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Nobel SMN, Swapno SMMR, Islam MR, Safran M, Alfarhood S, Mridha MF. A machine learning approach for vocal fold segmentation and disorder classification based on ensemble method. Sci Rep 2024; 14:14435. [PMID: 38910146 DOI: 10.1038/s41598-024-64987-5] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/02/2024] [Accepted: 06/14/2024] [Indexed: 06/25/2024] Open
Abstract
In the healthcare domain, the essential task is to understand and classify diseases affecting the vocal folds (VFs). The accurate identification of VF disease is the key issue in this domain. Integrating VF segmentation and disease classification into a single system is challenging but important for precise diagnostics. Our study addresses this challenge by combining VF illness categorization and VF segmentation into a single integrated system. We utilized two effective ensemble machine learning methods: ensemble EfficientNetV2L-LGBM and ensemble UNet-BiGRU. We utilized the EfficientNetV2L-LGBM model for classification, achieving a training accuracy of 98.88%, validation accuracy of 97.73%, and test accuracy of 97.88%. These exceptional outcomes highlight the system's ability to classify different VF illnesses precisely. In addition, we utilized the UNet-BiGRU model for segmentation, which attained a training accuracy of 92.55%, a validation accuracy of 89.87%, and a significant test accuracy of 91.47%. In the segmentation task, we examined some methods to improve our ability to divide data into segments, resulting in a testing accuracy score of 91.99% and an Intersection over Union (IOU) of 87.46%. These measures demonstrate skill of the model in accurately defining and separating VF. Our system's classification and segmentation results confirm its capacity to effectively identify and segment VF disorders, representing a significant advancement in enhancing diagnostic accuracy and healthcare in this specialized field. This study emphasizes the potential of machine learning to transform the medical field's capacity to categorize VF and segment VF, providing clinicians with a vital instrument to mitigate the profound impact of the condition. Implementing this innovative approach is expected to enhance medical procedures and provide a sense of optimism to those globally affected by VF disease.
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Affiliation(s)
- S M Nuruzzaman Nobel
- Department of Computer Science and Engineering, Bangladesh University of Business and Technology, Dhaka, 1216, Bangladesh
| | - S M Masfequier Rahman Swapno
- Department of Computer Science and Engineering, Bangladesh University of Business and Technology, Dhaka, 1216, Bangladesh
| | - Md Rajibul Islam
- Department of Electrical and Electronic Engineering, The Hong Kong Polytechnic University, Hong Kong, China
| | - Mejdl Safran
- Department of Computer Science, College of Computer and Information Sciences, King Saud University, P. O. Box 51178, 11543, Riyadh, Saudi Arabia.
| | - Sultan Alfarhood
- Department of Computer Science, College of Computer and Information Sciences, King Saud University, P. O. Box 51178, 11543, Riyadh, Saudi Arabia
| | - M F Mridha
- Department of Computer Science, American International University-Bangladesh, Dhaka, 1229, Bangladesh
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Barlow J, Sragi Z, Rivera-Rivera G, Al-Awady A, Daşdöğen Ü, Courey MS, Kirke DN. The Use of Deep Learning Software in the Detection of Voice Disorders: A Systematic Review. Otolaryngol Head Neck Surg 2024; 170:1531-1543. [PMID: 38168017 DOI: 10.1002/ohn.636] [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: 07/07/2023] [Revised: 11/30/2023] [Accepted: 12/07/2023] [Indexed: 01/05/2024]
Abstract
OBJECTIVE To summarize the use of deep learning in the detection of voice disorders using acoustic and laryngoscopic input, compare specific neural networks in terms of accuracy, and assess their effectiveness compared to expert clinical visual examination. DATA SOURCES Embase, MEDLINE, and Cochrane Central. REVIEW METHODS Databases were screened through November 11, 2023 for relevant studies. The inclusion criteria required studies to utilize a specified deep learning method, use laryngoscopy or acoustic input, and measure accuracy of binary classification between healthy patients and those with voice disorders. RESULTS Thirty-four studies met the inclusion criteria, with 18 focusing on voice analysis, 15 on imaging analysis, and 1 both. Across the 18 acoustic studies, 21 programs were used for identification of organic and functional voice disorders. These technologies included 10 convolutional neural networks (CNNs), 6 multilayer perceptrons (MLPs), and 5 other neural networks. The binary classification systems yielded a mean accuracy of 89.0% overall, including 93.7% for MLP programs and 84.5% for CNNs. Among the 15 imaging analysis studies, a total of 23 programs were utilized, resulting in a mean accuracy of 91.3%. Specifically, the twenty CNNs achieved a mean accuracy of 92.6% compared to 83.0% for the 3 MLPs. CONCLUSION Deep learning models were shown to be highly accurate in the detection of voice pathology, with CNNs most effective for assessing laryngoscopy images and MLPs most effective for assessing acoustic input. While deep learning methods outperformed expert clinical exam in limited comparisons, further studies integrating external validation are necessary.
