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Alazwari S, Maashi M, Alsamri J, Alamgeer M, Ebad SA, Alotaibi SS, Obayya M, Al Zanin S. Improving laryngeal cancer detection using chaotic metaheuristics integration with squeeze-and-excitation resnet model. Health Inf Sci Syst 2024; 12:38. [PMID: 39006830 PMCID: PMC11239646 DOI: 10.1007/s13755-024-00296-5] [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: 03/26/2024] [Accepted: 06/26/2024] [Indexed: 07/16/2024] Open
Abstract
Laryngeal cancer (LC) represents a substantial world health problem, with diminished survival rates attributed to late-stage diagnoses. Correct treatment for LC is complex, particularly in the final stages. This kind of cancer is a complex malignancy inside the head and neck region of patients. Recently, researchers serving medical consultants to recognize LC efficiently develop different analysis methods and tools. However, these existing tools and techniques have various problems regarding performance constraints, like lesser accuracy in detecting LC at the early stages, additional computational complexity, and colossal time utilization in patient screening. Deep learning (DL) approaches have been established that are effective in the recognition of LC. Therefore, this study develops an efficient LC Detection using the Chaotic Metaheuristics Integration with the DL (LCD-CMDL) technique. The LCD-CMDL technique mainly focuses on detecting and classifying LC utilizing throat region images. In the LCD-CMDL technique, the contrast enhancement process uses the CLAHE approach. For feature extraction, the LCD-CMDL technique applies the Squeeze-and-Excitation ResNet (SE-ResNet) model to learn the complex and intrinsic features from the image preprocessing. Moreover, the hyperparameter tuning of the SE-ResNet approach is performed using a chaotic adaptive sparrow search algorithm (CSSA). Finally, the extreme learning machine (ELM) model was applied to detect and classify the LC. The performance evaluation of the LCD-CMDL approach occurs utilizing a benchmark throat region image database. The experimental values implied the superior performance of the LCD-CMDL approach over recent state-of-the-art approaches.
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Affiliation(s)
- Sana Alazwari
- Department of Information Technology, College of Computers and Information Technology, Taif University, Taif P.O. Box 11099, 21944 Taif, Saudi Arabia
| | - Mashael Maashi
- Department of Software Engineering, College of Computer and Information Sciences, King Saud University, Po Box 103786, 11543 Riyadh, Saudi Arabia
| | - Jamal Alsamri
- Department of Biomedical Engineering, College of Engineering, Princess Nourah Bint Abdulrahman University, Riyadh, Saudi Arabia
| | - Mohammad Alamgeer
- Department of Information Systems, College of Science & Art at Mahayil, King Khalid University, Abha, Saudi Arabia
| | - Shouki A Ebad
- Department of Computer Science, Faculty of Science, Northern Border University, 91431 Arar, Saudi Arabia
| | - Saud S Alotaibi
- Department of Information Systems, College of Computing and Information Systems,, Umm Al-Qura University, Makkah, Saudi Arabia
| | - Marwa Obayya
- Department of Biomedical Engineering, College of Engineering, Princess Nourah Bint Abdulrahman University, Riyadh, Saudi Arabia
| | - Samah Al Zanin
- Department of Computer Science, Applied College, Prince Sattam Bin Abdulaziz University, Kharj, Saudi Arabia
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Mamidi IS, Dunham ME, Adkins LK, McWhorter AJ, Fang Z, Banh BT. Laryngeal Cancer Screening During Flexible Video Laryngoscopy Using Large Computer Vision Models. Ann Otol Rhinol Laryngol 2024; 133:720-728. [PMID: 38755974 DOI: 10.1177/00034894241253376] [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] [Indexed: 05/18/2024]
Abstract
OBJECTIVE Develop an artificial intelligence assisted computer vision model to screen for laryngeal cancer during flexible laryngoscopy. METHODS Using laryngeal images and flexible laryngoscopy video recordings, we developed computer vision models to classify video frames for usability and cancer screening. A separate model segments any identified lesions on the frames. We used these computer vision models to construct a video stream annotation system. This system classifies findings from flexible laryngoscopy as "potentially malignant" or "probably benign" and segments any detected lesions. Additionally, the model provides a confidence level for each classification. RESULTS The overall accuracy of the flexible laryngoscopy cancer screening model was 92%. For cancer screening, it achieved a sensitivity of 97.7% and a specificity of 76.9%. The segmentation model attained an average precision at a 0.50 intersection-over-union of 0.595. The confidence level for positive screening results can assist clinicians in counseling patients regarding the findings. CONCLUSION Our model is highly sensitive and adequately specific for laryngeal cancer screening. Segmentation helps endoscopists identify and describe potential lesions. Further optimization is required to enable the model's deployment in clinical settings for real-time annotation during flexible laryngoscopy.
