potential clinical applications of the deep learning model in laryngoscopy.
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Affiliation(s)
- Won Ki Cho
- Departments of Otorhinolaryngology-Head and Neck Surgery, Asan Medical Center, University of Ulsan College of Medicine, Seoul, South Korea
| | - Seung-Ho Choi
- Departments of Otorhinolaryngology-Head and Neck Surgery, Asan Medical Center, University of Ulsan College of Medicine, Seoul, South Korea.
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Parker F, Brodsky MB, Akst LM, Ali H. Machine Learning in Laryngoscopy Analysis: A Proof of Concept Observational Study for the Identification of Post-Extubation Ulcerations and Granulomas. Ann Otol Rhinol Laryngol 2020; 130:286-291. [PMID: 32795159 DOI: 10.1177/0003489420950364] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/16/2022]
Abstract
OBJECTIVE Computer-aided analysis of laryngoscopy images has potential to add objectivity to subjective evaluations. Automated classification of biomedical images is extremely challenging due to the precision required and the limited amount of annotated data available for training. Convolutional neural networks (CNNs) have the potential to improve image analysis and have demonstrated good performance in many settings. This study applied machine-learning technologies to laryngoscopy to determine the accuracy of computer recognition of known laryngeal lesions found in patients post-extubation. METHODS This is a proof of concept study that used a convenience sample of transnasal, flexible, distal-chip laryngoscopy images from patients post-extubation in the intensive care unit. After manually annotating images at the pixel-level, we applied a CNN-based method for analysis of granulomas and ulcerations to test potential machine-learning approaches for laryngoscopy analysis. RESULTS A total of 127 images from 25 patients were manually annotated for presence and shape of these lesions-100 for training, 27 for evaluating the system. There were 193 ulcerations (148 in the training set; 45 in the evaluation set) and 272 granulomas (208 in the training set; 64 in the evaluation set) identified. Time to annotate each image was approximately 3 minutes. Machine-based analysis demonstrated per-pixel sensitivity of 82.0% and 62.8% for granulomas and ulcerations respectively; specificity was 99.0% and 99.6%. CONCLUSION This work demonstrates the feasibility of machine learning via CNN-based methods to add objectivity to laryngoscopy analysis, suggesting that CNN may aid in laryngoscopy analysis for other conditions in the future.
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Affiliation(s)
- Felix Parker
- Department of Computer Science, Johns Hopkins University, Baltimore, MD, USA
| | - Martin B Brodsky
- Department of Physical Medicine and Rehabilitation, Johns Hopkins University, Baltimore, MD, USA.,Division Pulmonary and Critical Care Medicine, Johns Hopkins University, Baltimore, MD, USA.,Outcomes After Critical Illness and Surgery (OACIS) Research Group, Johns Hopkins University, Baltimore, MD, USA
| | - Lee M Akst
- Department of Otolaryngology - Head and Neck Surgery, Johns Hopkins University, Baltimore, MD, USA
| | - Haider Ali
- Department of Computer Science, Johns Hopkins University, Baltimore, MD, USA
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Abstract
This review provides a comprehensive compilation, from a digital image processing point of view of the most important techniques currently developed to characterize and quantify the vibration behaviour of the vocal folds, along with a detailed description of the laryngeal image modalities currently used in the clinic. The review presents an overview of the most significant glottal-gap segmentation and facilitative playbacks techniques used in the literature for the mentioned purpose, and shows the drawbacks and challenges that still remain unsolved to develop robust vocal folds vibration function analysis tools based on digital image processing.
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20
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Learned and handcrafted features for early-stage laryngeal SCC diagnosis. Med Biol Eng Comput 2019; 57:2683-2692. [PMID: 31728933 DOI: 10.1007/s11517-019-02051-5] [Citation(s) in RCA: 9] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/02/2019] [Accepted: 09/21/2019] [Indexed: 01/08/2023]
Abstract
Squamous cell carcinoma (SCC) is the most common and malignant laryngeal cancer. An early-stage diagnosis is of crucial importance to lower patient mortality and preserve both the laryngeal anatomy and vocal-fold function. However, this may be challenging as the initial larynx modifications, mainly concerning the mucosa vascular tree and the epithelium texture and color, are small and can pass unnoticed to the human eye. The primary goal of this paper was to investigate a learning-based approach to early-stage SCC diagnosis, and compare the use of (i) texture-based global descriptors, such as local binary patterns, and (ii) deep-learning-based descriptors. These features, extracted from endoscopic narrow-band images of the larynx, were classified with support vector machines as to discriminate healthy, precancerous, and early-stage SCC tissues. When tested on a benchmark dataset, a median classification recall of 98% was obtained with the best feature combination, outperforming the state of the art (recall = 95%). Despite further investigation is needed (e.g., testing on a larger dataset), the achieved results support the use of the developed methodology in the actual clinical practice to provide accurate early-stage SCC diagnosis. Graphical Abstract Workflow of the proposed solution. Patches of laryngeal tissue are pre-processed and feature extraction is performed. These features are used in the laryngeal tissue classification.
