1
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Du W, Jia M, Li J, Gao M, Zhang W, Yu Y, Wang H, Peng X. Prognostic prediction model for salivary gland carcinoma based on machine learning. Int J Oral Maxillofac Surg 2024; 53:905-910. [PMID: 38981745 DOI: 10.1016/j.ijom.2024.07.006] [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: 08/23/2023] [Revised: 06/23/2024] [Accepted: 07/01/2024] [Indexed: 07/11/2024]
Abstract
Although rare overall, salivary gland carcinomas (SGCs) are among the most common oral and maxillofacial malignancies. The aim of this study was to develop a machine learning-based model to predict the survival of patients with SGC. Patients in whom SGC was confirmed by histological testing and who underwent primary extirpation at the authors' institution between 1963 and 2014 were identified. Demographic and clinicopathological data with complete follow-up information were collected for analysis. Feature selection methods were used to determine the correlation between prognosis-related factors and survival in the collected patient data. The collected clinicopathological data and multiple machine learning algorithms were used to develop a survival prediction model. Three machine learning algorithms were applied to construct the prediction models. The area under the receiver operating characteristic curve (AUC) and accuracy were used to measure model performance. The best classification performance was achieved with a LightGBM algorithm (AUC = 0.83, accuracy = 0.91). This model enabled prognostic prediction of patient survival. The model may be useful in developing personalized diagnostic and treatment strategies and formulating individualized follow-up plans, as well as assisting in the communication between doctors and patients, facilitating a better understanding of and compliance with treatment.
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Affiliation(s)
- W Du
- Department of Oral and Maxillofacial Surgery, Peking University School and Hospital of Stomatology, Beijing, China
| | - M Jia
- Department of Oral and Maxillofacial Surgery, Peking University School and Hospital of Stomatology, Beijing, China; Zhongguancun Hospital, Beijing, China
| | - J Li
- Department of Oral and Maxillofacial Surgery, Peking University School and Hospital of Stomatology, Beijing, China
| | - M Gao
- Department of Oral and Maxillofacial Surgery, Peking University School and Hospital of Stomatology, Beijing, China
| | - W Zhang
- Department of Oral and Maxillofacial Surgery, Peking University School and Hospital of Stomatology, Beijing, China
| | - Y Yu
- Department of Oral and Maxillofacial Surgery, Peking University School and Hospital of Stomatology, Beijing, China
| | - H Wang
- School of Mathematical Sciences, Beihang University, Beijing, China
| | - X Peng
- Department of Oral and Maxillofacial Surgery, Peking University School and Hospital of Stomatology, Beijing, China.
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2
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Lechien JR. Generative AI and Otolaryngology-Head & Neck Surgery. Otolaryngol Clin North Am 2024; 57:753-765. [PMID: 38839556 DOI: 10.1016/j.otc.2024.04.006] [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: 06/07/2024]
Abstract
The increasing development of artificial intelligence (AI) generative models in otolaryngology-head and neck surgery will progressively change our practice. Practitioners and patients have access to AI resources, improving information, knowledge, and practice of patient care. This article summarizes the currently investigated applications of AI generative models, particularly Chatbot Generative Pre-trained Transformer, in otolaryngology-head and neck surgery.
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Affiliation(s)
- Jérôme R Lechien
- Research Committee of Young Otolaryngologists of the International Federation of Otorhinolaryngological Societies (IFOS), Paris, France; Division of Laryngology and Broncho-esophagology, Department of Otolaryngology-Head Neck Surgery, EpiCURA Hospital, UMONS Research Institute for Health Sciences and Technology, University of Mons (UMons), Mons, Belgium; Department of Otorhinolaryngology and Head and Neck Surgery, Foch Hospital, Paris Saclay University, Phonetics and Phonology Laboratory (UMR 7018 CNRS, Université Sorbonne Nouvelle/Paris 3), Paris, France; Department of Otorhinolaryngology and Head and Neck Surgery, CHU Saint-Pierre, Brussels, Belgium.
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3
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Chen S, Lobo BC. Regulatory and Implementation Considerations for Artificial Intelligence. Otolaryngol Clin North Am 2024; 57:871-886. [PMID: 38839554 DOI: 10.1016/j.otc.2024.04.007] [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: 06/07/2024]
Abstract
Successful artificial intelligence (AI) implementation is predicated on the trust of clinicians and patients, and is achieved through a culture of responsible use, focusing on regulations, standards, and education. Otolaryngologists can overcome barriers in AI implementation by promoting data standardization through professional societies, engaging in institutional efforts to integrate AI, and developing otolaryngology-specific AI education for both trainees and practitioners.
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Affiliation(s)
- Si Chen
- Department of Otolaryngology - Head and Neck Surgery, University of Florida College of Medicine, 1345 Center Drive, PO Box 100264, Gainesville, FL 32610, USA.
| | - Brian C Lobo
- Department of Otolaryngology - Head and Neck Surgery, University of Florida College of Medicine, 1345 Center Drive, PO Box 100264, Gainesville, FL 32610, USA
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4
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Sriram S, Creighton FX, Galaiya D. Autonomous Robotic Systems in Otolaryngology-Head and Neck Surgery. Otolaryngol Clin North Am 2024; 57:767-779. [PMID: 38971627 DOI: 10.1016/j.otc.2024.05.004] [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: 07/08/2024]
Abstract
Robotic surgery is a growing field with increasing applications to patient care. With the rising use of artificial intelligence (AI), a new frontier emerges, allowing semiautonomous robotics. This article reviews the origins of robotic surgery and subsequent trials of automaticity in all fields. It then describes specific nascent robotic and semiautonomous surgical prototypes within the field of otolaryngology. Finally, broader systemic considerations are posited regarding the implementation of AI-driven robotics in surgery.
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Affiliation(s)
- Shreya Sriram
- Johns Hopkins School of Medicine, Baltimore, MD, USA
| | - Francis X Creighton
- Department of Otolaryngology-Head and Neck Surgery, Johns Hopkins Hospital, Baltimore, MD, USA
| | - Deepa Galaiya
- Department of Otolaryngology-Head and Neck Surgery, Johns Hopkins Hospital, Baltimore, MD, USA.
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5
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Crowson MG, Nwosu OI. The Integration and Impact of Artificial Intelligence in Otolaryngology-Head and Neck Surgery: Navigating the Last Mile. Otolaryngol Clin North Am 2024; 57:887-895. [PMID: 38705741 DOI: 10.1016/j.otc.2024.04.001] [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: 05/07/2024]
Abstract
Incorporating artificial Intelligence and machine learning into otolaryngology requires careful data handling, security, and ethical considerations. Success depends on interdisciplinary cooperation, consistent innovation, and regulatory compliance to improve clinical outcomes, provider experience, and operational effectiveness.
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Affiliation(s)
- Matthew G Crowson
- Department of Otolaryngology-Head & Neck Surgery, Massachusetts Eye & Ear Hospital, Boston, MA, USA; Department of Otolaryngology-Head & Neck Surgery, Harvard Medical School, Boston, MA, USA.
| | - Obinna I Nwosu
- Department of Otolaryngology-Head & Neck Surgery, Massachusetts Eye & Ear Hospital, Boston, MA, USA; Department of Otolaryngology-Head & Neck Surgery, Harvard Medical School, Boston, MA, USA
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6
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Evangelista E, Bensoussan Y. Standardization, Collaboration, and Education in the Implementation of Artificial Intelligence in Otolaryngology: The Key to Scalable Impact. Otolaryngol Clin North Am 2024; 57:897-908. [PMID: 38845298 DOI: 10.1016/j.otc.2024.04.005] [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] [Indexed: 09/06/2024]
Abstract
The study delves into the crucial role of standardization, collaboration, and education in the integration of artificial intelligence (AI) in otolaryngology. It emphasizes the necessity of large, diverse datasets for effective AI implementation in health care, particularly in otolaryngology, due to its reliance on medical imagery and diverse instruments. The text identifies current barriers, including siloed work in academia and sparse academic-industrial partnerships, while proposing solutions like forming interdisciplinary teams and aligning incentives. Moreover, it discusses the importance of standardizing AI projects through system reporting and advocates for AI education and literacy among otolaryngology practitioners.
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Affiliation(s)
- Emily Evangelista
- Morsani College of Medicine, University of South Florida, Yael Bensoussan and Emily Evangelista, 13330 USF Laurel Drive, Morsani Health, Tampa, FL 33612, USA
| | - Yael Bensoussan
- Department of Otolaryngology - Head & Neck Surgery, University of South Florida, 13330 USF Laurel Drive, Tampa, FL 33612, USA.
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7
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Qin S, Chislett B, Ischia J, Ranasinghe W, de Silva D, Coles‐Black J, Woon D, Bolton D. ChatGPT and generative AI in urology and surgery-A narrative review. BJUI COMPASS 2024; 5:813-821. [PMID: 39323919 PMCID: PMC11420103 DOI: 10.1002/bco2.390] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/21/2023] [Revised: 04/27/2024] [Accepted: 05/12/2024] [Indexed: 09/27/2024] Open
Abstract
Introduction ChatGPT (generative pre-trained transformer [GPT]), developed by OpenAI, is a type of generative artificial intelligence (AI) that has been widely utilised since its public release. It orchestrates an advanced conversational intelligence, producing sophisticated responses to questions. ChatGPT has been successfully demonstrated across several applications in healthcare, including patient management, academic research and clinical trials. We aim to evaluate the different ways ChatGPT has been utilised in urology and more broadly in surgery. Methods We conducted a literature search of the PubMed and Embase electronic databases for the purpose of writing a narrative review and identified relevant articles on ChatGPT in surgery from the years 2000 to 2023. A PRISMA flow chart was created to highlight the article selection process. The search terms 'ChatGPT' and 'surgery' were intentionally kept broad given the nascency of the field. Studies unrelated to these terms were excluded. Duplicates were removed. Results Multiple papers have been published about novel uses of ChatGPT in surgery, ranging from assisting in administrative tasks including answering frequently asked questions, surgical consent, writing operation reports, discharge summaries, grants, journal article drafts, reviewing journal articles and medical education. AI and machine learning has also been extensively researched in surgery with respect to patient diagnosis and predicting outcomes. There are also several limitations with the software including artificial hallucination, bias, out-of-date information and patient confidentiality. Conclusion The potential of ChatGPT and related generative AI models are vast, heralding the beginning of a new era where AI may eventually become integrated seamlessly into surgical practice. Concerns with this new technology must not be disregarded in the urge to hasten progression, and potential risks impacting patients' interests must be considered. Appropriate regulation and governance of this technology will be key to optimising the benefits and addressing the intricate challenges of healthcare delivery and equity.