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Affiliation(s)
- Ishwarya S Mamidi
- Department of Otolaryngology-Head and Neck Surgery, Louisiana State University Health Sciences Center, New Orleans, LA, USA
| | - Michael E Dunham
- Department of Otolaryngology-Head and Neck Surgery, Louisiana State University Health Sciences Center, New Orleans, LA, USA
| | - Lacey K Adkins
- Department of Otolaryngology-Head and Neck Surgery, Louisiana State University Health Sciences Center, New Orleans, LA, USA
| | - Andrew J McWhorter
- Department of Otolaryngology-Head and Neck Surgery, Louisiana State University Health Sciences Center, New Orleans, LA, USA
| | - Zhide Fang
- Biostatistics Program, School of Public Health, Louisiana State University Health Sciences Center, New Orleans LA, USA
| | - Britney T Banh
- Our Lady of the Lake Voice Center, Our Lady of the Lake Regional Medical Center, Baton Rouge, LA, USA
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Wang ML, Tie CW, Wang JH, Zhu JQ, 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 based artificial intelligence model to assist vocal fold leukoplakia diagnosis: A multicentre diagnostic study. Am J Otolaryngol 2024; 45:104342. [PMID: 38703609 DOI: 10.1016/j.amjoto.2024.104342] [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: 02/28/2024] [Accepted: 04/23/2024] [Indexed: 05/06/2024]
Abstract
OBJECTIVE To develop a multi-instance learning (MIL) based artificial intelligence (AI)-assisted diagnosis models by using laryngoscopic images to differentiate benign and malignant vocal fold leukoplakia (VFL). METHODS The AI system was developed, trained and validated on 5362 images of 551 patients from three hospitals. Automated regions of interest (ROI) segmentation algorithm was utilized to construct image-level features. MIL was used to fusion image level results to patient level features, then the extracted features were modeled by seven machine learning algorithms. Finally, we evaluated the image level and patient level results. Additionally, 50 videos of VFL were prospectively gathered to assess the system's real-time diagnostic capabilities. A human-machine comparison database was also constructed to compare the diagnostic performance of otolaryngologists with and without AI assistance. RESULTS In internal and external validation sets, the maximum area under the curve (AUC) for image level segmentation models was 0.775 (95 % CI 0.740-0.811) and 0.720 (95 % CI 0.684-0.756), respectively. Utilizing a MIL-based fusion strategy, the AUC at the patient level increased to 0.869 (95 % CI 0.798-0.940) and 0.851 (95 % CI 0.756-0.945). For real-time video diagnosis, the maximum AUC at the patient level reached 0.850 (95 % CI, 0.743-0.957). With AI assistance, the AUC improved from 0.720 (95 % CI 0.682-0.755) to 0.808 (95 % CI 0.775-0.839) for senior otolaryngologists and from 0.647 (95 % CI 0.608-0.686) to 0.807 (95 % CI 0.773-0.837) for junior otolaryngologists. CONCLUSIONS The MIL based AI-assisted diagnosis system can significantly improve the diagnostic performance of otolaryngologists for VFL and help to make proper clinical decisions.
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Affiliation(s)
- Mei-Ling Wang
- Department of Endoscopy, National Cancer Center, National Clinical Research Center for Cancer, Cancer Hospital & Shenzhen Hospital, Chinese Academy of Medical Sciences, Peking Union Medical College, Shenzhen, China
| | - Cheng-Wei Tie
- Department of Endoscopy, National Cancer Center, National Clinical Research Center for Cancer, Cancer Hospital, Chinese Academy of Medical Sciences, Peking Union Medical College, Beijing, 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
| | - Ji-Qing Zhu
- Department of Endoscopy, National Cancer Center, National Clinical Research Center for Cancer, Cancer Hospital, Chinese Academy of Medical Sciences, Peking Union Medical College, Beijing, 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, 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, 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, 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, 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, Peking Union Medical College, Beijing, China.