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21
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Turkmen HI, Karsligil ME. Advanced computing solutions for analysis of laryngeal disorders. Med Biol Eng Comput 2019; 57:2535-2552. [DOI: 10.1007/s11517-019-02031-9] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/07/2018] [Accepted: 08/13/2019] [Indexed: 11/29/2022]
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Bayesian Inference of Vocal Fold Material Properties from Glottal Area Waveforms Using a 2D Finite Element Model. APPLIED SCIENCES-BASEL 2019; 9. [PMID: 34046213 PMCID: PMC8153513 DOI: 10.3390/app9132735] [Citation(s) in RCA: 11] [Impact Index Per Article: 2.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Indexed: 02/06/2023]
Abstract
Bayesian estimation has been previously demonstrated as a viable method for developing subject-specific vocal fold models from observations of the glottal area waveform. These prior efforts, however, have been restricted to lumped-element fitting models and synthetic observation data. The indirect relationship between the lumped-element parameters and physical tissue properties renders extracting the latter from the former difficult. Herein we propose a finite element fitting model, which treats the vocal folds as a viscoelastic deformable body comprised of three layers. Using the glottal area waveforms generated by self-oscillating silicone vocal folds we directly estimate the elastic moduli, density, and other material properties of the silicone folds using a Bayesian importance sampling approach. Estimated material properties agree with the “ground truth” experimental values to within 3% for most parameters. By considering cases with varying subglottal pressure and medial compression we demonstrate that the finite element model coupled with Bayesian estimation is sufficiently sensitive to distinguish between experimental configurations. Additional information not available experimentally, namely, contact pressures, are extracted from the developed finite element models. The contact pressures are found to increase with medial compression and subglottal pressure, in agreement with expectation.
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Affiliation(s)
- Turgut Karlıdağ
- Department of Otorhinolaryngology, Fırat University School of Medicine, Elazığ, Turkey
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24
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Crowson MG, Ranisau J, Eskander A, Babier A, Xu B, Kahmke RR, Chen JM, Chan TCY. A contemporary review of machine learning in otolaryngology-head and neck surgery. Laryngoscope 2019; 130:45-51. [PMID: 30706465 DOI: 10.1002/lary.27850] [Citation(s) in RCA: 67] [Impact Index Per Article: 13.4] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/07/2018] [Accepted: 01/11/2019] [Indexed: 11/07/2022]
Abstract
One of the key challenges with big data is leveraging the complex network of information to yield useful clinical insights. The confluence of massive amounts of health data and a desire to make inferences and insights on these data has produced a substantial amount of interest in machine-learning analytic methods. There has been a drastic increase in the otolaryngology literature volume describing novel applications of machine learning within the past 5 years. In this timely contemporary review, we provide an overview of popular machine-learning techniques, and review recent machine-learning applications in otolaryngology-head and neck surgery including neurotology, head and neck oncology, laryngology, and rhinology. Investigators have realized significant success in validated models with model sensitivities and specificities approaching 100%. Challenges remain in the implementation of machine-learning algorithms. This may be in part the unfamiliarity of these techniques to clinician leaders on the front lines of patient care. Spreading awareness and confidence in machine learning will follow with further validation and proof-of-value analyses that demonstrate model performance superiority over established methods. We are poised to see a greater influx of machine-learning applications to clinical problems in otolaryngology-head and neck surgery, and it is prudent for providers to understand the potential benefits and limitations of these technologies. Laryngoscope, 130:45-51, 2020.