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Affiliation(s)
- Shane Qin
- Department of UrologyAustin HealthHeidelbergVictoriaAustralia
| | - Bodie Chislett
- Department of UrologyAustin HealthHeidelbergVictoriaAustralia
| | - Joseph Ischia
- Department of UrologyAustin HealthHeidelbergVictoriaAustralia
- Department of SurgeryUniversity of Melbourne, Austin HealthMelbourneVictoriaAustralia
| | - Weranja Ranasinghe
- Department of Anatomy and Developmental BiologyMonash UniversityMelbourneVictoriaAustralia
- Department of UrologyMonash HealthMelbourneVictoriaAustralia
| | - Daswin de Silva
- Research Centre for Data Analytics and CognitionLa Trobe UniversityMelbourneVictoriaAustralia
| | | | - Dixon Woon
- Department of UrologyAustin HealthHeidelbergVictoriaAustralia
- Department of SurgeryUniversity of Melbourne, Austin HealthMelbourneVictoriaAustralia
| | - Damien Bolton
- Department of UrologyAustin HealthHeidelbergVictoriaAustralia
- Department of SurgeryUniversity of Melbourne, Austin HealthMelbourneVictoriaAustralia
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8
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Poche JN, Hernandez SC, Melder KL, Dunham ME, Nuss DW, Fang Z. Endoscopic surgical field clarity index: An artificial intelligence-based measure of transnasal endoscopic surgical field quality. Int Forum Allergy Rhinol 2024; 14:1501-1504. [PMID: 38648256 DOI: 10.1002/alr.23348] [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: 01/25/2024] [Revised: 03/07/2024] [Accepted: 03/13/2024] [Indexed: 04/25/2024]
Abstract
KEY POINTS Clear visualization during transnasal endoscopic surgery (TNES) is crucial for safe, efficient surgery. The endoscopic surgical field clarity index (ESFCI) is an artificial intelligence-enabled measure of surgical field quality. The ESFCI allows researchers to evaluate interventions to improve visualization during TNES.
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Affiliation(s)
- John N Poche
- Department of Otolaryngology-Head and Neck Surgery, Louisiana State University Health Sciences Center, New Orleans, Louisiana, USA
| | - Stephen C Hernandez
- Department of Otolaryngology-Head and Neck Surgery, Louisiana State University Health Sciences Center, New Orleans, Louisiana, USA
| | - Katie L Melder
- Department of Otolaryngology-Head and Neck Surgery, Louisiana State University Health Sciences Center, New Orleans, Louisiana, USA
| | - Michael E Dunham
- Department of Otolaryngology-Head and Neck Surgery, Louisiana State University Health Sciences Center, New Orleans, Louisiana, USA
| | - Daniel W Nuss
- Department of Otolaryngology-Head and Neck Surgery, Louisiana State University Health Sciences Center, New Orleans, Louisiana, USA
| | - Zhide Fang
- Biostatistics Program, School of Public Health, Louisiana State University Health Sciences Center, New Orleans, Louisiana, USA
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Koyama H. Machine learning application in otology. Auris Nasus Larynx 2024; 51:666-673. [PMID: 38704894 DOI: 10.1016/j.anl.2024.04.003] [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: 12/07/2023] [Revised: 03/13/2024] [Accepted: 04/02/2024] [Indexed: 05/07/2024]
Abstract
This review presents a comprehensive history of Artificial Intelligence (AI) in the context of the revolutionary application of machine learning (ML) to medical research and clinical utilization, particularly for the benefit of researchers interested in the application of ML in otology. To this end, we discuss the key components of ML-input, output, and algorithms. In particular, some representation algorithms commonly used in medical research are discussed. Subsequently, we review ML applications in otology research, including diagnosis, influential identification, and surgical outcome prediction. In the context of surgical outcome prediction, specific surgical treatments, including cochlear implantation, active middle ear implantation, tympanoplasty, and vestibular schwannoma resection, are considered. Finally, we highlight the obstacles and challenges that need to be overcome in future research.
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Affiliation(s)
- Hajime Koyama
- Department of Otorhinolaryngology and Head and Neck Surgery, Graduate School of Medicine, The University of Tokyo, Tokyo, Japan.
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10
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Truong B, Zapala M, Kammen B, Luu K. Automated Detection of Pediatric Foreign Body Aspiration from Chest X-rays Using Machine Learning. Laryngoscope 2024; 134:3807-3814. [PMID: 38366768 DOI: 10.1002/lary.31338] [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: 09/26/2023] [Revised: 01/19/2024] [Accepted: 01/26/2024] [Indexed: 02/18/2024]
Abstract
OBJECTIVE/HYPOTHESIS Standard chest radiographs are a poor diagnostic tool for pediatric foreign body aspiration. Machine learning may improve upon the diagnostic capabilities of chest radiographs. The objective is to develop a machine learning algorithm that improves the diagnostic capabilities of chest radiographs in pediatric foreign body aspiration. METHOD This retrospective, diagnostic study included a retrospective chart review of patients with a potential diagnosis of FBA from 2010 to 2020. Frontal view chest radiographs were extracted, processed, and uploaded to Google AutoML Vision. The developed algorithm was then evaluated against a pediatric radiologist. RESULTS The study selected 566 patients who were presented with a suspected diagnosis of foreign body aspiration. One thousand six hundred and eighty eight chest radiograph images were collected. The sensitivity and specificity of the radiologist interpretation were 50.6% (43.1-58.0) and 88.7% (85.3-91.5), respectively. The sensitivity and specificity of the algorithm were 66.7% (43.0-85.4) and 95.3% (90.6-98.1), respectively. The precision and recall of the algorithm were both 91.8% with an AuPRC of 98.3%. CONCLUSION Chest radiograph analysis augmented with machine learning can diagnose foreign body aspiration in pediatric patients at a level similar to a read performed by a pediatric radiologist despite only using single-view, fixed images. Overall, this study highlights the potential and capabilities of machine learning in diagnosing conditions with a wide range of clinical presentations. LEVEL OF EVIDENCE 3 Laryngoscope, 134:3807-3814, 2024.
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Affiliation(s)
- Brandon Truong
- School of Medicine, University of California, San Francisco, California, U.S.A
| | - Matthew Zapala
- Division of Pediatric Radiology, Department of Radiology and Biomedical Imaging, University of California, San Francisco, California, U.S.A
| | - Bamidele Kammen
- Division of Pediatric Radiology, Department of Radiology and Biomedical Imaging, University of California, San Francisco, California, U.S.A
| | - Kimberly Luu
- Division of Pediatric Otolaryngology, Department of Otolaryngology-Head and Neck Surgery, University of California, San Francisco, California, U.S.A
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11
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Ghosh Moulic A, Gaurkar SS, Deshmukh PT. Artificial Intelligence in Otology, Rhinology, and Laryngology: A Narrative Review of Its Current and Evolving Picture. Cureus 2024; 16:e66036. [PMID: 39224718 PMCID: PMC11366564 DOI: 10.7759/cureus.66036] [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: 06/13/2024] [Accepted: 08/02/2024] [Indexed: 09/04/2024] Open
Abstract
With technological advancements, artificial intelligence (AI) has progressed to become a ubiquitous part of human life. Its aspects in otorhinolaryngology are varied and are continuously evolving. Currently, AI has applications in hearing aids, imaging technologies, interpretation of auditory brain stem systems, and many more in otology. In rhinology, AI is seen to impact navigation, robotic surgeries, and the determination of various anomalies. Detection of voice pathologies and imaging are some areas of laryngology where AI is being used. This review gives an outlook on the diverse elements, applications, and advancements of AI in otorhinolaryngology. The various subfields of AI including machine learning, neural networks, and deep learning are also discussed. Clinical integration of AI and otorhinolaryngology has immense potential to revolutionize the healthcare system and improve the standards of patient care. The current applications of AI and its future scopes in developing this field are highlighted in this review.
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Affiliation(s)
- Ayushi Ghosh Moulic
- Otolaryngology - Head and Neck Surgery, Jawaharlal Nehru Medical College, Datta Meghe Institute of Higher Education and Research, Wardha, IND
| | - Sagar S Gaurkar
- Otolaryngology - Head and Neck Surgery, Jawaharlal Nehru Medical College, Datta Meghe Institute of Higher Education and Research, Wardha, IND
| | - Prasad T Deshmukh
- Otolaryngology - Head and Neck Surgery, Jawaharlal Nehru Medical College, Datta Meghe Institute of Higher Education and Research, Wardha, IND
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12
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Fung E, Patel D, Tatum S. Artificial intelligence in maxillofacial and facial plastic and reconstructive surgery. Curr Opin Otolaryngol Head Neck Surg 2024; 32:257-262. [PMID: 38837245 DOI: 10.1097/moo.0000000000000983] [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: 06/07/2024]
Abstract
PURPOSE OF REVIEW To provide a current review of artificial intelligence and its subtypes in maxillofacial and facial plastic surgery including a discussion of implications and ethical concerns. RECENT FINDINGS Artificial intelligence has gained popularity in recent years due to technological advancements. The current literature has begun to explore the use of artificial intelligence in various medical fields, but there is limited contribution to maxillofacial and facial plastic surgery due to the wide variance in anatomical facial features as well as subjective influences. In this review article, we found artificial intelligence's roles, so far, are to automatically update patient records, produce 3D models for preoperative planning, perform cephalometric analyses, and provide diagnostic evaluation of oropharyngeal malignancies. SUMMARY Artificial intelligence has solidified a role in maxillofacial and facial plastic surgery within the past few years. As high-quality databases expand with more patients, the role for artificial intelligence to assist in more complicated and unique cases becomes apparent. Despite its potential, ethical questions have been raised that should be noted as artificial intelligence continues to thrive. These questions include concerns such as compromise of the physician-patient relationship and healthcare justice.