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Affiliation(s)
- Joshua Barlow
- Department of Otolaryngology-Head and Neck Surgery, Icahn School of Medicine at Mount Sinai, New York City, New York, USA
| | - Zara Sragi
- Department of Otolaryngology-Head and Neck Surgery, Icahn School of Medicine at Mount Sinai, New York City, New York, USA
| | - Gabriel Rivera-Rivera
- Department of Otolaryngology-Head and Neck Surgery, Icahn School of Medicine at Mount Sinai, New York City, New York, USA
| | - Abdurrahman Al-Awady
- Department of Otolaryngology-Head and Neck Surgery, Icahn School of Medicine at Mount Sinai, New York City, New York, USA
| | - Ümit Daşdöğen
- Department of Otolaryngology-Head and Neck Surgery, Icahn School of Medicine at Mount Sinai, New York City, New York, USA
| | - Mark S Courey
- Department of Otolaryngology-Head and Neck Surgery, Icahn School of Medicine at Mount Sinai, New York City, New York, USA
| | - Diana N Kirke
- Department of Otolaryngology-Head and Neck Surgery, Icahn School of Medicine at Mount Sinai, New York City, New York, USA
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Taciuc IA, Dumitru M, Vrinceanu D, Gherghe M, Manole F, Marinescu A, Serboiu C, Neagos A, Costache A. Applications and challenges of neural networks in otolaryngology (Review). Biomed Rep 2024; 20:92. [PMID: 38765859 PMCID: PMC11099604 DOI: 10.3892/br.2024.1781] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/28/2024] [Accepted: 04/05/2024] [Indexed: 05/22/2024] Open
Abstract
Artificial Intelligence (AI) has become a topic of interest that is frequently debated in all research fields. The medical field is no exception, where several unanswered questions remain. When and how this field can benefit from AI support in daily routines are the most frequently asked questions. The present review aims to present the types of neural networks (NNs) available for development, discussing their advantages, disadvantages and how they can be applied practically. In addition, the present review summarizes how NNs (combined with various other features) have already been applied in studies in the ear nose throat research field, from assisting diagnosis to treatment management. Although the answer to this question regarding AI remains elusive, understanding the basics and types of applicable NNs can lead to future studies possibly using more than one type of NN. This approach may bypass the actual limitations in accuracy and relevance of information generated by AI. The proposed studies, the majority of which used convolutional NNs, obtained accuracies varying 70-98%, with a number of studies having the AI trained on a limited number of cases (<100 patients). The lack of standardization in AI protocols for research negatively affects data homogeneity and transparency of databases.
