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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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Paderno A, Bedi N, Rau A, Holsinger CF. Computer Vision and Videomics in Otolaryngology-Head and Neck Surgery: Bridging the Gap Between Clinical Needs and the Promise of Artificial Intelligence. Otolaryngol Clin North Am 2024:S0030-6665(24)00074-4. [PMID: 38981809 DOI: 10.1016/j.otc.2024.05.005] [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: 07/11/2024]
Abstract
This article discusses the role of computer vision in otolaryngology, particularly through endoscopy and surgery. It covers recent applications of artificial intelligence (AI) in nonradiologic imaging within otolaryngology, noting the benefits and challenges, such as improving diagnostic accuracy and optimizing therapeutic outcomes, while also pointing out the necessity for enhanced data curation and standardized research methodologies to advance clinical applications. Technical aspects are also covered, providing a detailed view of the progression from manual feature extraction to more complex AI models, including convolutional neural networks and vision transformers and their potential application in clinical settings.
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Affiliation(s)
- Alberto Paderno
- IRCCS Humanitas Research Hospital, via Manzoni 56, Rozzano, Milan 20089, Italy; Department of Biomedical Sciences, Humanitas University, Via Rita Levi Montalcini 4, Pieve Emanuele, Milan 20072, Italy.
| | - Nikita Bedi
- Division of Head and Neck Surgery, Department of Otolaryngology, Stanford University, Palo Alto, CA, USA
| | - Anita Rau
- Department of Biomedical Data Science, Stanford University, Palo Alto, CA, 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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Kim YE, Serpedin A, Periyakoil P, German D, Rameau A. Sociodemographic reporting in videomics research: a review of practices in otolaryngology - head and neck surgery. Eur Arch Otorhinolaryngol 2024:10.1007/s00405-024-08659-0. [PMID: 38704768 DOI: 10.1007/s00405-024-08659-0] [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: 02/19/2024] [Accepted: 04/02/2024] [Indexed: 05/07/2024]
Abstract
OBJECTIVE To assess reporting practices of sociodemographic data in Upper Aerodigestive Tract (UAT) videomics research in Otolaryngology-Head and Neck Surgery (OHNS). STUDY DESIGN Narrative review. METHODS Four online research databases were searched for peer-reviewed articles on videomics and UAT endoscopy in OHNS, published since January 1, 2017. Title and abstract search, followed by a full-text screening was performed. Dataset audit criteria were determined by the MINIMAR reporting standards for patient demographic characteristics, in addition to gender and author affiliations. RESULTS Of the 57 studies that were included, 37% reported any sociodemographic information on their dataset. Among these studies, all reported age, most reported sex (86%), two (10%) reported race, and one (5%) reported ethnicity and socioeconomic status. No studies reported gender. Most studies (84%) included at least one female author, and more than half of the studies (53%) had female first/senior authors, with no significant differences in the rate of sociodemographic reporting in studies with and without female authors (any female author: p = 0.2664; first/senior female author: p > 0.9999). Most studies based in the US reported at least one sociodemographic variable (79%), compared to those in Europe (24%) and in Asia (20%) (p = 0.0012). The rates of sociodemographic reporting in journals of different categories were as follows: clinical OHNS: 44%, clinical non-OHNS: 40%, technical: 42%, interdisciplinary: 10%. CONCLUSIONS There is prevalent underreporting of sociodemographic information in OHNS videomics research utilizing UAT endoscopy. Routine reporting of sociodemographic information should be implemented for AI-based research to help minimize algorithmic biases that have been previously demonstrated. LEVEL OF EVIDENCE: 4
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Affiliation(s)
- Yeo Eun Kim
- Department of Otolaryngology-Head and Neck Surgery, Weill Cornell Medicine, Sean Parker Institute for the Voice, 240 East 59th St, New York, NY, 10022, USA
| | - Aisha Serpedin
- Department of Otolaryngology-Head and Neck Surgery, Weill Cornell Medicine, Sean Parker Institute for the Voice, 240 East 59th St, New York, NY, 10022, USA
| | - Preethi Periyakoil
- Department of Otolaryngology-Head and Neck Surgery, Weill Cornell Medicine, Sean Parker Institute for the Voice, 240 East 59th St, New York, NY, 10022, USA
| | - Daniel German
- Department of Otolaryngology-Head and Neck Surgery, Weill Cornell Medicine, Sean Parker Institute for the Voice, 240 East 59th St, New York, NY, 10022, USA
| | - Anaïs Rameau
- Department of Otolaryngology-Head and Neck Surgery, Weill Cornell Medicine, Sean Parker Institute for the Voice, 240 East 59th St, New York, NY, 10022, USA.