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Affiliation(s)
| | | | - Sherard Tatum
- Department of Otolaryngology
- Department of Pediatrics, SUNY Upstate Medical University, Syracuse, New York, USA
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13
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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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14
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Torborg SR, Kim AYE, Rameau A. New developments in the application of artificial intelligence to laryngology. Curr Opin Otolaryngol Head Neck Surg 2024:00020840-990000000-00141. [PMID: 39146248 DOI: 10.1097/moo.0000000000000999] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 08/17/2024]
Abstract
PURPOSE OF REVIEW The purpose of this review is to summarize the existing literature on artificial intelligence technology utilization in laryngology, highlighting recent advances and current barriers to implementation. RECENT FINDINGS The volume of publications studying applications of artificial intelligence in laryngology has rapidly increased, demonstrating a strong interest in utilizing this technology. Vocal biomarkers for disease screening, deep learning analysis of videolaryngoscopy for lesion identification, and auto-segmentation of videofluoroscopy for detection of aspiration are a few of the new ways in which artificial intelligence is poised to transform clinical care in laryngology. Increasing collaboration is ongoing to establish guidelines and standards for the field to ensure generalizability. SUMMARY Artificial intelligence tools have the potential to greatly advance laryngology care by creating novel screening methods, improving how data-heavy diagnostics of laryngology are analyzed, and standardizing outcome measures. However, physician and patient trust in artificial intelligence must improve for the technology to be successfully implemented. Additionally, most existing studies lack large and diverse datasets, external validation, and consistent ground-truth references necessary to produce generalizable results. Collaborative, large-scale studies will fuel technological innovation and bring artificial intelligence to the forefront of patient care in laryngology.
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Affiliation(s)
- Stefan R Torborg
- Sean Parker Institute for the Voice, Department of Otolaryngology-Head and Neck Surgery, Weill Cornell Medicine
- Weill Cornell/Rockefeller/Sloan Kettering Tri-Institutional MD-PhD Program, New York, New York, USA
| | - Ashley Yeo Eun Kim
- Sean Parker Institute for the Voice, Department of Otolaryngology-Head and Neck Surgery, Weill Cornell Medicine
| | - Anaïs Rameau
- Sean Parker Institute for the Voice, Department of Otolaryngology-Head and Neck Surgery, Weill Cornell Medicine
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15
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Edalati S, Vasan V, Cheng CP, Patel Z, Govindaraj S, Iloreta AM. Can GPT-4 revolutionize otolaryngology? Navigating opportunities and ethical considerations. Am J Otolaryngol 2024; 45:104303. [PMID: 38678799 DOI: 10.1016/j.amjoto.2024.104303] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/10/2024] [Accepted: 04/14/2024] [Indexed: 05/01/2024]
Abstract
Otolaryngologists can enhance workflow efficiency, provide better patient care, and advance medical research and education by integrating artificial intelligence (AI) into their practices. GPT-4 technology is a revolutionary and contemporary example of AI that may apply to otolaryngology. The knowledge of otolaryngologists should be supplemented, not replaced when using GPT-4 to make critical medical decisions and provide individualized patient care. In our thorough examination, we explore the potential uses of the groundbreaking GPT-4 technology in the field of otolaryngology, covering aspects such as potential outcomes and technical boundaries. Additionally, we delve into the intricate and intellectually challenging dilemmas that emerge when incorporating GPT-4 into otolaryngology, considering the ethical considerations inherent in its implementation. Our stance is that GPT-4 has the potential to be very helpful. Its capabilities, which include aid in clinical decision-making, patient care, and administrative job automation, present exciting possibilities for enhancing patient outcomes, boosting the efficiency of healthcare delivery, and enhancing patient experiences. Even though there are still certain obstacles and limitations, the progress made so far shows that GPT-4 can be a valuable tool for modern medicine. GPT-4 may play a more significant role in clinical practice as technology develops, helping medical professionals deliver high-quality care tailored to every patient's unique needs.
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Affiliation(s)
- Shaun Edalati
- Department of Otolaryngology-Head and Neck Surgery, Icahn School of Medicine at Mount Sinai, New York, NY, USA.
| | - Vikram Vasan
- Department of Otolaryngology-Head and Neck Surgery, Icahn School of Medicine at Mount Sinai, New York, NY, USA
| | - Christopher P Cheng
- Department of Otolaryngology-Head and Neck Surgery, Icahn School of Medicine at Mount Sinai, New York, NY, USA
| | - Zara Patel
- Department of Otolaryngology-Head & Neck Surgery, Stanford University School of Medicine, Stanford, CA, USA
| | - Satish Govindaraj
- Department of Otolaryngology-Head and Neck Surgery, Icahn School of Medicine at Mount Sinai, New York, NY, USA
| | - Alfred Marc Iloreta
- Department of Otolaryngology-Head and Neck Surgery, Icahn School of Medicine at Mount Sinai, New York, NY, USA
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Wang CT, Chen TM, Lee NT, Fang SH. AI Detection of Glottic Neoplasm Using Voice Signals, Demographics, and Structured Medical Records. Laryngoscope 2024. [PMID: 38864282 DOI: 10.1002/lary.31563] [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: 09/28/2023] [Revised: 04/16/2024] [Accepted: 05/21/2024] [Indexed: 06/13/2024]
Abstract
OBJECTIVE This study investigated whether artificial intelligence (AI) models combining voice signals, demographics, and structured medical records can detect glottic neoplasm from benign voice disorders. METHODS We used a primary dataset containing 2-3 s of vowel "ah", demographics, and 26 items of structured medical records (e.g., symptoms, comorbidity, smoking and alcohol consumption, vocal demand) from 60 patients with pathology-proved glottic neoplasm (i.e., squamous cell carcinoma, carcinoma in situ, and dysplasia) and 1940 patients with benign voice disorders. The validation dataset comprised data from 23 patients with glottic neoplasm and 1331 patients with benign disorders. The AI model combined convolutional neural networks, gated recurrent units, and attention layers. We used 10-fold cross-validation (training-validation-testing: 8-1-1) and preserved the percentage between neoplasm and benign disorders in each fold. RESULTS Results from the AI model using voice signals reached an area under the ROC curve (AUC) value of 0.631, and additional demographics increased this to 0.807. The highest AUC of 0.878 was achieved when combining voice, demographics, and medical records (sensitivity: 0.783, specificity: 0.816, accuracy: 0.815). External validation yielded an AUC value of 0.785 (voice plus demographics; sensitivity: 0.739, specificity: 0.745, accuracy: 0.745). Subanalysis showed that AI had higher sensitivity but lower specificity than human assessment (p < 0.01). The accuracy of AI detection with additional medical records was comparable with human assessment (82% vs. 83%, p = 0.78). CONCLUSIONS Voice signal alone was insufficient for AI differentiation between glottic neoplasm and benign voice disorders, but additional demographics and medical records notably improved AI performance and approximated the prediction accuracy of humans. LEVEL OF EVIDENCE NA Laryngoscope, 2024.
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Affiliation(s)
- Chi-Te Wang
- Department of Otolaryngology Head and Neck Surgery, Far Eastern Memorial Hospital, Taipei, Taiwan
- Center of Artificial Intelligence, Far Eastern Memorial Hospital, Taipei, Taiwan
- Department of Electrical Engineering, Yuan Ze University, Taoyuan, Taiwan
| | - Tsai-Min Chen
- Graduate Program of Data Science, National Taiwan University and Academia Sinica, Taipei, Taiwan
- Research Center for Information Technology Innovation, Academia Sinica, Taipei, Taiwan
| | - Nien-Ting Lee
- Center of Artificial Intelligence, Far Eastern Memorial Hospital, Taipei, Taiwan
| | - Shih-Hau Fang
- Department of Electrical Engineering, Yuan Ze University, Taoyuan, Taiwan
- Department of Electrical Engineering, National Taiwan Normal University, Taipei, Taiwan
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Chen H, Ma X, Rives H, Serpedin A, Yao P, Rameau A. Trust in Machine Learning Driven Clinical Decision Support Tools Among Otolaryngologists. Laryngoscope 2024; 134:2799-2804. [PMID: 38230948 DOI: 10.1002/lary.31260] [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: 08/16/2023] [Revised: 11/29/2023] [Accepted: 12/20/2023] [Indexed: 01/18/2024]
Abstract
BACKGROUND Machine learning driven clinical decision support tools (ML-CDST) are on the verge of being integrated into clinical settings, including in Otolaryngology-Head & Neck Surgery. In this study, we investigated whether such CDST may influence otolaryngologists' diagnostic judgement. METHODS Otolaryngologists were recruited virtually across the United States for this experiment on human-AI interaction. Participants were shown 12 different video-stroboscopic exams from patients with previously diagnosed laryngopharyngeal reflux or vocal fold paresis and asked to determine the presence of disease. They were then exposed to a random diagnosis purportedly resulting from an ML-CDST and given the opportunity to revise their diagnosis. The ML-CDST output was presented with no explanation, a general explanation, or a specific explanation of its logic. The ML-CDST impact on diagnostic judgement was assessed with McNemar's test. RESULTS Forty-five participants were recruited. When participants reported less confidence (268 observations), they were significantly (p = 0.001) more likely to change their diagnostic judgement after exposure to ML-CDST output compared to when they reported more confidence (238 observations). Participants were more likely to change their diagnostic judgement when presented with a specific explanation of the CDST logic (p = 0.048). CONCLUSIONS Our study suggests that otolaryngologists are susceptible to accepting ML-CDST diagnostic recommendations, especially when less confident. Otolaryngologists' trust in ML-CDST output is increased when accompanied with a specific explanation of its logic. LEVEL OF EVIDENCE 2 Laryngoscope, 134:2799-2804, 2024.
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Affiliation(s)
- Hannah Chen
- Sean Parker Institute for the Voice, Department of Otolaryngology-Head and Neck Surgery, Weill Cornell Medicine, New York, New York, USA
| | - Xiaoyue Ma
- Division of Biostatistics, Department of Population Health Sciences, Weill Cornell Medical College, New York, New York, USA
| | - Hal Rives
- Sean Parker Institute for the Voice, Department of Otolaryngology-Head and Neck Surgery, Weill Cornell Medicine, New York, New York, USA
| | - Aisha Serpedin
- Sean Parker Institute for the Voice, Department of Otolaryngology-Head and Neck Surgery, Weill Cornell Medicine, New York, New York, USA
| | - Peter Yao
- Sean Parker Institute for the Voice, Department of Otolaryngology-Head and Neck Surgery, Weill Cornell Medicine, New York, New York, USA
| | - Anaïs Rameau
- Sean Parker Institute for the Voice, Department of Otolaryngology-Head and Neck Surgery, Weill Cornell Medicine, New York, New York, USA
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Khajonklin T, Sun YM, Leon Guo YL, Hsu HI, Yoon CS, Lin CY, Tsai PJ. Utilizing Artificial Neural Networks for Establishing Hearing-Loss Predicting Models Based on a Longitudinal Dataset and Their Implications for Managing the Hearing Conservation Program. Saf Health Work 2024; 15:220-227. [PMID: 39035795 PMCID: PMC11255955 DOI: 10.1016/j.shaw.2024.02.004] [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: 10/03/2023] [Revised: 02/02/2024] [Accepted: 02/18/2024] [Indexed: 07/23/2024] Open
Abstract
Background Though the artificial neural network (ANN) technique has been used to predict noise-induced hearing loss (NIHL), the established prediction models have primarily relied on cross-sectional datasets, and hence, they may not comprehensively capture the chronic nature of NIHL as a disease linked to long-term noise exposure among workers. Methods A comprehensive dataset was utilized, encompassing eight-year longitudinal personal hearing threshold levels (HTLs) as well as information on seven personal variables and two environmental variables to establish NIHL predicting models through the ANN technique. Three subdatasets were extracted from the afirementioned comprehensive dataset to assess the advantages of the present study in NIHL predictions. Results The dataset was gathered from 170 workers employed in a steel-making industry, with a median cumulative noise exposure and HTL of 88.40 dBA-year and 19.58 dB, respectively. Utilizing the longitudinal dataset demonstrated superior prediction capabilities compared to cross-sectional datasets. Incorporating the more comprehensive dataset led to improved NIHL predictions, particularly when considering variables such as noise pattern and use of personal protective equipment. Despite fluctuations observed in the measured HTLs, the ANN predicting models consistently revealed a discernible trend. Conclusions A consistent correlation was observed between the measured HTLs and the results obtained from the predicting models. However, it is essential to exercise caution when utilizing the model-predicted NIHLs for individual workers due to inherent personal fluctuations in HTLs. Nonetheless, these ANN models can serve as a valuable reference for the industry in effectively managing its hearing conservation program.