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Affiliation(s)
- Iulian-Alexandru Taciuc
- Department of Pathology, ‘Carol Davila’ University of Medicine and Pharmacy, 020021 Bucharest, Romania
| | - Mihai Dumitru
- Department of ENT, ‘Carol Davila’ University of Medicine and Pharmacy, 050751 Bucharest, Romania
| | - Daniela Vrinceanu
- Department of ENT, ‘Carol Davila’ University of Medicine and Pharmacy, 050751 Bucharest, Romania
| | - Mirela Gherghe
- Department of Nuclear Medicine, ‘Carol Davila’ University of Medicine and Pharmacy, 022328 Bucharest, Romania
| | - Felicia Manole
- Department of ENT, Faculty of Medicine University of Oradea, 410073 Oradea, Romania
| | - Andreea Marinescu
- Department of Radiology and Medical Imaging ‘Carol Davila’ University of Medicine and Pharmacy, 050096 Bucharest, Romania
| | - Crenguta Serboiu
- Department of Cell Biology, Molecular and Histology, ‘Carol Davila’ University of Medicine and Pharmacy, 050096 Bucharest, Romania
| | - Adriana Neagos
- Department of ENT, ‘George Emil Palade’ University of Medicine, Pharmacy, Science, and Technology of Targu Mures, 540142 Mures, Romania
| | - Adrian Costache
- Department of Pathology, ‘Carol Davila’ University of Medicine and Pharmacy, 020021 Bucharest, Romania
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Tie CW, Li DY, Zhu JQ, Wang ML, Wang JH, Chen BH, Li Y, Zhang S, Liu L, Guo L, Yang L, Yang LQ, Wei J, Jiang F, Zhao ZQ, Wang GQ, Zhang W, Zhang QM, Ni XG. Multi-Instance Learning for Vocal Fold Leukoplakia Diagnosis Using White Light and Narrow-Band Imaging: A Multicenter Study. Laryngoscope 2024. [PMID: 38801129 DOI: 10.1002/lary.31537] [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] [Received: 02/29/2024] [Revised: 05/01/2024] [Accepted: 05/09/2024] [Indexed: 05/29/2024]
Abstract
OBJECTIVES Vocal fold leukoplakia (VFL) is a precancerous lesion of laryngeal cancer, and its endoscopic diagnosis poses challenges. We aim to develop an artificial intelligence (AI) model using white light imaging (WLI) and narrow-band imaging (NBI) to distinguish benign from malignant VFL. METHODS A total of 7057 images from 426 patients were used for model development and internal validation. Additionally, 1617 images from two other hospitals were used for model external validation. Modeling learning based on WLI and NBI modalities was conducted using deep learning combined with a multi-instance learning approach (MIL). Furthermore, 50 prospectively collected videos were used to evaluate real-time model performance. A human-machine comparison involving 100 patients and 12 laryngologists assessed the real-world effectiveness of the model. RESULTS The model achieved the highest area under the receiver operating characteristic curve (AUC) values of 0.868 and 0.884 in the internal and external validation sets, respectively. AUC in the video validation set was 0.825 (95% CI: 0.704-0.946). In the human-machine comparison, AI significantly improved AUC and accuracy for all laryngologists (p < 0.05). With the assistance of AI, the diagnostic abilities and consistency of all laryngologists improved. CONCLUSIONS Our multicenter study developed an effective AI model using MIL and fusion of WLI and NBI images for VFL diagnosis, particularly aiding junior laryngologists. However, further optimization and validation are necessary to fully assess its potential impact in clinical settings. LEVEL OF EVIDENCE 3 Laryngoscope, 2024.