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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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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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Mohamed N, Almutairi RL, Abdelrahim S, Alharbi R, Alhomayani FM, Elamin Elnaim BM, Elhag AA, Dhakal R. Automated Laryngeal Cancer Detection and Classification Using Dwarf Mongoose Optimization Algorithm with Deep Learning. Cancers (Basel) 2023; 16:181. [PMID: 38201608 PMCID: PMC10778016 DOI: 10.3390/cancers16010181] [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: 11/28/2023] [Revised: 12/25/2023] [Accepted: 12/26/2023] [Indexed: 01/12/2024] Open
Abstract
Laryngeal cancer (LCA) is a serious disease with a concerning global rise in incidence. Accurate treatment for LCA is particularly challenging in later stages, due to its complex nature as a head and neck malignancy. To address this challenge, researchers have been actively developing various analysis methods and tools to assist medical professionals in efficient LCA identification. However, existing tools and methods often suffer from various limitations, including low accuracy in early-stage LCA detection, high computational complexity, and lengthy patient screening times. With this motivation, this study presents an Automated Laryngeal Cancer Detection and Classification using a Dwarf Mongoose Optimization Algorithm with Deep Learning (ALCAD-DMODL) technique. The main objective of the ALCAD-DMODL method is to recognize the existence of LCA using the DL model. In the presented ALCAD-DMODL technique, a median filtering (MF)-based noise removal process takes place to get rid of the noise. Additionally, the ALCAD-DMODL technique involves the EfficientNet-B0 model for deriving feature vectors from the pre-processed images. For optimal hyperparameter tuning of the EfficientNet-B0 model, the DMO algorithm can be applied to select the parameters. Finally, the multi-head bidirectional gated recurrent unit (MBGRU) model is applied for the recognition and classification of LCA. The simulation result analysis of the ALCAD-DMODL technique is carried out on the throat region image dataset. The comparison study stated the supremacy of the ALCAD-DMODL technique in terms of distinct measures.
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Affiliation(s)
- Nuzaiha Mohamed
- Department of Public Health, College of Public Health and Health Informatics, University of Hail, Ha’il 81451, Saudi Arabia; (N.M.); (R.L.A.); (S.A.)
| | - Reem Lafi Almutairi
- Department of Public Health, College of Public Health and Health Informatics, University of Hail, Ha’il 81451, Saudi Arabia; (N.M.); (R.L.A.); (S.A.)
| | - Sayda Abdelrahim
- Department of Public Health, College of Public Health and Health Informatics, University of Hail, Ha’il 81451, Saudi Arabia; (N.M.); (R.L.A.); (S.A.)
| | - Randa Alharbi
- Department of Statistics, Faculty of Science, University of Tabuk, Tabuk 71491, Saudi Arabia;
| | - Fahad Mohammed Alhomayani
- College of Computers and Information Technology, Taif University, Taif 21944, Saudi Arabia;
- Applied College, Taif University, Taif 21944, Saudi Arabia
| | - Bushra M. Elamin Elnaim
- Department of Computer Science, College of Science and Humanities in Al-Sulail, Prince Sattam Bin Abdulaziz University, Al-Kharj 16278, Saudi Arabia;
| | - Azhari A. Elhag
- Department of Mathematics and Statistics, College of Science, Taif University, Taif 21944, Saudi Arabia;
| | - Rajendra Dhakal
- Department of Computer Science and Engineering, Sejong University, Seoul 05006, Republic of Korea
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Ding X, Huang Y, Zhao Y, Tian X, Feng G, Gao Z. Accurate Segmentation and Tracking of Chorda Tympani in Endoscopic Middle Ear Surgery with Artificial Intelligence. EAR, NOSE & THROAT JOURNAL 2023:1455613231212051. [PMID: 38083840 DOI: 10.1177/01455613231212051] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/22/2023] Open
Abstract
Objective: We introduce a novel endoscopic middle ear surgery dataset specifically designed for evaluating deep learning (DL)-based semantic segmentation of chorda tympani. Methods: We curated a dataset comprising 8240 images from 25 patients, divided into a training set (20%, 1648 images), validation set (5%, 412 images), and test set (75%, 6180 images). We employed data enhancement techniques to expand the picture size of the training and validation sets by 5 times (training set: 8240 images, verification set: 2060 images). Subsequently, we employed a multistage transfer learning training method to establish, train, and validate various convolutional neural networks. Results: On the validation set of 2060 labeled images, our proposed network achieved good results, with the U-net exhibiting the highest effectiveness (mIOU = 0.8737, mPA = 0.9263). Furthermore, when applied to the test dataset of 6180 raw images and contrasted with the prediction of otologists, the overall performance of the U-net was excellent (accuracy = 0.911, precision = 0.9823, sensitivity = 0.8777, specificity = 0.9714). Conclusions: Our findings demonstrate that DL can be successfully employed for automatic segmentation of chorda tympani in endoscopic middle ear surgery, yielding high-performance results. This study validates the potential feasibility of future intelligent navigation technologies to assist in endoscopic middle ear surgery.