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Baldini C, Azam MA, Sampieri C, Ioppi A, Ruiz-Sevilla L, Vilaseca I, Alegre B, Tirrito A, Pennacchi A, Peretti G, Moccia S, Mattos LS. An automated approach for real-time informative frames classification in laryngeal endoscopy using deep learning. Eur Arch Otorhinolaryngol 2024:10.1007/s00405-024-08676-z. [PMID: 38698163 DOI: 10.1007/s00405-024-08676-z] [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: 02/18/2024] [Accepted: 04/08/2024] [Indexed: 05/05/2024]
Abstract
PURPOSE Informative image selection in laryngoscopy has the potential for improving automatic data extraction alone, for selective data storage and a faster review process, or in combination with other artificial intelligence (AI) detection or diagnosis models. This paper aims to demonstrate the feasibility of AI in providing automatic informative laryngoscopy frame selection also capable of working in real-time providing visual feedback to guide the otolaryngologist during the examination. METHODS Several deep learning models were trained and tested on an internal dataset (n = 5147 images) and then tested on an external test set (n = 646 images) composed of both white light and narrow band images. Four videos were used to assess the real-time performance of the best-performing model. RESULTS ResNet-50, pre-trained with the pretext strategy, reached a precision = 95% vs. 97%, recall = 97% vs, 89%, and the F1-score = 96% vs. 93% on the internal and external test set respectively (p = 0.062). The four testing videos are provided in the supplemental materials. CONCLUSION The deep learning model demonstrated excellent performance in identifying diagnostically relevant frames within laryngoscopic videos. With its solid accuracy and real-time capabilities, the system is promising for its development in a clinical setting, either autonomously for objective quality control or in conjunction with other algorithms within a comprehensive AI toolset aimed at enhancing tumor detection and diagnosis.
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Affiliation(s)
- Chiara Baldini
- Department of Advanced Robotics, Istituto Italiano di Tecnologia, Genoa, Italy
- Departement of Informatics, Bioengineering, Robotics and Systems Engineering, University of Genoa, Genoa, Italy
| | - Muhammad Adeel Azam
- Department of Advanced Robotics, Istituto Italiano di Tecnologia, Genoa, Italy
- Departement of Informatics, Bioengineering, Robotics and Systems Engineering, University of Genoa, Genoa, Italy
| | - Claudio Sampieri
- Department of Experimental Medicine (DIMES), University of Genoa, Genoa, Italy.
- Department of Otolaryngology, Hospital Clínic, C. de Villarroel, 170, 08029, Barcelona, Spain.