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Affiliation(s)
- Matthew G Crowson
- Department of Otolaryngology-Head and Neck Surgery, Sunnybrook Health Sciences Center, Toronto, Ontario, Canada
| | - Jonathan Ranisau
- Department of Mechanical and Industrial Engineering, University of Toronto, Toronto, Ontario, Canada
| | - Antoine Eskander
- Department of Otolaryngology-Head and Neck Surgery, Sunnybrook Health Sciences Center, Toronto, Ontario, Canada
- Institute for Clinical Evaluative Sciences, Toronto, Ontario, Canada
| | - Aaron Babier
- Department of Mechanical and Industrial Engineering, University of Toronto, Toronto, Ontario, Canada
| | - Bin Xu
- Department of Pathology, Sunnybrook Health Sciences Center, Toronto, Ontario, Canada
| | - Russel R Kahmke
- Division of Otolaryngology-Head and Neck Surgery, Duke University Medical Center, Durham, North Carolina, U.S.A
| | - Joseph M Chen
- Department of Otolaryngology-Head and Neck Surgery, Sunnybrook Health Sciences Center, Toronto, Ontario, Canada
| | - Timothy C Y Chan
- Department of Mechanical and Industrial Engineering, University of Toronto, Toronto, Ontario, Canada
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Laves MH, Bicker J, Kahrs LA, Ortmaier T. A dataset of laryngeal endoscopic images with comparative study on convolution neural network-based semantic segmentation. Int J Comput Assist Radiol Surg 2019; 14:483-492. [PMID: 30649670 DOI: 10.1007/s11548-018-01910-0] [Citation(s) in RCA: 43] [Impact Index Per Article: 8.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/09/2018] [Accepted: 12/28/2018] [Indexed: 11/24/2022]
Abstract
PURPOSE Automated segmentation of anatomical structures in medical image analysis is a prerequisite for autonomous diagnosis as well as various computer- and robot-aided interventions. Recent methods based on deep convolutional neural networks (CNN) have outperformed former heuristic methods. However, those methods were primarily evaluated on rigid, real-world environments. In this study, existing segmentation methods were evaluated for their use on a new dataset of transoral endoscopic exploration. METHODS Four machine learning-based methods SegNet, UNet, ENet and ErfNet were trained with supervision on a novel 7-class dataset of the human larynx. The dataset contains 536 manually segmented images from two patients during laser incisions. The Intersection-over-Union (IoU) evaluation metric was used to measure the accuracy of each method. Data augmentation and network ensembling were employed to increase segmentation accuracy. Stochastic inference was used to show uncertainties of the individual models. Patient-to-patient transfer was investigated using patient-specific fine-tuning. RESULTS In this study, a weighted average ensemble network of UNet and ErfNet was best suited for the segmentation of laryngeal soft tissue with a mean IoU of 84.7%. The highest efficiency was achieved by ENet with a mean inference time of 9.22 ms per image. It is shown that 10 additional images from a new patient are sufficient for patient-specific fine-tuning. CONCLUSION CNN-based methods for semantic segmentation are applicable to endoscopic images of laryngeal soft tissue. The segmentation can be used for active constraints or to monitor morphological changes and autonomously detect pathologies. Further improvements could be achieved by using a larger dataset or training the models in a self-supervised manner on additional unlabeled data.
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Affiliation(s)
- Max-Heinrich Laves
- Leibniz Universität Hannover, Appelstraße 11A, 30167, Hannover, Germany.
| | - Jens Bicker
- Leibniz Universität Hannover, Appelstraße 11A, 30167, Hannover, Germany
| | - Lüder A Kahrs
- Leibniz Universität Hannover, Appelstraße 11A, 30167, Hannover, Germany
| | - Tobias Ortmaier
- Leibniz Universität Hannover, Appelstraße 11A, 30167, Hannover, Germany
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Resteghini C, Trama A, Borgonovi E, Hosni H, Corrao G, Orlandi E, Calareso G, De Cecco L, Piazza C, Mainardi L, Licitra L. Big Data in Head and Neck Cancer. Curr Treat Options Oncol 2018; 19:62. [DOI: 10.1007/s11864-018-0585-2] [Citation(s) in RCA: 25] [Impact Index Per Article: 4.2] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/20/2022]
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27
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Moccia S, Vanone GO, Momi ED, Laborai A, Guastini L, Peretti G, Mattos LS. Learning-based classification of informative laryngoscopic frames. COMPUTER METHODS AND PROGRAMS IN BIOMEDICINE 2018; 158:21-30. [PMID: 29544787 DOI: 10.1016/j.cmpb.2018.01.030] [Citation(s) in RCA: 24] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 09/27/2017] [Revised: 12/18/2017] [Accepted: 01/29/2018] [Indexed: 06/08/2023]
Abstract
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 cost of some drawbacks, such as large amount of data to review to make the diagnosis. The purpose of this paper is to present a strategy to perform automatic selection of informative endoscopic video frames, which can reduce the amount of data to process and potentially increase diagnosis performance. METHODS A new method to classify NBI endoscopic frames based on intensity, keypoint and image spatial content features is proposed. Support vector machines with the radial basis function and the one-versus-one scheme are used to classify frames as informative, blurred, with saliva or specular reflections, or underexposed. RESULTS When tested on a balanced set of 720 images from 18 different laryngoscopic videos, a classification recall of 91% was achieved for informative frames, significantly overcoming three state of the art methods (Wilcoxon rank-signed test, significance level = 0.05). CONCLUSIONS Due to the high performance in identifying informative frames, the approach is a valuable tool to perform informative frame selection, which can be potentially applied in different fields, such us computer-assisted diagnosis and endoscopic view expansion.