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Affiliation(s)
- Cheng-Wei Tie
- Department of Endoscopy, National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China
| | - De-Yang Li
- The First Affiliated Hospital of Harbin Medical University, Harbin, China
| | - Ji-Qing Zhu
- Department of Endoscopy, National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China
| | - Mei-Ling Wang
- Department of Endoscopy, National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital & Shenzhen Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Shenzhen, China
| | - Jian-Hui Wang
- Department of Endoscopy, Shanxi Province Cancer Hospital/Shanxi Hospital Affiliated to Cancer Hospital, Chinese Academy of Medical Sciences/Cancer Hospital Affiliated to Shanxi Medical University, Taiyuan, China
| | - Bing-Hong Chen
- Department of Endoscopy, National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital & Shenzhen Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Shenzhen, China
| | - Ying Li
- Department of Endoscopy, National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital & Shenzhen Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Shenzhen, China
| | - Sen Zhang
- Department of Otolaryngology Head and Neck Surgery, The First Hospital, Shanxi Medical University, Taiyuan, China
| | - Lin Liu
- Department of Otolaryngology Head and Neck Surgery, Dalian Friendship Hospital, Dalian, China
| | - Li Guo
- Department of Otolaryngology Head and Neck Surgery, The First Affiliated Hospital, College of Clinical Medicine of Henan University of Science and Technology, Luoyang, China
| | - Long Yang
- Department of Otolaryngology, The Second People's Hospital of Baoshan City, Baoshan, China
| | - Li-Qun Yang
- Department of Otolaryngology, The Second People's Hospital of Baoshan City, Baoshan, China
| | - Jiao Wei
- Department of Otolaryngology, Qujing Second People's Hospital of Yunnan Province, Qujing, China
| | - Feng Jiang
- Department of Otolaryngology, Kunming First People's Hospital, Kunming, China
| | - Zhi-Qiang Zhao
- Department of Otolaryngology, Baoshan People's Hospital, Baoshan, China
| | - Gui-Qi Wang
- Department of Endoscopy, National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China
| | - Wei Zhang
- Department of Endoscopy, National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital & Shenzhen Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Shenzhen, China
| | - Quan-Mao Zhang
- Department of Endoscopy, Shanxi Province Cancer Hospital/Shanxi Hospital Affiliated to Cancer Hospital, Chinese Academy of Medical Sciences/Cancer Hospital Affiliated to Shanxi Medical University, Taiyuan, China
| | - Xiao-Guang Ni
- Department of Endoscopy, National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China
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You Z, Han B, Shi Z, Zhao M, Du S, Yan J, Liu H, Hei X, Ren X, Yan Y. Vocal cord leukoplakia classification using deep learning models in white light and narrow band imaging endoscopy images. Head Neck 2023; 45:3129-3145. [PMID: 37837264 DOI: 10.1002/hed.27543] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/05/2023] [Revised: 09/15/2023] [Accepted: 09/29/2023] [Indexed: 10/15/2023] Open
Abstract
BACKGROUND Accurate vocal cord leukoplakia classification is critical for the individualized treatment and early detection of laryngeal cancer. Numerous deep learning techniques have been proposed, but it is unclear how to select one to apply in the laryngeal tasks. This article introduces and reliably evaluates existing deep learning models for vocal cord leukoplakia classification. METHODS We created white light and narrow band imaging (NBI) image datasets of vocal cord leukoplakia which were classified into six classes: normal tissues (NT), inflammatory keratosis (IK), mild dysplasia (MiD), moderate dysplasia (MoD), severe dysplasia (SD), and squamous cell carcinoma (SCC). Vocal cord leukoplakia classification was performed using six classical deep learning models, AlexNet, VGG, Google Inception, ResNet, DenseNet, and Vision Transformer. RESULTS GoogLeNet (i.e., Google Inception V1), DenseNet-121, and ResNet-152 perform excellent classification. The highest overall accuracy of white light image classification is 0.9583, while the highest overall accuracy of NBI image classification is 0.9478. These three neural networks all provide very high sensitivity, specificity, and precision values. CONCLUSION GoogLeNet, ResNet, and DenseNet can provide accurate pathological classification of vocal cord leukoplakia. It facilitates early diagnosis, providing judgment on conservative treatment or surgical treatment of different degrees, and reducing the burden on endoscopists.