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Affiliation(s)
- Thanawat Khajonklin
- Department of Environmental and Occupational Health, College of Medicine, National Cheng Kung University, Tainan City, Taiwan
| | - Yih-Min Sun
- Department of Occupational Safety and Health, Chung Hwa University of Medical Technology, Tainan County, Taiwan
| | - Yue-Liang Leon Guo
- Department of Environmental and Occupational Medicine, Medical College, National Taiwan University, Taipei City, Taiwan
| | - Hsin-I Hsu
- Environmental and Labor Affairs Division, Southern Taiwan Science Park Bureau, Ministry of Science and Technology, Tainan City, Taiwan
| | - Chung Sik Yoon
- Department of Environmental Health Sciences, Seoul National University Graduate School of Public Health, Seoul, Republic of Korea
| | - Cheng-Yu Lin
- Department of Otolaryngology, National Cheng Kung University Hospital, College of Medicine, National Cheng Kung University, Tainan City, Taiwan
| | - Perng-Jy Tsai
- Department of Environmental and Occupational Health, College of Medicine, National Cheng Kung University, Tainan City, Taiwan
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Li R, Zheng Z, Yang L, Li S, Qin S, Xu S, Wu C, Wang W. Development of a Machine Learning Algorithm to Forecast the Likelihood of Postoperative Neurological Complications in Patients With Parotid Tumors. EAR, NOSE & THROAT JOURNAL 2024:1455613241258648. [PMID: 38804648 DOI: 10.1177/01455613241258648] [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: 05/29/2024] Open
Abstract
Objective: The objective of this study was to create and verify a machine learning-driven predictive model to forecast the likelihood of facial nerve impairment in patients with parotid tumors following surgery. Methods: We retrospectively collected data from patients with parotid tumors between 2013 and 2023 to develop a prediction model for postoperative facial nerve dysfunction using 5 ML techniques: Logistic Regression (Logit), Random Forest (RF), XGBoost (XGB), Artificial Neural Network (ANN), and Support Vector Machine (SVM). Predictor variables were screened using binomial-LASSO regression. Results: The study had a total of 403 participants, out of which 56 individuals encountered facial nerve damage after the surgery. By employing binomial-LASSO regression, we have successfully identified 8 crucial predictive variables: tumor kind, tumor pain, surgeon's experience, tumor volume, basophil percentage, red blood cell count, partial thromboplastin time, and prothrombin time. The models utilizing ANN and Logit achieved higher area under the curve (AUC) values, namely 0.829, which was significantly better than the SVM model that had an AUC of 0.724. There were no noticeable disparities in the AUC values between the ANN and Logit models, as well as between these models and other techniques like RF and XGB. Conclusion: Using machine learning, our prediction model accurately predicts the likelihood that patients with parotid tumors may experience facial nerve damage following surgery. By using this model, doctors can assess patients' risks more accurately before to surgery, and it may also help optimize postoperative treatment techniques. It is anticipated that this tool would enhance patients' quality of life and therapeutic outcomes.
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Affiliation(s)
- Ruilin Li
- Guangxi University of Chinese Medicine, Nanning, Guangxi, China
| | - Zhanhang Zheng
- Guangxi University of Chinese Medicine, Nanning, Guangxi, China
| | - Lianzhao Yang
- Guangxi University of Chinese Medicine, Nanning, Guangxi, China
| | - Shuimei Li
- Guigang City People's Hospital, Guigang, Guangxi, China
| | - Shuhong Qin
- Guangxi University of Chinese Medicine, Nanning, Guangxi, China
| | - Sujuan Xu
- Guigang City People's Hospital, Guigang, Guangxi, China
| | - Chenxingzi Wu
- Guangxi University of Chinese Medicine, Nanning, Guangxi, China
| | - Wenjuan Wang
- Guangxi University of Chinese Medicine, Nanning, Guangxi, China
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Pruthi N, Yap T, Moore C, Cirillo N, McCullough MJ. Applying Machine Learning for Enhanced MicroRNA Analysis: A Companion Risk Tool for Oral Squamous Cell Carcinoma in Standard Care Incisional Biopsy. Biomolecules 2024; 14:458. [PMID: 38672474 PMCID: PMC11048287 DOI: 10.3390/biom14040458] [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: 02/11/2024] [Revised: 04/04/2024] [Accepted: 04/06/2024] [Indexed: 04/28/2024] Open
Abstract
Machine learning analyses within the realm of oral cancer outcomes are relatively underexplored compared to other cancer types. This study aimed to assess the performance of machine learning algorithms in identifying oral cancer patients, utilizing microRNA expression data. In this study, we implemented this approach using a panel of oral cancer-associated microRNAs sourced from standard incisional biopsy specimens to identify cases of oral squamous cell carcinomas (OSCC). For the model development process, we used a dataset comprising 30 OSCC and 30 histologically normal epithelium (HNE) cases. We initially trained a logistic regression prediction model using 70 percent of the dataset, while reserving the remaining 30 percent for testing. Subsequently, the model underwent hyperparameter tuning resulting in enhanced performance metrics. The hyperparameter-tuned model exhibited high accuracy (0.894) and ROC AUC (0.898) in predicting OSCC. Testing the model on cases of potentially malignant disorders (OPMDs) revealed that leukoplakia with mild dysplasia was predicted as having a high risk of progressing to OSCC, emphasizing machine learning's advantage over histopathology in detecting early molecular changes. These findings underscore the necessity for further refinement, incorporating a broader set of variables to enhance the model's predictive capabilities in assessing the risk of oral potentially malignant disorders.
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Affiliation(s)
| | | | | | | | - Michael J. McCullough
- Melbourne Dental School, The University of Melbourne, Carlton, VIC 3053, Australia; (N.P.); (T.Y.); (C.M.); (N.C.)
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Giannitto C, Carnicelli G, Lusi S, Ammirabile A, Casiraghi E, De Virgilio A, Esposito AA, Farina D, Ferreli F, Franzese C, Frigerio GM, Lo Casto A, Malvezzi L, Lorini L, Othman AE, Preda L, Scorsetti M, Bossi P, Mercante G, Spriano G, Balzarini L, Francone M. The Use of Artificial Intelligence in Head and Neck Cancers: A Multidisciplinary Survey. J Pers Med 2024; 14:341. [PMID: 38672968 PMCID: PMC11050769 DOI: 10.3390/jpm14040341] [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: 02/19/2024] [Revised: 03/19/2024] [Accepted: 03/21/2024] [Indexed: 04/28/2024] Open
Abstract
Artificial intelligence (AI) approaches have been introduced in various disciplines but remain rather unused in head and neck (H&N) cancers. This survey aimed to infer the current applications of and attitudes toward AI in the multidisciplinary care of H&N cancers. From November 2020 to June 2022, a web-based questionnaire examining the relationship between AI usage and professionals' demographics and attitudes was delivered to different professionals involved in H&N cancers through social media and mailing lists. A total of 139 professionals completed the questionnaire. Only 49.7% of the respondents reported having experience with AI. The most frequent AI users were radiologists (66.2%). Significant predictors of AI use were primary specialty (V = 0.455; p < 0.001), academic qualification and age. AI's potential was seen in the improvement of diagnostic accuracy (72%), surgical planning (64.7%), treatment selection (57.6%), risk assessment (50.4%) and the prediction of complications (45.3%). Among participants, 42.7% had significant concerns over AI use, with the most frequent being the 'loss of control' (27.6%) and 'diagnostic errors' (57.0%). This survey reveals limited engagement with AI in multidisciplinary H&N cancer care, highlighting the need for broader implementation and further studies to explore its acceptance and benefits.
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Affiliation(s)
- Caterina Giannitto
- Department of Biomedical Sciences, Humanitas University, Pieve Emanuele, 20072 Milan, Italy (G.M.F.); (L.L.); (P.B.)
- Department of Diagnostic and Interventional Radiology, IRCCS Humanitas Research Hospital, Via Manzoni 56, 20089 Milan, Italy
| | - Giorgia Carnicelli
- Department of Biomedical Sciences, Humanitas University, Pieve Emanuele, 20072 Milan, Italy (G.M.F.); (L.L.); (P.B.)
- Department of Diagnostic and Interventional Radiology, IRCCS Humanitas Research Hospital, Via Manzoni 56, 20089 Milan, Italy
| | - Stefano Lusi
- Department of Biomedical Sciences, Humanitas University, Pieve Emanuele, 20072 Milan, Italy (G.M.F.); (L.L.); (P.B.)
- Department of Diagnostic and Interventional Radiology, IRCCS Humanitas Research Hospital, Via Manzoni 56, 20089 Milan, Italy
| | - Angela Ammirabile
- Department of Biomedical Sciences, Humanitas University, Pieve Emanuele, 20072 Milan, Italy (G.M.F.); (L.L.); (P.B.)
- Department of Diagnostic and Interventional Radiology, IRCCS Humanitas Research Hospital, Via Manzoni 56, 20089 Milan, Italy
| | - Elena Casiraghi
- Department of Computer Science “Giovanni degli Antoni”, University of Milan, Via Celoria 18, 20133 Milan, Italy;
- Environmental Genomics and Systems Biology Division, Lawrence Berkeley National Laboratory, 717 Potter Street, Berkeley, CA 94710, USA
| | - Armando De Virgilio
- Department of Biomedical Sciences, Humanitas University, Pieve Emanuele, 20072 Milan, Italy (G.M.F.); (L.L.); (P.B.)