- Unit of Head and Neck Tumors, Hospital Clínic, Barcelona, Spain.
| | | | - Laura Ruiz-Sevilla
- Otorhinolaryngology Head-Neck Surgery Department, Hospital Universitari Joan XXIII de Tarragona, Tarragona, Spain
| | - Isabel Vilaseca
- Department of Otolaryngology, Hospital Clínic, C. de Villarroel, 170, 08029, Barcelona, Spain
- Unit of Head and Neck Tumors, Hospital Clínic, Barcelona, Spain
- Translational Genomics and Target Therapies in Solid Tumors Group, Institut d́Investigacions Biomèdiques August Pi i Sunyer, IDIBAPS, Barcelona, Spain
- Faculty of Medicine, University of Barcelona, Barcelona, Spain
| | - Berta Alegre
- Department of Otolaryngology, Hospital Clínic, C. de Villarroel, 170, 08029, Barcelona, Spain
- Unit of Head and Neck Tumors, Hospital Clínic, Barcelona, Spain
| | - Alessandro Tirrito
- 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
| | - Alessia Pennacchi
- 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
| | - 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
| | - Sara Moccia
- The BioRobotics Institute and Department of Excellence in Robotics and AI, Scuola Superiore Sant'Anna, Pisa, Italy
| | - Leonardo S Mattos
- Department of Advanced Robotics, Istituto Italiano di Tecnologia, Genoa, Italy
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Marchi F, Bellini E, Iandelli A, Sampieri C, Peretti G. Exploring the landscape of AI-assisted decision-making in head and neck cancer treatment: a comparative analysis of NCCN guidelines and ChatGPT responses. Eur Arch Otorhinolaryngol 2024; 281:2123-2136. [PMID: 38421392 DOI: 10.1007/s00405-024-08525-z] [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: 12/14/2023] [Accepted: 02/02/2024] [Indexed: 03/02/2024]
Abstract
PURPOSE Recent breakthroughs in natural language processing and machine learning, exemplified by ChatGPT, have spurred a paradigm shift in healthcare. Released by OpenAI in November 2022, ChatGPT rapidly gained global attention. Trained on massive text datasets, this large language model holds immense potential to revolutionize healthcare. However, existing literature often overlooks the need for rigorous validation and real-world applicability. METHODS This head-to-head comparative study assesses ChatGPT's capabilities in providing therapeutic recommendations for head and neck cancers. Simulating every NCCN Guidelines scenarios. ChatGPT is queried on primary treatments, adjuvant treatment, and follow-up, with responses compared to the NCCN Guidelines. Performance metrics, including sensitivity, specificity, and F1 score, are employed for assessment. RESULTS The study includes 68 hypothetical cases and 204 clinical scenarios. ChatGPT exhibits promising capabilities in addressing NCCN-related queries, achieving high sensitivity and overall accuracy across primary treatment, adjuvant treatment, and follow-up. The study's metrics showcase robustness in providing relevant suggestions. However, a few inaccuracies are noted, especially in primary treatment scenarios. CONCLUSION Our study highlights the proficiency of ChatGPT in providing treatment suggestions. The model's alignment with the NCCN Guidelines sets the stage for a nuanced exploration of AI's evolving role in oncological decision support. However, challenges related to the interpretability of AI in clinical decision-making and the importance of clinicians understanding the underlying principles of AI models remain unexplored. As AI continues to advance, collaborative efforts between models and medical experts are deemed essential for unlocking new frontiers in personalized cancer care.
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Affiliation(s)
- Filippo Marchi
- Unit of Otorhinolaryngology-Head and Neck Surgery, IRCCS Ospedale Policlinico San Martino, Largo Rosanna Benzi, 10, 16132, Genoa, Italy
- Department of Surgical Sciences and Integrated Diagnostics (DISC), University of Genoa, 16132, Genoa, Italy
| | - Elisa Bellini
- Unit of Otorhinolaryngology-Head and Neck Surgery, IRCCS Ospedale Policlinico San Martino, Largo Rosanna Benzi, 10, 16132, Genoa, Italy.