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Affiliation(s)
- Zhenzhen You
- Shaanxi Key Laboratory for Network Computing and Security Technology, School of Computer Science and Engineering, Xi'an University of Technology, Xi'an, China
| | - Botao Han
- Shaanxi Key Laboratory for Network Computing and Security Technology, School of Computer Science and Engineering, Xi'an University of Technology, Xi'an, China
| | - Zhenghao Shi
- Shaanxi Key Laboratory for Network Computing and Security Technology, School of Computer Science and Engineering, Xi'an University of Technology, Xi'an, China
| | - Minghua Zhao
- Shaanxi Key Laboratory for Network Computing and Security Technology, School of Computer Science and Engineering, Xi'an University of Technology, Xi'an, China
| | - Shuangli Du
- Shaanxi Key Laboratory for Network Computing and Security Technology, School of Computer Science and Engineering, Xi'an University of Technology, Xi'an, China
| | - Jing Yan
- Department of Otorhinolaryngology, Second Affiliated Hospital of Medical College, Xi'an Jiaotong University, Xi'an, China
| | - Haiqin Liu
- Department of Otorhinolaryngology, Second Affiliated Hospital of Medical College, Xi'an Jiaotong University, Xi'an, China
| | - Xinhong Hei
- Shaanxi Key Laboratory for Network Computing and Security Technology, School of Computer Science and Engineering, Xi'an University of Technology, Xi'an, China
| | - Xiaoyong Ren
- Department of Otorhinolaryngology, Second Affiliated Hospital of Medical College, Xi'an Jiaotong University, Xi'an, China
| | - Yan Yan
- Department of Otorhinolaryngology, Second Affiliated Hospital of Medical College, Xi'an Jiaotong University, Xi'an, China
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Tsilivigkos C, Athanasopoulos M, Micco RD, Giotakis A, Mastronikolis NS, Mulita F, Verras GI, Maroulis I, Giotakis E. Deep Learning Techniques and Imaging in Otorhinolaryngology-A State-of-the-Art Review. J Clin Med 2023; 12:6973. [PMID: 38002588 PMCID: PMC10672270 DOI: 10.3390/jcm12226973] [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: 10/14/2023] [Revised: 11/02/2023] [Accepted: 11/06/2023] [Indexed: 11/26/2023] Open
Abstract
Over the last decades, the field of medicine has witnessed significant progress in artificial intelligence (AI), the Internet of Medical Things (IoMT), and deep learning (DL) systems. Otorhinolaryngology, and imaging in its various subspecialties, has not remained untouched by this transformative trend. As the medical landscape evolves, the integration of these technologies becomes imperative in augmenting patient care, fostering innovation, and actively participating in the ever-evolving synergy between computer vision techniques in otorhinolaryngology and AI. To that end, we conducted a thorough search on MEDLINE for papers published until June 2023, utilizing the keywords 'otorhinolaryngology', 'imaging', 'computer vision', 'artificial intelligence', and 'deep learning', and at the same time conducted manual searching in the references section of the articles included in our manuscript. Our search culminated in the retrieval of 121 related articles, which were subsequently subdivided into the following categories: imaging in head and neck, otology, and rhinology. Our objective is to provide a comprehensive introduction to this burgeoning field, tailored for both experienced specialists and aspiring residents in the domain of deep learning algorithms in imaging techniques in otorhinolaryngology.
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Affiliation(s)
- Christos Tsilivigkos
- 1st Department of Otolaryngology, National and Kapodistrian University of Athens, Hippocrateion Hospital, 115 27 Athens, Greece; (A.G.); (E.G.)
| | - Michail Athanasopoulos
- Department of Otolaryngology, University Hospital of Patras, 265 04 Patras, Greece; (M.A.); (N.S.M.)
| | - Riccardo di Micco
- Department of Otolaryngology and Head and Neck Surgery, Medical School of Hannover, 30625 Hannover, Germany;
| | - Aris Giotakis
- 1st Department of Otolaryngology, National and Kapodistrian University of Athens, Hippocrateion Hospital, 115 27 Athens, Greece; (A.G.); (E.G.)
| | - Nicholas S. Mastronikolis
- Department of Otolaryngology, University Hospital of Patras, 265 04 Patras, Greece; (M.A.); (N.S.M.)
| | - Francesk Mulita
- Department of Surgery, University Hospital of Patras, 265 04 Patras, Greece; (G.-I.V.); (I.M.)
| | - Georgios-Ioannis Verras
- Department of Surgery, University Hospital of Patras, 265 04 Patras, Greece; (G.-I.V.); (I.M.)
| | - Ioannis Maroulis
- Department of Surgery, University Hospital of Patras, 265 04 Patras, Greece; (G.-I.V.); (I.M.)
| | - Evangelos Giotakis
- 1st Department of Otolaryngology, National and Kapodistrian University of Athens, Hippocrateion Hospital, 115 27 Athens, Greece; (A.G.); (E.G.)