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Affiliation(s)
- Sara Moccia
- Department of Electronics, Information, and Bioengineering, Politecnico di Milano, Milan, Italy; Department of Advanced Robotics, Istituto Italiano di Tecnologia, Genoa, Italy.
| | - Gabriele O Vanone
- Department of Electronics, Information, and Bioengineering, Politecnico di Milano, Milan, Italy
| | - Elena De Momi
- Department of Electronics, Information, and Bioengineering, Politecnico di Milano, Milan, Italy
| | - Andrea Laborai
- Department of Otorhinolaryngology, Head and Neck Surgery, University of Genoa, Genoa, Italy
| | - Luca Guastini
- Department of Otorhinolaryngology, Head and Neck Surgery, University of Genoa, Genoa, Italy
| | - Giorgio Peretti
- Department of Otorhinolaryngology, Head and Neck Surgery, University of Genoa, Genoa, Italy
| | - Leonardo S Mattos
- Department of Advanced Robotics, Istituto Italiano di Tecnologia, Genoa, Italy
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Döllinger M, Gómez P, Patel RR, Alexiou C, Bohr C, Schützenberger A. Biomechanical simulation of vocal fold dynamics in adults based on laryngeal high-speed videoendoscopy. PLoS One 2017; 12:e0187486. [PMID: 29121085 PMCID: PMC5679561 DOI: 10.1371/journal.pone.0187486] [Citation(s) in RCA: 15] [Impact Index Per Article: 2.1] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/15/2016] [Accepted: 10/18/2017] [Indexed: 12/18/2022] Open
Abstract
MOTIVATION Human voice is generated in the larynx by the two oscillating vocal folds. Owing to the limited space and accessibility of the larynx, endoscopic investigation of the actual phonatory process in detail is challenging. Hence the biomechanics of the human phonatory process are still not yet fully understood. Therefore, we adapt a mathematical model of the vocal folds towards vocal fold oscillations to quantify gender and age related differences expressed by computed biomechanical model parameters. METHODS The vocal fold dynamics are visualized by laryngeal high-speed videoendoscopy (4000 fps). A total of 33 healthy young subjects (16 females, 17 males) and 11 elderly subjects (5 females, 6 males) were recorded. A numerical two-mass model is adapted to the recorded vocal fold oscillations by varying model masses, stiffness and subglottal pressure. For adapting the model towards the recorded vocal fold dynamics, three different optimization algorithms (Nelder-Mead, Particle Swarm Optimization and Simulated Bee Colony) in combination with three cost functions were considered for applicability. Gender differences and age-related kinematic differences reflected by the model parameters were analyzed. RESULTS AND CONCLUSION The biomechanical model in combination with numerical optimization techniques allowed phonatory behavior to be simulated and laryngeal parameters involved to be quantified. All three optimization algorithms showed promising results. However, only one cost function seems to be suitable for this optimization task. The gained model parameters reflect the phonatory biomechanics for men and women well and show quantitative age- and gender-specific differences. The model parameters for younger females and males showed lower subglottal pressures, lower stiffness and higher masses than the corresponding elderly groups. Females exhibited higher subglottal pressures, smaller oscillation masses and larger stiffness than the corresponding similar aged male groups. Optimizing numerical models towards vocal fold oscillations is useful to identify underlying laryngeal components controlling the phonatory process.