- Otorhinolaryngology Unit, IRCCS Humanitas Research Hospital, Via Manzoni 56, 20089 Milan, Italy
| | | | - Davide Farina
- Department of Medical and Surgical Specialties, Radiological Sciences and Public Health, University of Brescia ASST Spedali Civili of Brescia, 25123 Brescia, Italy;
| | - Fabio Ferreli
- Department of Biomedical Sciences, Humanitas University, Pieve Emanuele, 20072 Milan, Italy (G.M.F.); (L.L.); (P.B.)
- Otorhinolaryngology Unit, IRCCS Humanitas Research Hospital, Via Manzoni 56, 20089 Milan, Italy
| | - Ciro Franzese
- Department of Biomedical Sciences, Humanitas University, Pieve Emanuele, 20072 Milan, Italy (G.M.F.); (L.L.); (P.B.)
- Department of Radiotherapy and Radiosurgery IRCCS Humanitas Research Hospital, Via Manzoni 56, 20089 Milan, Italy
| | - Gian Marco Frigerio
- Department of Biomedical Sciences, Humanitas University, Pieve Emanuele, 20072 Milan, Italy (G.M.F.); (L.L.); (P.B.)
- Department of Diagnostic and Interventional Radiology, IRCCS Humanitas Research Hospital, Via Manzoni 56, 20089 Milan, Italy
| | - Antonio Lo Casto
- Department of Biomedicine, Neuroscience and Advanced Diagnostics (BIND), University Hospital of Palermo, 90127 Palermo, Italy;
| | - Luca Malvezzi
- Department of Biomedical Sciences, Humanitas University, Pieve Emanuele, 20072 Milan, Italy (G.M.F.); (L.L.); (P.B.)
- Otorhinolaryngology Unit, IRCCS Humanitas Research Hospital, Via Manzoni 56, 20089 Milan, Italy
| | - Luigi Lorini
- Department of Biomedical Sciences, Humanitas University, Pieve Emanuele, 20072 Milan, Italy (G.M.F.); (L.L.); (P.B.)
- Medical Oncology and Hematology Unit IRCCS Humanitas Research Hospital, Via Manzoni 56, 20089 Milan, Italy
| | - Ahmed E. Othman
- Department of Neuroradiology, University Medical Center Mainz, 55131 Mainz, Germany;
| | - Lorenzo Preda
- Radiology Unit, Department of Clinical, Surgical, Diagnostic and Pediatric Sciences, University of Pavia, 27100 Pavia, Italy;
| | - Marta Scorsetti
- Department of Biomedical Sciences, Humanitas University, Pieve Emanuele, 20072 Milan, Italy (G.M.F.); (L.L.); (P.B.)
- Department of Radiotherapy and Radiosurgery IRCCS Humanitas Research Hospital, Via Manzoni 56, 20089 Milan, Italy
| | - Paolo Bossi
- Department of Biomedical Sciences, Humanitas University, Pieve Emanuele, 20072 Milan, Italy (G.M.F.); (L.L.); (P.B.)
- Otorhinolaryngology Unit, IRCCS Humanitas Research Hospital, Via Manzoni 56, 20089 Milan, Italy
| | - Giuseppe Mercante
- Department of Biomedical Sciences, Humanitas University, Pieve Emanuele, 20072 Milan, Italy (G.M.F.); (L.L.); (P.B.)
- Otorhinolaryngology Unit, IRCCS Humanitas Research Hospital, Via Manzoni 56, 20089 Milan, Italy
| | - Giuseppe Spriano
- Department of Biomedical Sciences, Humanitas University, Pieve Emanuele, 20072 Milan, Italy (G.M.F.); (L.L.); (P.B.)
- Otorhinolaryngology Unit, IRCCS Humanitas Research Hospital, Via Manzoni 56, 20089 Milan, Italy
| | - Luca Balzarini
- Department of Diagnostic and Interventional Radiology, IRCCS Humanitas Research Hospital, Via Manzoni 56, 20089 Milan, Italy
| | - Marco Francone
- Department of Biomedical Sciences, Humanitas University, Pieve Emanuele, 20072 Milan, Italy (G.M.F.); (L.L.); (P.B.)
- Department of Diagnostic and Interventional Radiology, IRCCS Humanitas Research Hospital, Via Manzoni 56, 20089 Milan, Italy
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Zeitler DM, Buchlak QD, Ramasundara S, Farrokhi F, Esmaili N. Predicting Acoustic Hearing Preservation Following Cochlear Implant Surgery Using Machine Learning. Laryngoscope 2024; 134:926-936. [PMID: 37449725 DOI: 10.1002/lary.30894] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/10/2023] [Revised: 05/24/2023] [Accepted: 07/02/2023] [Indexed: 07/18/2023]
Abstract
OBJECTIVES The aim of the study was to train and test supervised machine-learning classifiers to predict acoustic hearing preservation after CI using preoperative clinical data. STUDY DESIGN Retrospective predictive modeling study of prospectively collected single-institution CI dataset. METHODS One hundred and seventy-five patients from a REDCap database including 761 patients >18 years who underwent CI and had audiometric testing preoperatively and one month after surgery were included. The primary outcome variable was the lowest quartile change in acoustic hearing at one month after CI using various formulae (standard pure tone average, SPTA; low-frequency PTA, LFPTA). Analysis involved applying multivariate logistic regression to detect statistical associations and training and testing supervised learning classifiers. Classifier performance was assessed with numerous metrics including area under the receiver operating characteristic curve (AUC) and Matthews correlation coefficient (MCC). RESULTS Lowest quartile change (indicating hearing preservation) in SPTA was positively associated with a history of meningitis, preoperative LFPTA, and preoperative SPTA. Lowest quartile change in SPTA was negatively associated with sudden hearing loss, noise exposure, aural fullness, and abnormal anatomy. Lowest quartile change in LFPTA was positively associated with preoperative LFPTA. Lowest quartile change in LFPTA was negatively associated with tobacco use. Random forest demonstrated the highest mean classification performance on the validation dataset when predicting each of the outcome variables. CONCLUSIONS Machine learning demonstrated utility for predicting preservation of residual acoustic hearing in patients undergoing CI surgery, and the detected associations facilitated the interpretation of our machine-learning models. The models and statistical associations together may be used to facilitate improvements in shared clinical decision-making and patient outcomes. LEVEL OF EVIDENCE 3 Laryngoscope, 134:926-936, 2024.
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Affiliation(s)
- Daniel M Zeitler
- Neuroscience Institute, Virginia Mason Franciscan Health, Seattle, Washington, USA
- Department of Otolaryngology-Head Neck Surgery, Section of Otology/Neurotology, Virginia Mason Franciscan Health, Seattle, Washington, USA
| | - Quinlan D Buchlak
- School of Medicine, University of Notre Dame Australia, Sydney, New South Wales, Australia
- Department of Neurosurgery, Monash Health, Melbourne, Victoria, Australia
| | - Savindi Ramasundara
- School of Medicine, University of Notre Dame Australia, Sydney, New South Wales, Australia
| | - Farrokh Farrokhi
- Neuroscience Institute, Virginia Mason Franciscan Health, Seattle, Washington, USA
- Department of Neurosurgery, Virginia Mason Franciscan Health, Seattle, Washington, USA
| | - Nazanin Esmaili
- School of Medicine, University of Notre Dame Australia, Sydney, New South Wales, Australia
- Faculty of Engineering and Information Technology, University of Technology Sydney, Sydney, New South Wales, Australia
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Hidaka T, Miyamoto S, Furuse K, Oshima A, Matsuura K, Higashino T. Machine learning approach to predict tracheal necrosis after total pharyngolaryngectomy. Head Neck 2024; 46:408-416. [PMID: 38088269 DOI: 10.1002/hed.27598] [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: 07/31/2023] [Revised: 11/06/2023] [Accepted: 12/03/2023] [Indexed: 01/13/2024] Open
Abstract
BACKGROUND Tracheal necrosis is a potentially severe complication of total pharyngolarynjectomy (TPL), sometimes combined with total esophagectomy. The risk factors for tracheal necrosis after TPL without total esophagectomy remain unknown. METHODS We retrospectively reviewed data of 395 patients who underwent TPL without total esophagectomy. Relevant factors associated with tracheal necrosis were evaluated using random forest machine learning and traditional multivariable logistic regression models. RESULTS Tracheal necrosis occurred in 25 (6.3%) patients. Both the models identified almost the same factors relevant to tracheal necrosis. History of radiotherapy was the most important predicting and significant risk factor in both models. Paratracheal lymph node dissection and total thyroidectomy with TPL were also relevant. Random forest model was able to predict tracheal necrosis with an accuracy of 0.927. CONCLUSIONS Random forest is useful in predicting tracheal necrosis. Countermeasures should be considered when creating a tracheostoma, particularly in patients with identified risk factors.
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Affiliation(s)
- Takeaki Hidaka
- Department of Plastic and Reconstructive Surgery, National Cancer Center Hospital East, Kashiwa, Japan
| | - Shimpei Miyamoto
- Department of Plastic, Reconstructive and Aesthetic Surgery, Graduate School of Medicine, The University of Tokyo, Hongo, Japan
| | - Kiichi Furuse
- Department of Plastic and Reconstructive Surgery, National Cancer Center Hospital East, Kashiwa, Japan
| | - Azusa Oshima
- Department of Plastic and Reconstructive Surgery, National Cancer Center Hospital East, Kashiwa, Japan
| | - Kazuto Matsuura
- Department of Head and Neck Surgery, National Cancer Center Hospital East, Kashiwa, Japan
| | - Takuya Higashino
- Department of Plastic and Reconstructive Surgery, National Cancer Center Hospital East, Kashiwa, Japan
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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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Chiesa‐Estomba CM, González‐García JA, Larruscain E, Sistiaga Suarez JA, Quer M, León X, Martínez‐Ruiz de Apodaca P, López‐Mollá C, Mayo‐Yanez M, Medela A. Facial nerve palsy following parotid gland surgery: A machine learning prediction outcome approach. World J Otorhinolaryngol Head Neck Surg 2023; 9:271-279. [PMID: 38059137 PMCID: PMC10696266 DOI: 10.1002/wjo2.94] [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: 07/15/2022] [Revised: 11/27/2022] [Accepted: 12/16/2022] [Indexed: 04/03/2023] Open
Abstract
Introduction Machine learning (ML)-based facial nerve injury (FNI) forecasting grounded on multicentric data has not been released up to now. Three distinct ML models, random forest (RF), K-nearest neighbor, and artificial neural network (ANN), for the prediction of FNI were evaluated in this mode. Methods A retrospective, longitudinal, multicentric study was performed, including patients who went through parotid gland surgery for benign tumors at three different university hospitals. Results Seven hundred and thirty-six patients were included. The most compelling aspects related to risk escalation of FNI were as follows: (1) location, in the mid-portion of the gland, near to or above the main trunk of the facial nerve and at the top part, over the frontal or the orbital branch of the facial nerve; (2) tumor volume in the anteroposterior axis; (3) the necessity to simultaneously dissect more than one level; and (4) the requirement of an extended resection compared to a lesser extended resection. By contrast, in accordance with the ML analysis, the size of the tumor (>3 cm), as well as gender and age did not result in a determining favor in relation to the risk of FNI. Discussion The findings of this research conclude that ML models such as RF and ANN may serve evidence-based predictions from multicentric data regarding the risk of FNI. Conclusion Along with the advent of ML technology, an improvement of the information regarding the potential risks of FNI associated with patients before each procedure may be achieved with the implementation of clinical, radiological, histological, and/or cytological data.