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Affiliation(s)
- Xin Ding
- Department of Otorhinolaryngology Head and Neck Surgery, Peking Union Medical College Hospital, Dongcheng District, Beijing, China
| | - Yu Huang
- Department of Otorhinolaryngology Head and Neck Surgery, Peking Union Medical College Hospital, Dongcheng District, Beijing, China
| | - Yang Zhao
- Department of Otorhinolaryngology Head and Neck Surgery, Peking Union Medical College Hospital, Dongcheng District, Beijing, China
| | - Xu Tian
- Department of Otorhinolaryngology Head and Neck Surgery, Peking Union Medical College Hospital, Dongcheng District, Beijing, China
| | - Guodong Feng
- Department of Otorhinolaryngology Head and Neck Surgery, Peking Union Medical College Hospital, Dongcheng District, Beijing, China
| | - Zhiqiang Gao
- Department of Otorhinolaryngology Head and Neck Surgery, Peking Union Medical College Hospital, Dongcheng District, Beijing, China
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Zhong NN, Wang HQ, Huang XY, Li ZZ, Cao LM, Huo FY, Liu B, Bu LL. Enhancing head and neck tumor management with artificial intelligence: Integration and perspectives. Semin Cancer Biol 2023; 95:52-74. [PMID: 37473825 DOI: 10.1016/j.semcancer.2023.07.002] [Citation(s) in RCA: 4] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/29/2023] [Revised: 07/11/2023] [Accepted: 07/15/2023] [Indexed: 07/22/2023]
Abstract
Head and neck tumors (HNTs) constitute a multifaceted ensemble of pathologies that primarily involve regions such as the oral cavity, pharynx, and nasal cavity. The intricate anatomical structure of these regions poses considerable challenges to efficacious treatment strategies. Despite the availability of myriad treatment modalities, the overall therapeutic efficacy for HNTs continues to remain subdued. In recent years, the deployment of artificial intelligence (AI) in healthcare practices has garnered noteworthy attention. AI modalities, inclusive of machine learning (ML), neural networks (NNs), and deep learning (DL), when amalgamated into the holistic management of HNTs, promise to augment the precision, safety, and efficacy of treatment regimens. The integration of AI within HNT management is intricately intertwined with domains such as medical imaging, bioinformatics, and medical robotics. This article intends to scrutinize the cutting-edge advancements and prospective applications of AI in the realm of HNTs, elucidating AI's indispensable role in prevention, diagnosis, treatment, prognostication, research, and inter-sectoral integration. The overarching objective is to stimulate scholarly discourse and invigorate insights among medical practitioners and researchers to propel further exploration, thereby facilitating superior therapeutic alternatives for patients.
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Affiliation(s)
- Nian-Nian Zhong
- State Key Laboratory of Oral & Maxillofacial Reconstruction and Regeneration, Key Laboratory of Oral Biomedicine Ministry of Education, Hubei Key Laboratory of Stomatology, School & Hospital of Stomatology, Wuhan University, Wuhan 430079, China
| | - Han-Qi Wang
- State Key Laboratory of Oral & Maxillofacial Reconstruction and Regeneration, Key Laboratory of Oral Biomedicine Ministry of Education, Hubei Key Laboratory of Stomatology, School & Hospital of Stomatology, Wuhan University, Wuhan 430079, China
| | - Xin-Yue Huang
- State Key Laboratory of Oral & Maxillofacial Reconstruction and Regeneration, Key Laboratory of Oral Biomedicine Ministry of Education, Hubei Key Laboratory of Stomatology, School & Hospital of Stomatology, Wuhan University, Wuhan 430079, China
| | - Zi-Zhan Li
- State Key Laboratory of Oral & Maxillofacial Reconstruction and Regeneration, Key Laboratory of Oral Biomedicine Ministry of Education, Hubei Key Laboratory of Stomatology, School & Hospital of Stomatology, Wuhan University, Wuhan 430079, China
| | - Lei-Ming Cao
- State Key Laboratory of Oral & Maxillofacial Reconstruction and Regeneration, Key Laboratory of Oral Biomedicine Ministry of Education, Hubei Key Laboratory of Stomatology, School & Hospital of Stomatology, Wuhan University, Wuhan 430079, China
| | - Fang-Yi Huo
- State Key Laboratory of Oral & Maxillofacial Reconstruction and Regeneration, Key Laboratory of Oral Biomedicine Ministry of Education, Hubei Key Laboratory of Stomatology, School & Hospital of Stomatology, Wuhan University, Wuhan 430079, China
| | - Bing Liu
- State Key Laboratory of Oral & Maxillofacial Reconstruction and Regeneration, Key Laboratory of Oral Biomedicine Ministry of Education, Hubei Key Laboratory of Stomatology, School & Hospital of Stomatology, Wuhan University, Wuhan 430079, China; Department of Oral & Maxillofacial - Head Neck Oncology, School & Hospital of Stomatology, Wuhan University, Wuhan 430079, China.