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Affiliation(s)
- Michael Döllinger
- Division of Phoniatrics and Pediatric Audiology, Department of Otorhinolaryngology, Head and Neck Surgery, Medical School, Friedrich-Alexander-University Erlangen-Nürnberg, Erlangen, Germany
| | - Pablo Gómez
- Division of Phoniatrics and Pediatric Audiology, Department of Otorhinolaryngology, Head and Neck Surgery, Medical School, Friedrich-Alexander-University Erlangen-Nürnberg, Erlangen, Germany
| | - Rita R. Patel
- Department of Speech and Hearing Sciences, Indiana University, Bloomington, Indiana, Indiana, United States of America
| | - Christoph Alexiou
- Section of Experimental Oncology and Nanomedicine (SEON), Department of Otorhinolaryngology, Head and Neck Surgery, Medical School, Else Kröner-Fresenius-Stiftung-Professorship, Friedrich-Alexander-University Erlangen-Nürnberg, Erlangen, Germany
| | - Christopher Bohr
- Division of Phoniatrics and Pediatric Audiology, Department of Otorhinolaryngology, Head and Neck Surgery, Medical School, Friedrich-Alexander-University Erlangen-Nürnberg, Erlangen, Germany
| | - Anne Schützenberger
- Division of Phoniatrics and Pediatric Audiology, Department of Otorhinolaryngology, Head and Neck Surgery, Medical School, Friedrich-Alexander-University Erlangen-Nürnberg, Erlangen, Germany
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Moccia S, De Momi E, Guarnaschelli M, Savazzi M, Laborai A, Guastini L, Peretti G, Mattos LS. Confident texture-based laryngeal tissue classification for early stage diagnosis support. J Med Imaging (Bellingham) 2017; 4:034502. [PMID: 28983494 DOI: 10.1117/1.jmi.4.3.034502] [Citation(s) in RCA: 30] [Impact Index Per Article: 4.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/26/2017] [Accepted: 09/12/2017] [Indexed: 12/24/2022] Open
Abstract
Early stage diagnosis of laryngeal squamous cell carcinoma (SCC) is of primary importance for lowering patient mortality or after treatment morbidity. Despite the challenges in diagnosis reported in the clinical literature, few efforts have been invested in computer-assisted diagnosis. The objective of this paper is to investigate the use of texture-based machine-learning algorithms for early stage cancerous laryngeal tissue classification. To estimate the classification reliability, a measure of confidence is also exploited. From the endoscopic videos of 33 patients affected by SCC, a well-balanced dataset of 1320 patches, relative to four laryngeal tissue classes, was extracted. With the best performing feature, the achieved median classification recall was 93% [interquartile range [Formula: see text]]. When excluding low-confidence patches, the achieved median recall was increased to 98% ([Formula: see text]), proving the high reliability of the proposed approach. This research represents an important advancement in the state-of-the-art computer-assisted laryngeal diagnosis, and the results are a promising step toward a helpful endoscope-integrated processing system to support early stage diagnosis.
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Affiliation(s)
- Sara Moccia
- Politecnico di Milano, Department of Electronics, Information, and Bioengineering, Milan, Italy.,Istituto Italiano di Tecnologia, Department of Advanced Robotics, Genoa, Italy
| | - Elena De Momi
- Politecnico di Milano, Department of Electronics, Information, and Bioengineering, Milan, Italy
| | - Marco Guarnaschelli
- Politecnico di Milano, Department of Electronics, Information, and Bioengineering, Milan, Italy
| | - Matteo Savazzi
- Politecnico di Milano, Department of Electronics, Information, and Bioengineering, Milan, Italy
| | - Andrea Laborai
- University of Genoa, Department of Otorhinolaryngology, Head, and Neck Surgery, Genoa, Italy
| | - Luca Guastini
- University of Genoa, Department of Otorhinolaryngology, Head, and Neck Surgery, Genoa, Italy
| | - Giorgio Peretti
- University of Genoa, Department of Otorhinolaryngology, Head, and Neck Surgery, Genoa, Italy
| | - Leonardo S Mattos
- Istituto Italiano di Tecnologia, Department of Advanced Robotics, Genoa, Italy
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Evaluation of clinical value of videokymography for diagnosis and treatment of voice disorders. Eur Arch Otorhinolaryngol 2017; 274:3941-3949. [PMID: 28856469 DOI: 10.1007/s00405-017-4726-1] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/13/2017] [Accepted: 08/21/2017] [Indexed: 10/19/2022]
Abstract
This study aimed at determining the clinical value of videokymography (VKG) as an additional tool for the assessment of voice disorders. 105 subjects with voice disorders were examined by an experienced laryngologist. A questionnaire was used to specify diagnosis, diagnostic confidence, and treatment recommendations before and after VKG. The first part of questionnaire was filled by the laryngologist for each patient after routine ear-nose-throat evaluation, including stroboscopy, the second part after the subsequent VKG examination. In 31% of subjects VKG confirmed the stroboscopic diagnosis, in 44% it made the diagnosis more accurate, in 20% there was adjustment of the treatment, and in 5% it was not found diagnostically useful. After VKG the diagnostic confidence increased in 68% of the subjects. VKG may help clinicians to take some important treatment decisions and may be recommended to be performed in patients, where clinicians are uncertain about diagnosis and treatment.