- Department of Surgical Sciences and Integrated Diagnostics (DISC), University of Genoa, 16132, Genoa, Italy.
| | - Andrea Iandelli
- Unit of Otorhinolaryngology-Head and Neck Surgery, IRCCS Ospedale Policlinico San Martino, Largo Rosanna Benzi, 10, 16132, Genoa, Italy
| | - Claudio Sampieri
- Department of Experimental Medicine (DIMES), University of Genoa, Genoa, Italy
- Department of Otolaryngology-Hospital Cliníc, Barcelona, Spain
- Functional Unit of Head and Neck Tumors-Hospital Cliníc, Barcelona, Spain
| | - Giorgio Peretti
- Unit of Otorhinolaryngology-Head and Neck Surgery, IRCCS Ospedale Policlinico San Martino, Largo Rosanna Benzi, 10, 16132, Genoa, Italy
- Department of Surgical Sciences and Integrated Diagnostics (DISC), University of Genoa, 16132, Genoa, Italy
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Sampieri C, Ioppi A, Costantino A. Comments on: "Vocal Cord Leukoplakia Classification Using Siamese Network Under Small Samples of White Light Endoscopy Images". Otolaryngol Head Neck Surg 2024. [PMID: 38385676 DOI: 10.1002/ohn.690] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/30/2023] [Revised: 01/22/2024] [Accepted: 01/26/2024] [Indexed: 02/23/2024]
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
| | - Alessandro Ioppi
- Department of Otorhinolaryngology-Head and Neck Surgery, "S. Chiara" Hospital, Azienda Provinciale per i Servizi Sanitari (APSS), Trento, Italy
| | - Andrea Costantino
- Department of Otolaryngology-Head and Neck Surgery, AdventHealth Orlando, 410 Celebration Place, Celebration, Florida, USA
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Costantino A, Sampieri C, Pace GM, Festa BM, Cerri L, Giordano GG, Dalè M, Spriano G, Peretti G, De Virgilio A. Development of machine learning models for the prediction of long-term feeding tube dependence after oral and oropharyngeal cancer surgery. Oral Oncol 2024; 148:106643. [PMID: 38006688 DOI: 10.1016/j.oraloncology.2023.106643] [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: 05/30/2023] [Revised: 09/11/2023] [Accepted: 11/16/2023] [Indexed: 11/27/2023]
Abstract
PURPOSE To predict the necessity of enteral nutrition at 28 days after surgery in patients undergoing major head and neck oncologic procedures for oral and oropharyngeal cancers. MATERIAL AND METHODS Data from 193 patients with oral cavity and oropharyngeal squamous cell carcinoma were retrospectively collected at two tertiary referral centers to train (n = 135) and validate (n = 58) six supervised machine learning (ML) models for binary prediction employing 29 clinical variables available pre-operatively. RESULTS The accuracy of the six ML models ranged between 0.74 and 0.88, while the measured area under the curve (AUC) between 0.75 and 0.87. The ML algorithms showed high specificity (range 0.87-0.96) and moderate sensitivity (range: 0.31-0.77) in detecting patients with ≥28 days feeding tube dependence. Negative predictive value was higher (range: 0.81-0.93) compared to positive predictive value (range: 0.40-0.71). Finally, the F1 score ranged between 0.35 and 0.74. CONCLUSIONS Classification performance of the ML algorithms showed optimistic accuracy in the prediction of enteral nutrition at 28 days after surgery. Prospective studies are mandatory to define the clinical benefit of a ML-based pre-operative prediction of a personalized nutrition protocol.
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Affiliation(s)
- Andrea Costantino
- Department of Biomedical Sciences, Humanitas University, Via Rita Levi Montalcini, 4, 20090 Pieve Emanuele (MI), Italy; Otorhinolaryngology Unit, IRCCS Humanitas Research Hospital, Via Manzoni 56, Rozzano (MI), Italy
| | - Claudio Sampieri
- Department of Medical Science (DIMES), University of Genoa, Genoa, Italy; Functional Unit of Head and Neck Tumors, Hospital Clínic, Barcelona, Spain; Otorhinolaryngology Department, Hospital Clínic, Barcelona, Spain.