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Affiliation(s)
- Carlos M. Chiesa‐Estomba
- Department of Otorhinolaryngology—Head and Neck SurgeryDonostia University HospitalDonosti‐San SebastiánSpain
- Head & Neck Study Group of Young‐Otolaryngologists of the International Federations of Oto‐rhino‐laryngological Societies (YO‐IFOS)ParisFrance
- Biodonostia Health Research InstituteSan SebastiánSpain
| | - Jose A. González‐García
- Department of Otorhinolaryngology—Head and Neck SurgeryDonostia University HospitalDonosti‐San SebastiánSpain
| | - Ekhiñe Larruscain
- Department of Otorhinolaryngology—Head and Neck SurgeryDonostia University HospitalDonosti‐San SebastiánSpain
| | - Jon A. Sistiaga Suarez
- Department of Otorhinolaryngology—Head and Neck SurgeryDonostia University HospitalDonosti‐San SebastiánSpain
| | - Miquel Quer
- Department of Otorhinolaryngology, Hospital Santa Creu I Sant PauUniversitat Autònoma de BarcelonaBarcelonaSpain
| | - Xavier León
- Department of Otorhinolaryngology, Hospital Santa Creu I Sant PauUniversitat Autònoma de BarcelonaBarcelonaSpain
| | - Paula Martínez‐Ruiz de Apodaca
- Head & Neck Study Group of Young‐Otolaryngologists of the International Federations of Oto‐rhino‐laryngological Societies (YO‐IFOS)ParisFrance
- Department of OtorhinolaryngologyDoctor Peset University HospitalValenciaSpain
| | - Celia López‐Mollá
- Department of OtorhinolaryngologyDoctor Peset University HospitalValenciaSpain
| | - Miguel Mayo‐Yanez
- Head & Neck Study Group of Young‐Otolaryngologists of the International Federations of Oto‐rhino‐laryngological Societies (YO‐IFOS)ParisFrance
- Otorhinolaryngology—Head and Neck Surgery DepartmentComplexo Hospitalario Universitario A Coruña (CHUAC)A CoruñaGaliciaSpain
- Clinical Research in Medicine, International Center for Doctorate and Advanced Studies (CIEDUS), Universidade de Santiago de, Compostela (USC)Santiago de CompostelaGaliciaSpain
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Calà F, Frassineti L, Sforza E, Onesimo R, D’Alatri L, Manfredi C, Lanata A, Zampino G. Artificial Intelligence Procedure for the Screening of Genetic Syndromes Based on Voice Characteristics. Bioengineering (Basel) 2023; 10:1375. [PMID: 38135966 PMCID: PMC10741055 DOI: 10.3390/bioengineering10121375] [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: 10/11/2023] [Revised: 11/25/2023] [Accepted: 11/27/2023] [Indexed: 12/24/2023] Open
Abstract
Perceptual and statistical evidence has highlighted voice characteristics of individuals affected by genetic syndromes that differ from those of normophonic subjects. In this paper, we propose a procedure for systematically collecting such pathological voices and developing AI-based automated tools to support differential diagnosis. Guidelines on the most appropriate recording devices, vocal tasks, and acoustical parameters are provided to simplify, speed up, and make the whole procedure homogeneous and reproducible. The proposed procedure was applied to a group of 56 subjects affected by Costello syndrome (CS), Down syndrome (DS), Noonan syndrome (NS), and Smith-Magenis syndrome (SMS). The entire database was divided into three groups: pediatric subjects (PS; individuals < 12 years of age), female adults (FA), and male adults (MA). In line with the literature results, the Kruskal-Wallis test and post hoc analysis with Dunn-Bonferroni test revealed several significant differences in the acoustical features not only between healthy subjects and patients but also between syndromes within the PS, FA, and MA groups. Machine learning provided a k-nearest-neighbor classifier with 86% accuracy for the PS group, a support vector machine (SVM) model with 77% accuracy for the FA group, and an SVM model with 84% accuracy for the MA group. These preliminary results suggest that the proposed method based on acoustical analysis and AI could be useful for an effective, non-invasive automatic characterization of genetic syndromes. In addition, clinicians could benefit in the case of genetic syndromes that are extremely rare or present multiple variants and facial phenotypes.
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Affiliation(s)
- Federico Calà
- Department of Information Engineering, University of Florence, 50139 Florence, Italy; (F.C.); (L.F.); (A.L.)
| | - Lorenzo Frassineti
- Department of Information Engineering, University of Florence, 50139 Florence, Italy; (F.C.); (L.F.); (A.L.)
- Department of Information Engineering, Università degli Studi di Pisa, 56122 Pisa, Italy
| | - Elisabetta Sforza
- Department of Life Sciences and Public Health, Faculty of Medicine and Surgery, Catholic University of Sacred Heart, 00168 Rome, Italy; (E.S.); (G.Z.)
| | - Roberta Onesimo
- Centre for Rare Diseases and Transition, Department of Woman and Child Health and Public Health, Fondazione Policlinico Universitario A. Gemelli IRCCS, 00168 Rome, Italy;
| | - Lucia D’Alatri
- Unit for Ear, Nose and Throat Medicine, Department of Neuroscience, Sensory Organs and Chest, Fondazione Policlinico Universitario A. Gemelli IRCCS, 00168 Rome, Italy;
| | - Claudia Manfredi
- Department of Information Engineering, University of Florence, 50139 Florence, Italy; (F.C.); (L.F.); (A.L.)
| | - Antonio Lanata
- Department of Information Engineering, University of Florence, 50139 Florence, Italy; (F.C.); (L.F.); (A.L.)
| | - Giuseppe Zampino
- Department of Life Sciences and Public Health, Faculty of Medicine and Surgery, Catholic University of Sacred Heart, 00168 Rome, Italy; (E.S.); (G.Z.)
- Centre for Rare Diseases and Transition, Department of Woman and Child Health and Public Health, Fondazione Policlinico Universitario A. Gemelli IRCCS, 00168 Rome, Italy;
- European Reference Network for Rare Malformation Syndromes, Intellectual and Other Neurodevelopmental Disorders—ERN ITHACA
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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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Nwosu OI, Crowson MG, Rameau A. Artificial Intelligence Governance and Otolaryngology-Head and Neck Surgery. Laryngoscope 2023; 133:2868-2870. [PMID: 37658749 PMCID: PMC10592089 DOI: 10.1002/lary.31013] [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: 08/16/2023] [Accepted: 08/18/2023] [Indexed: 09/05/2023]
Abstract
This rapid communication highlights components of artificial intelligence governance in healthcare and suggests adopting key governance approaches in otolaryngology – head and neck surgery.
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Affiliation(s)
- Obinna I. Nwosu
- Department of Otolaryngology-Head & Neck Surgery, Massachusetts Eye & Ear, Boston, Massachusetts, USA
- Department of Otolaryngology-Head & Neck Surgery, Harvard Medical School, Boston, Massachusetts, USA
| | - Matthew G. Crowson
- Department of Otolaryngology-Head & Neck Surgery, Massachusetts Eye & Ear, Boston, Massachusetts, USA
- Department of Otolaryngology-Head & Neck Surgery, Harvard Medical School, Boston, Massachusetts, USA
- Deloitte Consulting, Boston, Massachusetts, USA
| | - Anaïs Rameau
- Department of Otolaryngology–Head and Neck Surgery, Sean Parker Institute for the Voice, Weill Cornell Medical College, New York, New York, USA
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Akinosun M, Farrokhian N, Flynn J, Ovaitt A, Kriet JD, Humphrey C. Creation of Lateral View Facial Images Using Artificial Intelligence: A Feasibility Study. Facial Plast Surg Aesthet Med 2023; 25:536-538. [PMID: 37428546 DOI: 10.1089/fpsam.2022.0388] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 07/11/2023] Open
Affiliation(s)
- Moriyike Akinosun
- Department of Otolaryngology-Head and Neck Surgery, University of Kansas School of Medicine, Kansas City, Kansas, USA
| | - Nathan Farrokhian
- Department of Otolaryngology-Head and Neck Surgery, University of Kansas School of Medicine, Kansas City, Kansas, USA
| | - John Flynn
- Department of Otolaryngology-Head and Neck Surgery, University of Kansas School of Medicine, Kansas City, Kansas, USA
| | - Alyssa Ovaitt
- Department of Otolaryngology-Head and Neck Surgery, University of Kansas School of Medicine, Kansas City, Kansas, USA
| | - J David Kriet
- Department of Otolaryngology-Head and Neck Surgery, University of Kansas School of Medicine, Kansas City, Kansas, USA
| | - Clinton Humphrey
- Department of Otolaryngology-Head and Neck Surgery, University of Kansas School of Medicine, Kansas City, Kansas, USA
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30
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Loperfido A, Celebrini A, Marzetti A, Bellocchi G. Current role of artificial intelligence in head and neck cancer surgery: a systematic review of literature. EXPLORATION OF TARGETED ANTI-TUMOR THERAPY 2023; 4:933-940. [PMID: 37970203 PMCID: PMC10645467 DOI: 10.37349/etat.2023.00174] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/12/2023] [Accepted: 07/19/2023] [Indexed: 11/17/2023] Open
Abstract
Aim Artificial intelligence (AI) is a new field of science in which computers will provide decisions-supporting tools to help doctors make difficult clinical choices. Recent AI applications in otolaryngology include head and neck oncology, rhinology, neurotology, and laryngology. The aim of this systematic review is to describe the potential uses of AI in head and neck oncology with a special focus on the surgical field. Methods The authors performed a systematic review, in accordance with the Preferred Reporting Items for Systematic reviews and Meta-Analyses (PRISMA) guidelines, in the main medical databases, including PubMed, Scopus, and Cochrane Library, considering all original studies published until February 2023 about the role of AI in head and neck cancer surgery. The search strategy included a combination of the following terms: "artificial intelligence" or "machine learning" and "head and neck cancer". Results Overall, 303 papers were identified and after duplicate removal (12 papers) and excluding papers not written in English (1 paper) and off-topic (4 papers), papers were assessed for eligibility; finally, only 12 papers were included. Three main fields of clinical interest were identified: the most widely investigated included the role of AI in surgical margins assessment (7 papers); the second most frequently evaluated topic was complications assessment (4 papers); finally, only one paper dealt with the indication of salvage laryngectomy after primary radiotherapy. Conclusions The authors report the first systematic review in the literature concerning the role of AI in head and neck cancer surgery. An increasing influx of AI applications to clinical problems in otolaryngology is expected, so specialists should be increasingly prepared to manage the constant changes. It will always remain critical for clinicians to use their skills and knowledge to critically evaluate the additional information provided by AI and make the final decisions on each patient.