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Glottal Gap tracking by a continuous background modeling using inpainting. Med Biol Eng Comput 2017; 55:2123-2141. [DOI: 10.1007/s11517-017-1652-8] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/18/2016] [Accepted: 04/19/2017] [Indexed: 11/26/2022]
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Andrade-Miranda G, Henrich Bernardoni N, Godino-Llorente JI. Synthesizing the motion of the vocal folds using optical flow based techniques. Biomed Signal Process Control 2017. [DOI: 10.1016/j.bspc.2017.01.002] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.6] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/20/2022]
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Ji Y, Li L, Tao Q, Zhang X, Luan J, Zhao S, Liu H, Ju D. Deprivation of asparagine triggers cytoprotective autophagy in laryngeal squamous cell carcinoma. Appl Microbiol Biotechnol 2017; 101:4951-4961. [DOI: 10.1007/s00253-017-8221-9] [Citation(s) in RCA: 12] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/16/2017] [Revised: 02/27/2017] [Accepted: 03/04/2017] [Indexed: 01/31/2023]
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Volgger V, Felicio A, Lohscheller J, Englhard AS, Al-Muzaini H, Betz CS, Schuster ME. Evaluation of the combined use of narrow band imaging and high-speed imaging to discriminate laryngeal lesions. Lasers Surg Med 2017; 49:609-618. [PMID: 28231400 DOI: 10.1002/lsm.22652] [Citation(s) in RCA: 14] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 02/04/2017] [Indexed: 02/05/2023]
Abstract
BACKGROUND AND OBJECTIVE Laryngeal lesions are usually investigated by microlaryngoscopy, biopsy, and histopathology. This study aimed to evaluate the combined use of Narrow Band Imaging (NBI) and High-Speed Imaging (HSI) in the differentiation of glottic lesions in awake patients. STUDY DESIGN Prospective diagnostic study. MATERIALS AND METHODS Thirty-six awake patients with 41 glottic lesions were investigated with both NBI and HSI, and the suspected diagnoses were compared to the histopathological results of tissue biopsies taken during subsequent microlaryngoscopies. Of the 41 lesions, 28 were primary lesions and 13 recurrent lesions after previous laryngeal pathologies. RESULTS Sensitivity, specificity, positive predictive value, and negative predictive value in the differentiation between benign/premalignant and malignant lesions with both NBI and HSI accounted to 100.0%, 79.4%, 50.0%, and 100.0%. Sensitivities and specificities were 100.0% and 85.7% for HSI alone, and 100.0% and 79.4% for NBI alone. Regarding only primary lesions the results were generally better with sensitivities and specificities of 100% and 81% for NBI, 100% and 84.2% for HSI and 100% and 85.7% for the combination of both methods, respectively. CONCLUSION NBI and HSI both seem to be promising adjunct tools in the differentiation of various laryngeal lesions in awake patients with high sensitivities. Specificities, however, were moderate but could be increased when using NBI and HSI in combination in a subgroup of patients with only primary lesions. Although both methods still have limitations they might ameliorate the evaluation of suspicious laryngeal lesions in the future and could possibly spare patients from repeated invasive tissue biopsies. Lasers Surg. Med. 49:609-618, 2017. © 2017 Wiley Periodicals, Inc.