| | - Gian Marco Pace
- Department of Biomedical Sciences, Humanitas University, Via Rita Levi Montalcini, 4, 20090 Pieve Emanuele (MI), Italy; Otorhinolaryngology Unit, IRCCS Humanitas Research Hospital, Via Manzoni 56, Rozzano (MI), Italy
| | - Bianca Maria Festa
- Department of Biomedical Sciences, Humanitas University, Via Rita Levi Montalcini, 4, 20090 Pieve Emanuele (MI), Italy; Otorhinolaryngology Unit, IRCCS Humanitas Research Hospital, Via Manzoni 56, Rozzano (MI), Italy
| | - Luca Cerri
- Department of Biomedical Sciences, Humanitas University, Via Rita Levi Montalcini, 4, 20090 Pieve Emanuele (MI), Italy
| | - Giorgio Gregory Giordano
- 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
| | - Michael Dalè
- 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
| | - Giuseppe Spriano
- Department of Biomedical Sciences, Humanitas University, Via Rita Levi Montalcini, 4, 20090 Pieve Emanuele (MI), Italy; Otorhinolaryngology Unit, IRCCS Humanitas Research Hospital, Via Manzoni 56, Rozzano (MI), Italy
| | - 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
| | - Armando De Virgilio
- Department of Biomedical Sciences, Humanitas University, Via Rita Levi Montalcini, 4, 20090 Pieve Emanuele (MI), Italy; Otorhinolaryngology Unit, IRCCS Humanitas Research Hospital, Via Manzoni 56, Rozzano (MI), Italy
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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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10
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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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11
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Bassani S, Lee YK, Campagnari V, Eccher A, Monzani D, Nocini R, Sacchetto L, Molteni G. From Hype To Reality: A Narrative Review on the Promising Role of Artificial Intelligence in Larynx Cancer Detection and Transoral Microsurgery. Crit Rev Oncog 2023; 28:21-24. [PMID: 37968989 DOI: 10.1615/critrevoncog.2023049134] [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: 11/17/2023]
Abstract
Early larynx cancer detection plays a crucial role in improving treatment outcomes and recent studies have shown promising results in using artificial intelligence for larynx cancer detection. Artificial intelligence also has the potential to enhance transoral larynx microsurgery. This narrative review summarizes the current evidence regarding its use in larynx cancer detection and potential applications in transoral larynx microsurgery. The utilization of artificial intelligence in larynx cancer detection with white light endoscopy and narrow-band imaging helps improve diagnostic accuracy and efficiency. It can also potentially enhance transoral larynx microsurgery by aiding surgeons in real-time decision-making and minimizing the risk of complications. However, further prospective studies are warranted to validate the findings, and additional research is necessary to optimize the integration of artificial intelligence in our clinical practice.
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Affiliation(s)
- Sara Bassani
- Otolaryngology-Head and Neck Surgery Department, University of Verona, Verona, Italy
| | - Ying Ki Lee
- Head and Neck Surgery Department, Guy's and St. Thomas' NHS Foundation Trust, London, United Kingdom
| | - Valentina Campagnari
- Unit of Otorhinolaryngology- Head and Neck Surgery, IRCCS Ospedale Policlinico San Martino, Genoa, Italy
| | - Albino Eccher
- Department of Pathology and Diagnostics, University and Hospital Trust of Verona, Verona, Italy
| | - Daniele Monzani
- Otolaryngology-Head and Neck Surgery Department, University of Verona, Verona, Italy
| | - Riccardo Nocini
- Otolaryngology-Head and Neck Surgery Department, University of Verona, Verona, Italy
| | - Luca Sacchetto
- Otolaryngology-Head and Neck Surgery Department, University of Verona, Verona, Italy
| | - Gabriele Molteni
- Department of Otorhinolaryngology, Head and Neck Surgery, IRCCS Azienda Ospedaliero-Universitaria di Bologna, Policlinico S. Orsola-Malpighi, Bologna, Italy
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