| | - Lin-Lin Bu
- State Key Laboratory of Oral & Maxillofacial Reconstruction and Regeneration, Key Laboratory of Oral Biomedicine Ministry of Education, Hubei Key Laboratory of Stomatology, School & Hospital of Stomatology, Wuhan University, Wuhan 430079, China; Department of Oral & Maxillofacial - Head Neck Oncology, School & Hospital of Stomatology, Wuhan University, Wuhan 430079, China.
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9
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Filipovsky T, Kalfert D, Lukavcova E, Zavazalova S, Hlozek J, Kovar D, Astl J, Holy R. The importance of preoperative and perioperative Narrow Band Imaging endoscopy in the diagnosis of pre-tumor and tumor lesions of the larynx. J Appl Biomed 2023; 21:107-112. [PMID: 37747310 DOI: 10.32725/jab.2023.015] [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: 08/16/2023] [Accepted: 09/13/2023] [Indexed: 09/26/2023] Open
Abstract
INTRODUCTION Narrow band imaging (NBI) is an endoscopic imaging method intended for the diagnosis of mucosal lesions of the larynx that are not visible in white-light endoscopy, but are typical of pre-tumor and tumor lesions of the larynx. THE PURPOSE OF THE STUDY To compare preoperative/perioperative white light endoscopy and NBI endoscopy with the results of histopathological examinations in pre-tumor and tumor lesions of the larynx. METHODS A prospective study, over a period of five years (5/2018-5/2023), included 87 patients with laryngeal lesions aged 24-80 years. We evaluated preoperative/ perioperative white light and NBI endoscopy, established a working prehistological diagnosis, and compared this with the definitive histopathological results of laryngeal biopsies. RESULTS In relation to the definitive histology score, a statistically significant correlation was found between the evaluation of the finding and the definitive histology for preoperative and perioperative white light endoscopy and NBI endoscopy (p < 0.001). Both methods showed higher precision when used perioperatively. CONCLUSION NBI endoscopy is an optical method that allows us to improve the diagnosis of laryngeal lesions, perform a controlled perioperative biopsy, and refine the surgical scope. The NBI endoscopy is a suitable method for the diagnosis of early cancerous lesions of the larynx. The use of preoperative/perioperative NBI endoscopy allowed us to achieve a high level of agreement correlation (p < 0.001) between the prehistological working diagnosis and the final histopathological result. The NBI method proves its application in the diagnosis of pre-tumor and tumor lesions of the larynx.
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Affiliation(s)
- Tomas Filipovsky
- Military University Hospital Prague, Department of Otorhinolaryngology and Maxillofacial Surgery, Prague, Czech Republic
- Charles University, Third Faculty of Medicine, Prague, Czech Republic
| | - David Kalfert
- Motol University Hospital, Department of Otorhinolaryngology and Head and Neck Surgery, Prague, Czech Republic
- Charles University, First Faculty of Medicine, Prague, Czech Republic
| | - Eva Lukavcova
- Military University Hospital Prague, Department of Otorhinolaryngology and Maxillofacial Surgery, Prague, Czech Republic
| | - Sarka Zavazalova
- Military University Hospital Prague, Department of Otorhinolaryngology and Maxillofacial Surgery, Prague, Czech Republic
- Charles University, Third Faculty of Medicine, Prague, Czech Republic
| | - Jiri Hlozek
- Military University Hospital Prague, Department of Otorhinolaryngology and Maxillofacial Surgery, Prague, Czech Republic
- Charles University, Third Faculty of Medicine, Prague, Czech Republic
| | - Daniel Kovar
- Military University Hospital Prague, Department of Otorhinolaryngology and Maxillofacial Surgery, Prague, Czech Republic
- Charles University, Third Faculty of Medicine, Prague, Czech Republic
| | - Jaromir Astl
- Military University Hospital Prague, Department of Otorhinolaryngology and Maxillofacial Surgery, Prague, Czech Republic
- Charles University, Third Faculty of Medicine, Prague, Czech Republic
| | - Richard Holy
- Military University Hospital Prague, Department of Otorhinolaryngology and Maxillofacial Surgery, Prague, Czech Republic
- Charles University, Third Faculty of Medicine, Prague, Czech Republic
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