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Affiliation(s)
- Veronika Volgger
- Department of Otorhinolaryngology, Head and Neck Surgery, Klinikum der Universität München, 81377, Munich, Germany
| | - Axelle Felicio
- Department of Otorhinolaryngology, Head and Neck Surgery, Klinikum der Universität München, 81377, Munich, Germany
| | - Jörg Lohscheller
- Department of Informatics, Trier University of Applied Sciences, Schneidershof, 54208, Trier, Germany
| | - Anna S Englhard
- Department of Otorhinolaryngology, Head and Neck Surgery, Klinikum der Universität München, 81377, Munich, Germany
| | - Hanan Al-Muzaini
- Department of Otorhinolaryngology, Head and Neck Surgery, Klinikum der Universität München, 81377, Munich, Germany
| | - Christian S Betz
- Department of Otorhinolaryngology, Head and Neck Surgery, Klinikum der Universität München, 81377, Munich, Germany
| | - Maria E Schuster
- Department of Otorhinolaryngology, Head and Neck Surgery, Klinikum der Universität München, 81377, Munich, Germany
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Laryngeal High-Speed Videoendoscopy: Sensitivity of Objective Parameters towards Recording Frame Rate. BIOMED RESEARCH INTERNATIONAL 2016; 2016:4575437. [PMID: 27990428 PMCID: PMC5136634 DOI: 10.1155/2016/4575437] [Citation(s) in RCA: 14] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 07/06/2016] [Accepted: 10/10/2016] [Indexed: 11/29/2022]
Abstract
The current use of laryngeal high-speed videoendoscopy in clinic settings involves subjective visual assessment of vocal fold vibratory characteristics. However, objective quantification of vocal fold vibrations for evidence-based diagnosis and therapy is desired, and objective parameters assessing laryngeal dynamics have therefore been suggested. This study investigated the sensitivity of the objective parameters and their dependence on recording frame rate. A total of 300 endoscopic high-speed videos with recording frame rates between 1000 and 15 000 fps were analyzed for a vocally healthy female subject during sustained phonation. Twenty parameters, representing laryngeal dynamics, were computed. Four different parameter characteristics were found: parameters showing no change with increasing frame rate; parameters changing up to a certain frame rate, but then remaining constant; parameters remaining constant within a particular range of recording frame rates; and parameters changing with nearly every frame rate. The results suggest that (1) parameter values are influenced by recording frame rates and different parameters have varying sensitivities to recording frame rate; (2) normative values should be determined based on recording frame rates; and (3) the typically used recording frame rate of 4000 fps seems to be too low to distinguish accurately certain characteristics of the human phonation process in detail.
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Zhou J, Zhang D, Zhou L, Yang Y, Liu F, Tao L, Lu LM. Association of the recurrence and canceration rate of vocal leukoplakia with interleukin-10 promoter variants over a 2-year period. Acta Otolaryngol 2016; 136:1147-1153. [PMID: 27299734 DOI: 10.1080/00016489.2016.1193893] [Citation(s) in RCA: 7] [Impact Index Per Article: 0.9] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/21/2022]
Abstract
Conclusion This study indicates that IL-10 promoter polymorphism variants, smoking, and alcohol consumption increase the risk of recurrence and canceration in vocal leukoplakia. Objective This prospective, clinical trial was performed to evaluate the association of interleukin (IL)-10 promoter polymorphism variants and canceration and recurrence rates in vocal leukoplakia (a pre-cancerous laryngeal carcinoma lesion) over a 2-year period. Participants and method Sixty-one post-operative patients with vocal leukoplakia were enrolled in this prospective, observational study and genotyped for the IL-10 promoter gene (IL-10-1082 A/G, -819 T/C and -592 A/C) using pyrosequencing, and responded to a 2-year follow-up survey. Recurrence and canceration rates were used to evaluate the association between the genotype variants and the clinical outcome. Results There was an increased canceration rate in the variant genotype group compared to that in the normal genotype group in the 2-year follow-up period (18.4% vs 0%, p-value = 0.038). Compared with the non-smoker group, the smoker group had a higher recurrence rate of vocal leukoplakia (29.3% vs 5%, p-value =0.044). Likewise, the recurrence rate in the alcohol consumption group was also higher (30.6% vs 8%, p-value =0.034). The percentage of cancerization in the alcohol consumption group was significantly higher than that in the non-alcohol consumption group (19.4% vs 0%, p-value =0.035).
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Herbst CT, Unger J, Herzel H, Švec JG, Lohscheller J. Phasegram Analysis of Vocal Fold Vibration Documented With Laryngeal High-speed Video Endoscopy. J Voice 2016; 30:771.e1-771.e15. [DOI: 10.1016/j.jvoice.2015.11.006] [Citation(s) in RCA: 10] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/21/2015] [Accepted: 11/12/2015] [Indexed: 11/16/2022]
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In vivo cross-sectional imaging of the phonating larynx using long-range Doppler optical coherence tomography. Sci Rep 2016; 6:22792. [PMID: 26960250 PMCID: PMC4785353 DOI: 10.1038/srep22792] [Citation(s) in RCA: 21] [Impact Index Per Article: 2.6] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/09/2015] [Accepted: 01/28/2016] [Indexed: 12/04/2022] Open
Abstract
Diagnosis and treatment of vocal fold lesions has been a long-evolving science for the otolaryngologist. Contemporary practice requires biopsy of a glottal lesion in the operating room under general anesthesia for diagnosis. Current in-office technology is limited to visualizing the surface of the vocal folds with fiber-optic or rigid endoscopy and using stroboscopic or high-speed video to infer information about submucosal processes. Previous efforts using optical coherence tomography (OCT) have been limited by small working distances and imaging ranges. Here we report the first full field, high-speed, and long-range OCT images of awake patients’ vocal folds as well as cross-sectional video and Doppler analysis of their vocal fold motions during phonation. These vertical-cavity surface-emitting laser source (VCSEL) OCT images offer depth resolved, high-resolution, high-speed, and panoramic images of both the true and false vocal folds. This technology has the potential to revolutionize in-office imaging of the larynx.