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Sampieri C, Baldini C, Azam MA, Moccia S, Mattos LS, Vilaseca I, Peretti G, Ioppi A. Artificial Intelligence for Upper Aerodigestive Tract Endoscopy and Laryngoscopy: A Guide for Physicians and State-of-the-Art Review. Otolaryngol Head Neck Surg 2023; 169:811-829. [PMID: 37051892 DOI: 10.1002/ohn.343] [Citation(s) in RCA: 9] [Impact Index Per Article: 9.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/29/2022] [Revised: 03/03/2023] [Accepted: 03/23/2023] [Indexed: 04/14/2023]
Abstract
OBJECTIVE The endoscopic and laryngoscopic examination is paramount for laryngeal, oropharyngeal, nasopharyngeal, nasal, and oral cavity benign lesions and cancer evaluation. Nevertheless, upper aerodigestive tract (UADT) endoscopy is intrinsically operator-dependent and lacks objective quality standards. At present, there has been an increased interest in artificial intelligence (AI) applications in this area to support physicians during the examination, thus enhancing diagnostic performances. The relative novelty of this research field poses a challenge both for the reviewers and readers as clinicians often lack a specific technical background. DATA SOURCES Four bibliographic databases were searched: PubMed, EMBASE, Cochrane, and Google Scholar. REVIEW METHODS A structured review of the current literature (up to September 2022) was performed. Search terms related to topics of AI, machine learning (ML), and deep learning (DL) in UADT endoscopy and laryngoscopy were identified and queried by 3 independent reviewers. Citations of selected studies were also evaluated to ensure comprehensiveness. CONCLUSIONS Forty-one studies were included in the review. AI and computer vision techniques were used to achieve 3 fundamental tasks in this field: classification, detection, and segmentation. All papers were summarized and reviewed. IMPLICATIONS FOR PRACTICE This article comprehensively reviews the latest developments in the application of ML and DL in UADT endoscopy and laryngoscopy, as well as their future clinical implications. The technical basis of AI is also explained, providing guidance for nonexpert readers to allow critical appraisal of the evaluation metrics and the most relevant quality requirements.
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Affiliation(s)
- Claudio Sampieri
- Department of Experimental Medicine (DIMES), University of Genoa, Genoa, Italy
- Functional Unit of Head and Neck Tumors, Hospital Clínic, Barcelona, Spain
- Otorhinolaryngology Department, Hospital Clínic, Barcelona, Spain
| | - Chiara Baldini
- Department of Advanced Robotics, Istituto Italiano di Tecnologia, Genoa, Italy
- Dipartimento di Informatica, Bioingegneria, Robotica e Ingegneria dei Sistemi (DIBRIS), University of Genoa, Genoa, Italy
| | - Muhammad Adeel Azam
- Department of Advanced Robotics, Istituto Italiano di Tecnologia, Genoa, Italy
- Dipartimento di Informatica, Bioingegneria, Robotica e Ingegneria dei Sistemi (DIBRIS), University of Genoa, Genoa, Italy
| | - Sara Moccia
- Department of Excellence in Robotics and AI, The BioRobotics Institute, Pisa, Italy
| | - Leonardo S Mattos
- Department of Advanced Robotics, Istituto Italiano di Tecnologia, Genoa, Italy
| | - Isabel Vilaseca
- Functional Unit of Head and Neck Tumors, Hospital Clínic, Barcelona, Spain
- Otorhinolaryngology Department, Hospital Clínic, Barcelona, Spain
- Head Neck Clínic, Agència de Gestió d'Ajuts Universitaris i de Recerca, Barcelona, Catalunya, Spain
- Surgery and Medical-Surgical Specialties Department, Faculty of Medicine and Health Sciences, Universitat de Barcelona, Barcelona, Spain
- Translational Genomics and Target Therapies in Solid Tumors Group, Faculty of Medicine, Institut d́Investigacions Biomèdiques August Pi i Sunyer (IDIBAPS), Barcelona, Spain
- University of Barcelona, Barcelona, Spain
| | - Giorgio Peretti
- Unit of Otorhinolaryngology-Head and Neck Surgery, IRCCS Ospedale Policlinico San Martino, Genoa, Italy
- Department of Surgical Sciences and Integrated Diagnostics (DISC), University of Genoa, Genoa, Italy
| | - Alessandro Ioppi
- Unit of Otorhinolaryngology-Head and Neck Surgery, IRCCS Ospedale Policlinico San Martino, Genoa, Italy
- Department of Surgical Sciences and Integrated Diagnostics (DISC), University of Genoa, Genoa, Italy