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Affiliation(s)
| | | | - Andrea Marzetti
- Department of Otolaryngology Head and Neck Surgery, Fabrizio Spaziani Hospital, 03100 Frosinone, Italy
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Larrow DR, Kadosh OK, Fracchia S, Radano M, Hartnick CJ. Harnessing the power of electronic health records and open natural language data mining to capture meaningful patient experience during routine clinical care. Int J Pediatr Otorhinolaryngol 2023; 173:111698. [PMID: 37597315 DOI: 10.1016/j.ijporl.2023.111698] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 07/05/2023] [Revised: 08/09/2023] [Accepted: 08/11/2023] [Indexed: 08/21/2023]
Abstract
INTRODUCTION Electronic health records (EHR) are a rich data source for both quality improvement and clinical research. Natural language processing can be harnessed to extract data from these previously difficult to access sources. OBJECTIVE The objective of this study was to create and apply a natural language search query to extract EHR data to ask and answer quality improvement questions at a pediatric aerodigestive center. METHODS We developed a combined natural language search query to extract clinically meaningful data along with International Statistical Classification of Diseases (ICD10) and Current Procedural Terminology (CPT) code data. This search query was applied to a single pediatric aerodigestive center to answer key clinical questions asked by families. Data were extracted from EHR data from first clinic visit, operative note, microbiology lab report, and pathology report for all new patients from 2020 to 2021. Included as three queries were: 1) if I bring my child to a pediatric aerodigestive center, how often will my child obtain a medical diagnosis without needing an intervention? 2) if my child has a diagnostic procedure, how often will a diagnosis be made? 3) if a diagnosis is made, can it be addressed during that endoscopic intervention? RESULTS For the 711 new patients coming to the pediatric aerodigestive center from 2020 to 2021, only 26-32% required an interventional triple endoscopy (rigid/flexible bronchoscopy with esophagoduodenoscopy). Of these triple endoscopies, 75.7% resulted in a positive finding that enabled optimization of that child's care. Of the 221 patients who underwent diagnostic triple endoscopies, 40.7% underwent intervention at the same time for laryngeal cleft (injection or suture, dependent upon age). CONCLUSION Here we created an effective model of open language search query to extract meaningful metrics of patient experience from EHR data. This model easily allows the EHR to be harnessed to create retrospective and prospective databases that can be readily queried to answer clinical questions important to patients. Such databases are widely applicable not just to pediatric aerodigestive centers but to any clinical care setting using an EHR.
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Affiliation(s)
- Danielle R Larrow
- Department of Otolaryngology-Head and Neck Surgery, Massachusetts Eye and Ear, Boston, USA; Department of Otolaryngology-Head and Neck Surgery, Harvard Medical School, Boston, MA, USA
| | - Orna Katz Kadosh
- Department of Otolaryngology-Head and Neck Surgery, Dana-Dwek Children's Hospital, Tel Aviv, Israel; Sackler School of Medicine, Tel Aviv University, Tel Aviv, Israel
| | - Shannon Fracchia
- Department of Pediatric Pulmonology, Massachusetts General Hospital, Boston, USA
| | - Marcella Radano
- Department of Pediatric Gastroenterology, Massachusetts General Hospital, Boston, USA
| | - Christopher J Hartnick
- Department of Otolaryngology-Head and Neck Surgery, Massachusetts Eye and Ear, Boston, USA; Department of Otolaryngology-Head and Neck Surgery, Harvard Medical School, Boston, MA, USA.
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Song D, Kim T, Lee Y, Kim J. Image-Based Artificial Intelligence Technology for Diagnosing Middle Ear Diseases: A Systematic Review. J Clin Med 2023; 12:5831. [PMID: 37762772 PMCID: PMC10531728 DOI: 10.3390/jcm12185831] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/28/2023] [Revised: 08/27/2023] [Accepted: 08/29/2023] [Indexed: 09/29/2023] Open
Abstract
Otolaryngological diagnoses, such as otitis media, are traditionally performed using endoscopy, wherein diagnostic accuracy can be subjective and vary among clinicians. The integration of objective tools, like artificial intelligence (AI), could potentially improve the diagnostic process by minimizing the influence of subjective biases and variability. We systematically reviewed the AI techniques using medical imaging in otolaryngology. Relevant studies related to AI-assisted otitis media diagnosis were extracted from five databases: Google Scholar, PubMed, Medline, Embase, and IEEE Xplore, without date restrictions. Publications that did not relate to AI and otitis media diagnosis or did not utilize medical imaging were excluded. Of the 32identified studies, 26 used tympanic membrane images for classification, achieving an average diagnosis accuracy of 86% (range: 48.7-99.16%). Another three studies employed both segmentation and classification techniques, reporting an average diagnosis accuracy of 90.8% (range: 88.06-93.9%). These findings suggest that AI technologies hold promise for improving otitis media diagnosis, offering benefits for telemedicine and primary care settings due to their high diagnostic accuracy. However, to ensure patient safety and optimal outcomes, further improvements in diagnostic performance are necessary.
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Affiliation(s)
- Dahye Song
- Major in Bio Artificial Intelligence, Department of Applied Artificial Intelligence, Hanyang University, Ansan 15588, Republic of Korea; (D.S.); (T.K.)
| | - Taewan Kim
- Major in Bio Artificial Intelligence, Department of Applied Artificial Intelligence, Hanyang University, Ansan 15588, Republic of Korea; (D.S.); (T.K.)
| | - Yeonjoon Lee
- Major in Bio Artificial Intelligence, Department of Applied Artificial Intelligence, Hanyang University, Ansan 15588, Republic of Korea; (D.S.); (T.K.)
| | - Jaeyoung Kim
- Department of Dermatology and Skin Sciences, University of British Columbia, Vancouver, BC V6T 1Z1, Canada;
- Core Research & Development Center, Korea University Ansan Hospital, Ansan 15355, Republic of Korea
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Costantino A, Canali L, Festa BM, Kim SH, Spriano G, De Virgilio A. Development of machine learning models to predict lymph node metastases in major salivary gland cancers. EUROPEAN JOURNAL OF SURGICAL ONCOLOGY 2023; 49:106965. [PMID: 37393130 DOI: 10.1016/j.ejso.2023.06.017] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/20/2023] [Revised: 06/11/2023] [Accepted: 06/19/2023] [Indexed: 07/03/2023]
Abstract
INTRODUCTION Indications for elective treatment of the neck in patients with major salivary gland cancers are still debated. Our purpose was to develop a machine learning (ML) model able to generate a predictive algorithm to identify lymph node metastases (LNM) in patients with major salivary gland cancer (SGC). METHODS A Retrospective study was performed with data obtained from the Surveillance, Epidemiology, and End Results (SEER) program. Patients diagnosed with a major SGC between 1988 and 2019 were included. Two 2-class supervised ML decision models (random forest, RF; extreme gradient boosting, XGB) were used to predict the presence of LNM, implementing thirteen demographics and clinical variables collected from the SEER database. A permutation feature importance (PFI) score was computed using the testing dataset to identify the most important variables used in model prediction. RESULTS A total of 10 350 patients (males: 52%; mean age: 59.9 ± 17.2 years) were included in the study. The RF and the XGB prediction models showed an overall accuracy of 0.68. Both models showed a high specificity (RF: 0.90; XGB: 0.83) and low sensitivity (RF: 0.27; XGB: 0.38) in identifying LNM. According, a high negative predictive value (RF: 0.70; XGB: 0.72) and a low positive predictive value (RF: 0.58; XGB: 0.56) were measured. T classification and tumor size were the most important features in the construction of the prediction algorithms. CONCLUSIONS Classification performance of the ML algorithms showed high specificity and negative predictive value that allow to preoperatively identify patients with a lower risk of LNM. LAY SUMMARY Based on data from the Surveillance, Epidemiology, and End Results (SEER) program, our study showed that machine learning algorithms owns a high specificity and negative predictive value, allowing to preoperatively identify patients with a lower risk of lymph node metastasis. LEVEL OF EVIDENCE: 3
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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
| | - Luca Canali
- 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.
| | - Se-Heon Kim
- Department of Otorhinolaryngology, Yonsei University College of Medicine, Seoul, South Korea
| | - 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
| | - 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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Massey CJ, Asokan A, Tietbohl C, Morris M, Ramakrishnan VR. Otolaryngologist perceptions of AI-based sinus CT interpretation. Am J Otolaryngol 2023; 44:103932. [PMID: 37245324 DOI: 10.1016/j.amjoto.2023.103932] [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: 05/01/2023] [Accepted: 05/13/2023] [Indexed: 05/30/2023]
Abstract
BACKGROUND Overcoming non-standardization, vagueness, and subjectivity in sinus CT radiology reports is an ongoing need, particularly in keeping with data-driven healthcare initiatives. Our aim was to explore otolaryngologists' perceptions of quantitative objective disease measures as enabled by AI-based analysis, and determine preferences for sinus CT interpretation. METHODS A multi-methods design was used. We administered a survey to American Rhinologic Society members and conducted semi-structured interviews with a purposeful sample of otolaryngologists and rhinologists from varying backgrounds, practice settings and locations during 2020-2021. Interview topics included sinus CT reports, familiarity with AI-based analysis, and potential requisites for its future implementation. Interviews were then coded for content analysis. Differences in survey responses were calculated using Chi-squared test. RESULTS 120 of 955 surveys were returned, and 19 otolaryngologists (8 rhinologists) were interviewed. Survey data revealed more trust in conventional radiologist reports, but that AI-based reports would be more systematic and comprehensive. Interviews expanded on these results. Interviewees believed that conventional sinus CT reports had limited utility due to inconsistent content. However, they described relying on them for reporting incidental extra-sinus findings. Reporting could be improved with standardization and more detailed anatomical analysis. Interviewees expressed interest in AI-derived analysis given potential for standardization, although they desired evidence of accuracy and reproducibility to gain trust in AI-based reports. CONCLUSIONS Sinus CT interpretation has shortcomings in its current state. Standardization and objectivity could be aided with deep learning-enabled quantitative analysis, although clinicians desire thorough validation to gain trust in the technology prior to its implementation.