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Betz CS, Kraft M, Arens C, Schuster M, Pfeffer C, Rühm A, Stepp H, Englhard A, Volgger V. Optische Diagnoseverfahren zur Tumorfrühdiagnostik im oberen Luft-Speise-Weg. HNO 2015; 64:41-8. [DOI: 10.1007/s00106-015-0104-8] [Citation(s) in RCA: 5] [Impact Index Per Article: 0.6] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/22/2022]
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Unger J, Schuster M, Hecker DJ, Schick B, Lohscheller J. A generalized procedure for analyzing sustained and dynamic vocal fold vibrations from laryngeal high-speed videos using phonovibrograms. Artif Intell Med 2015; 66:15-28. [PMID: 26597002 DOI: 10.1016/j.artmed.2015.10.002] [Citation(s) in RCA: 12] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/16/2015] [Revised: 09/28/2015] [Accepted: 10/20/2015] [Indexed: 12/01/2022]
Abstract
OBJECTIVE This work presents a computer-based approach to analyze the two-dimensional vocal fold dynamics of endoscopic high-speed videos, and constitutes an extension and generalization of a previously proposed wavelet-based procedure. While most approaches aim for analyzing sustained phonation conditions, the proposed method allows for a clinically adequate analysis of both dynamic as well as sustained phonation paradigms. MATERIALS AND METHODS The analysis procedure is based on a spatio-temporal visualization technique, the phonovibrogram, that facilitates the documentation of the visible laryngeal dynamics. From the phonovibrogram, a low-dimensional set of features is computed using a principle component analysis strategy that quantifies the type of vibration patterns, irregularity, lateral symmetry and synchronicity, as a function of time. Two different test bench data sets are used to validate the approach: (I) 150 healthy and pathologic subjects examined during sustained phonation. (II) 20 healthy and pathologic subjects that were examined twice: during sustained phonation and a glissando from a low to a higher fundamental frequency. In order to assess the discriminative power of the extracted features, a Support Vector Machine is trained to distinguish between physiologic and pathologic vibrations. The results for sustained phonation sequences are compared to the previous approach. Finally, the classification performance of the stationary analyzing procedure is compared to the transient analysis of the glissando maneuver. RESULTS For the first test bench the proposed procedure outperformed the previous approach (proposed feature set: accuracy: 91.3%, sensitivity: 80%, specificity: 97%, previous approach: accuracy: 89.3%, sensitivity: 76%, specificity: 96%). Comparing the classification performance of the second test bench further corroborates that analyzing transient paradigms provides clear additional diagnostic value (glissando maneuver: accuracy: 90%, sensitivity: 100%, specificity: 80%, sustained phonation: accuracy: 75%, sensitivity: 80%, specificity: 70%). CONCLUSIONS The incorporation of parameters describing the temporal evolvement of vocal fold vibration clearly improves the automatic identification of pathologic vibration patterns. Furthermore, incorporating a dynamic phonation paradigm provides additional valuable information about the underlying laryngeal dynamics that cannot be derived from sustained conditions. The proposed generalized approach provides a better overall classification performance than the previous approach, and hence constitutes a new advantageous tool for an improved clinical diagnosis of voice disorders.
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Affiliation(s)
- Jakob Unger
- Department of Computer Science, Trier University of Applied Sciences, Schneidershof, 54293 Trier, Germany.
| | - Maria Schuster
- Department of Otorhinolaryngology and Head and Neck Surgery, University of Munich, Campus Grosshadern, Marchioninistr. 13, 81366 München, Germany
| | - Dietmar J Hecker
- Department of Otorhinolaryngology, Saarland University Hospital, Kirrbergerstr., 66424 Homburg/Saar, Germany
| | - Bernhard Schick
- Department of Otorhinolaryngology, Saarland University Hospital, Kirrbergerstr., 66424 Homburg/Saar, Germany
| | - Jörg Lohscheller
- Department of Computer Science, Trier University of Applied Sciences, Schneidershof, 54293 Trier, Germany
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