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Kim GH, Hwang YJ, Lee H, Sung ES, Nam KW. Convolutional neural network-based vocal cord tumor classification technique for home-based self-prescreening purpose. Biomed Eng Online 2023; 22:81. [PMID: 37596652 PMCID: PMC10439563 DOI: 10.1186/s12938-023-01139-2] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/22/2022] [Accepted: 07/20/2023] [Indexed: 08/20/2023] Open
Abstract
BACKGROUND In this study, we proposed a deep learning technique that can simultaneously detect suspicious positions of benign vocal cord tumors in laparoscopic images and classify the types of tumors into cysts, granulomas, leukoplakia, nodules and polyps. This technique is useful for simplified home-based self-prescreening purposes to detect the generation of tumors around the vocal cord early in the benign stage. RESULTS We implemented four convolutional neural network (CNN) models (two Mask R-CNNs, Yolo V4, and a single-shot detector) that were trained, validated and tested using 2183 laryngoscopic images. The experimental results demonstrated that among the four applied models, Yolo V4 showed the highest F1-score for all tumor types (0.7664, cyst; 0.9875, granuloma; 0.8214, leukoplakia; 0.8119, nodule; and 0.8271, polyp). The model with the lowest false-negative rate was different for each tumor type (Yolo V4 for cysts/granulomas and Mask R-CNN for leukoplakia/nodules/polyps). In addition, the embedded-operated Yolo V4 model showed an approximately equivalent F1-score (0.8529) to that of the computer-operated Yolo-4 model (0.8683). CONCLUSIONS Based on these results, we conclude that the proposed deep-learning-based home screening techniques have the potential to aid in the early detection of tumors around the vocal cord and can improve the long-term survival of patients with vocal cord tumors.
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Affiliation(s)
- Gun Ho Kim
- Medical Research Institute, Pusan National University, Yangsan, Korea
- Department of Biomedical Engineering, Pusan National University Yangsan Hospital, Yangsan, Korea
| | - Young Jun Hwang
- Department of Biomedical Engineering, School of Medicine, Pusan National University, 49, Busandaehak-Ro, Mulgeum-Eup, Yangsan, 50629 Korea
| | - Hongje Lee
- Department of Nuclear Medicine, Dongnam Institute of Radiological & Medical Sciences, Busan, Korea
| | - Eui-Suk Sung
- Department of Otolaryngology-Head and Neck Surgery, Pusan National University Yangsan Hospital, Yangsan, Korea
- Department of Otolaryngology-Head and Neck Surgery, School of Medicine, Pusan National University, Yangsan, Korea
- Research Institute for Convergence of Biomedical Science and Technology, Pusan National University Yangsan Hospital, Yangsan, Korea
| | - Kyoung Won Nam
- Department of Biomedical Engineering, Pusan National University Yangsan Hospital, Yangsan, Korea
- Department of Biomedical Engineering, School of Medicine, Pusan National University, 49, Busandaehak-Ro, Mulgeum-Eup, Yangsan, 50629 Korea
- Research Institute for Convergence of Biomedical Science and Technology, Pusan National University Yangsan Hospital, Yangsan, Korea
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Zheng Z, Wang Y, Ni N, Tong G, Cheng N, Yin P, Chen Y, Wu Y, Xie G, Yang T. Deep Learning-Based Quantitative Blastocyst Assessment. 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: 38083389 DOI: 10.1109/embc40787.2023.10340963] [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
Selecting the single best blastocyst based on morphological appearance for implantation is a crucial part of in vitro fertilization (IVF). Various deep learning and computer vision-based methods have recently been applied for assessing blastocyst quality. However, to the best of our knowledge, most previous works utilize classification networks to give a qualitative evaluation. It would be challenging to rank blastocyst quality with the same qualitative result. Thus, this paper proposes a regression network combined with a soft attention mechanism for quantitatively evaluating blastocyst quality. The network outputs a continuous score to represent blastocyst quality precisely rather than some categories. As to the soft attention mechanism, the attention module in the network outputs an activation map (attention map) localizing the regions of interest (ROI, i.e., inner cell mass (ICM)) of microscopic blastocyst images. The generated activation map guides the entire network to predict ICM quality more accurately. The experimental results demonstrate that the proposed method is superior to traditional classification-based networks. Moreover, the visualized activation map makes the proposed network decision more reliable.
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Paderno A, Gennarini F, Sordi A, Montenegro C, Lancini D, Villani FP, Moccia S, Piazza C. Artificial intelligence in clinical endoscopy: Insights in the field of videomics. Front Surg 2022; 9:933297. [PMID: 36171813 PMCID: PMC9510389 DOI: 10.3389/fsurg.2022.933297] [Citation(s) in RCA: 8] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/30/2022] [Accepted: 08/22/2022] [Indexed: 11/13/2022] Open
Abstract
Artificial intelligence is being increasingly seen as a useful tool in medicine. Specifically, these technologies have the objective to extract insights from complex datasets that cannot easily be analyzed by conventional statistical methods. While promising results have been obtained for various -omics datasets, radiological images, and histopathologic slides, analysis of videoendoscopic frames still represents a major challenge. In this context, videomics represents a burgeoning field wherein several methods of computer vision are systematically used to organize unstructured data from frames obtained during diagnostic videoendoscopy. Recent studies have focused on five broad tasks with increasing complexity: quality assessment of endoscopic images, classification of pathologic and nonpathologic frames, detection of lesions inside frames, segmentation of pathologic lesions, and in-depth characterization of neoplastic lesions. Herein, we present a broad overview of the field, with a focus on conceptual key points and future perspectives.
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Affiliation(s)
- Alberto Paderno
- Unit of Otorhinolaryngology—Head and Neck Surgery, ASST Spedali Civili of Brescia, Brescia, Italy
- Department of Medical and Surgical Specialties, Radiological Sciences, and Public Health, School of Medicine, University of Brescia, Brescia, Italy
- Correspondence: Alberto Paderno
| | - Francesca Gennarini
- Unit of Otorhinolaryngology—Head and Neck Surgery, ASST Spedali Civili of Brescia, Brescia, Italy
- Department of Medical and Surgical Specialties, Radiological Sciences, and Public Health, School of Medicine, University of Brescia, Brescia, Italy
| | - Alessandra Sordi
- Unit of Otorhinolaryngology—Head and Neck Surgery, ASST Spedali Civili of Brescia, Brescia, Italy
- Department of Medical and Surgical Specialties, Radiological Sciences, and Public Health, School of Medicine, University of Brescia, Brescia, Italy
| | - Claudia Montenegro
- Unit of Otorhinolaryngology—Head and Neck Surgery, ASST Spedali Civili of Brescia, Brescia, Italy
- Department of Medical and Surgical Specialties, Radiological Sciences, and Public Health, School of Medicine, University of Brescia, Brescia, Italy
| | - Davide Lancini
- Unit of Otorhinolaryngology—Head and Neck Surgery, ASST Spedali Civili of Brescia, Brescia, Italy
| | - Francesca Pia Villani
- The BioRobotics Institute, Scuola Superiore Sant’Anna, Pisa, Italy
- Department of Excellence in Robotics and AI, Scuola Superiore Sant’Anna, Pisa, Italy
| | - Sara Moccia
- The BioRobotics Institute, Scuola Superiore Sant’Anna, Pisa, Italy
- Department of Excellence in Robotics and AI, Scuola Superiore Sant’Anna, Pisa, Italy
| | - Cesare Piazza
- Unit of Otorhinolaryngology—Head and Neck Surgery, ASST Spedali Civili of Brescia, Brescia, Italy
- Department of Medical and Surgical Specialties, Radiological Sciences, and Public Health, School of Medicine, University of Brescia, Brescia, Italy
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