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Affiliation(s)
- Conner J Massey
- Department of Otolaryngology - Head & Neck Surgery, University of Colorado School of Medicine, Aurora, CO, United States of America.
| | - Annapoorani Asokan
- University of Texas Southwestern Medical School, Dallas, TX, United States of America
| | - Caroline Tietbohl
- Department of Family Medicine, University of Colorado School of Medicine, Aurora, CO, United States of America; Qualitative and Mixed Methods Research Core, Adult and Child Center for Outcomes Research Delivery Science (ACCORDS), University of Colorado School of Medicine, Aurora, CO, United States of America
| | - Megan Morris
- Qualitative and Mixed Methods Research Core, Adult and Child Center for Outcomes Research Delivery Science (ACCORDS), University of Colorado School of Medicine, Aurora, CO, United States of America; Department of Internal Medicine, University of Colorado School of Medicine, Aurora, CO, United States of America
| | - Vijay R Ramakrishnan
- Department of Otolaryngology - Head & Neck Surgery, Indiana University School of Medicine, Indianapolis, IN, United States of America
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Chen SL, Chin SC, Chan KC, Ho CY. A Machine Learning Approach to Assess Patients with Deep Neck Infection Progression to Descending Mediastinitis: Preliminary Results. Diagnostics (Basel) 2023; 13:2736. [PMID: 37685275 PMCID: PMC10486957 DOI: 10.3390/diagnostics13172736] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/14/2023] [Revised: 07/25/2023] [Accepted: 08/22/2023] [Indexed: 09/10/2023] Open
Abstract
BACKGROUND Deep neck infection (DNI) is a serious infectious disease, and descending mediastinitis is a fatal infection of the mediastinum. However, no study has applied artificial intelligence to assess progression to descending mediastinitis in DNI patients. Thus, we developed a model to assess the possible progression of DNI to descending mediastinitis. METHODS Between August 2017 and December 2022, 380 patients with DNI were enrolled; 75% of patients (n = 285) were assigned to the training group for validation, whereas the remaining 25% (n = 95) were assigned to the test group to determine the accuracy. The patients' clinical and computed tomography (CT) parameters were analyzed via the k-nearest neighbor method. The predicted and actual progression of DNI patients to descending mediastinitis were compared. RESULTS In the training and test groups, there was no statistical significance (all p > 0.05) noted at clinical variables (age, gender, chief complaint period, white blood cells, C-reactive protein, diabetes mellitus, and blood sugar), deep neck space (parapharyngeal, submandibular, retropharyngeal, and multiple spaces involved, ≥3), tracheostomy performance, imaging parameters (maximum diameter of abscess and nearest distance from abscess to level of sternum notch), or progression to mediastinitis. The model had a predictive accuracy of 82.11% (78/95 patients), with sensitivity and specificity of 41.67% and 87.95%, respectively. CONCLUSIONS Our model can assess the progression of DNI to descending mediastinitis depending on clinical and imaging parameters. It can be used to identify DNI patients who will benefit from prompt treatment.
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Affiliation(s)
- Shih-Lung Chen
- Department of Otorhinolaryngology & Head and Neck Surgery, Chang Gung Memorial Hospital, New Taipei City 333, Taiwan
- School of Medicine, Chang Gung University, Taoyuan 333, Taiwan
| | - Shy-Chyi Chin
- School of Medicine, Chang Gung University, Taoyuan 333, Taiwan
- Department of Medical Imaging and Intervention, Chang Gung Memorial Hospital, New Taipei City 333, Taiwan
| | - Kai-Chieh Chan
- Department of Otorhinolaryngology & Head and Neck Surgery, Chang Gung Memorial Hospital, New Taipei City 333, Taiwan
- School of Medicine, Chang Gung University, Taoyuan 333, Taiwan
| | - Chia-Ying Ho
- School of Medicine, Chang Gung University, Taoyuan 333, Taiwan
- Division of Chinese Internal Medicine, Center for Traditional Chinese Medicine, Chang Gung Memorial Hospital, Taoyuan 333, Taiwan
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Petruzzi G, Coden E, Iocca O, di Maio P, Pichi B, Campo F, De Virgilio A, Francesco M, Vidiri A, Pellini R. Machine learning in laryngeal cancer: A pilot study to predict oncological outcomes and the role of adverse features. Head Neck 2023; 45:2068-2078. [PMID: 37345573 DOI: 10.1002/hed.27434] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/26/2022] [Revised: 04/27/2023] [Accepted: 06/10/2023] [Indexed: 06/23/2023] Open
Abstract
BACKGROUND Laryngeal carcinoma (LC) remains a significant economic and emotional problem to the healthcare system and severe social morbidity. New tools as Machine Learning could allow clinicians to develop accurate and reproducible treatments. METHODS This study aims to evaluate the performance of a ML-algorithm in predicting 1- and 3-year overall survival (OS) in a cohort of patients surgical treated for LC. Moreover, the impact of different adverse features on prognosis will be investigated. Data was collected on oncological FU of 132 patients. A retrospective review was performed to create a dataset of 23 variables for each patient. RESULTS The decision-tree algorithm is highly effective in predicting the prognosis, with a 95% accuracy in predicting the 1-year survival and 82.5% in 3-year survival; The measured AUC area is 0.886 at 1-year Test and 0.871 at 3-years Test. The measured AUC area is 0.917 at 1-year Training set and 0.964 at 3-years Training set. Factors that affected 1yOS are: LNR, type of surgery, and subsite. The most significant variables at 3yOS are: number of metastasis, perineural invasion and Grading. CONCLUSIONS The integration of ML in medical practices could revolutionize our approach on cancer pathology.
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Affiliation(s)
- Gerardo Petruzzi
- Department of Otolaryngology and Head and Neck Surgery, IRCCS Regina Elena National Cancer Institute, Rome, Italy
| | - Elisa Coden
- Division of Otorhinolaryngology - Head and Neck Surgery, ASST Sette Laghi, Ospedale di Circolo e Fondazione Macchi, University of Insubria, Varese, Italy
| | - Oreste Iocca
- Division of Maxillofacial Surgery, Città della Salute e della Scienza, University of Torino, Torino, Italy
| | - Pasquale di Maio
- Department of otolaryngology-Head and Neck Surgery, Giuseppe Fornaroli Hospital, ASST Ovest Milanese, Magenta, Italy
| | - Barbara Pichi
- Department of Otolaryngology and Head and Neck Surgery, IRCCS Regina Elena National Cancer Institute, Rome, Italy
| | - Flaminia Campo
- Department of Otolaryngology and Head and Neck Surgery, IRCCS Regina Elena National Cancer Institute, Rome, Italy
| | - Armando De Virgilio
- Department of Biomedical Sciences, Humanitas University, Milan, Italy
- Department of Otolaryngology and Head and Neck Surgery, IRCCS Humanitas Research Hospital, Milan, Italy
| | - Mazzola Francesco
- Department of Otolaryngology and Head and Neck Surgery, IRCCS Regina Elena National Cancer Institute, Rome, Italy
| | - Antonello Vidiri
- Department of Radiology and Diagnostic Imaging, IRCCS Regina Elena National Cancer Institute, Rome, Italy
| | - Raul Pellini
- Department of Otolaryngology and Head and Neck Surgery, IRCCS Regina Elena National Cancer Institute, Rome, Italy
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Amanian A, Heffernan A, Ishii M, Creighton FX, Thamboo A. The Evolution and Application of Artificial Intelligence in Rhinology: A State of the Art Review. Otolaryngol Head Neck Surg 2023; 169:21-30. [PMID: 35787221 PMCID: PMC11110957 DOI: 10.1177/01945998221110076] [Citation(s) in RCA: 4] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/31/2022] [Accepted: 06/10/2022] [Indexed: 11/16/2022]
Abstract
OBJECTIVE To provide a comprehensive overview on the applications of artificial intelligence (AI) in rhinology, highlight its limitations, and propose strategies for its integration into surgical practice. DATA SOURCES Medline, Embase, CENTRAL, Ei Compendex, IEEE, and Web of Science. REVIEW METHODS English studies from inception until January 2022 and those focusing on any application of AI in rhinology were included. Study selection was independently performed by 2 authors; discrepancies were resolved by the senior author. Studies were categorized by rhinology theme, and data collection comprised type of AI utilized, sample size, and outcomes, including accuracy and precision among others. CONCLUSIONS An overall 5435 articles were identified. Following abstract and title screening, 130 articles underwent full-text review, and 59 articles were selected for analysis. Eleven studies were from the gray literature. Articles were stratified into image processing, segmentation, and diagnostics (n = 27); rhinosinusitis classification (n = 14); treatment and disease outcome prediction (n = 8); optimizing surgical navigation and phase assessment (n = 3); robotic surgery (n = 2); olfactory dysfunction (n = 2); and diagnosis of allergic rhinitis (n = 3). Most AI studies were published from 2016 onward (n = 45). IMPLICATIONS FOR PRACTICE This state of the art review aimed to highlight the increasing applications of AI in rhinology. Next steps will entail multidisciplinary collaboration to ensure data integrity, ongoing validation of AI algorithms, and integration into clinical practice. Future research should be tailored at the interplay of AI with robotics and surgical education.
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Affiliation(s)
- Ameen Amanian
- Division of Otolaryngology–Head and Neck Surgery, Department of Surgery, University of British Columbia, Vancouver, Canada
| | - Austin Heffernan
- Division of Otolaryngology–Head and Neck Surgery, Department of Surgery, University of British Columbia, Vancouver, Canada
| | - Masaru Ishii
- Department of Otolaryngology–Head and Neck Surgery, School of Medicine, Johns Hopkins University, Baltimore, Maryland, USA
| | - Francis X. Creighton
- Department of Otolaryngology–Head and Neck Surgery, School of Medicine, Johns Hopkins University, Baltimore, Maryland, USA
| | - Andrew Thamboo
- Division of Otolaryngology–Head and Neck Surgery, Department of Surgery, University of British Columbia, Vancouver, Canada
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Huang Z, Chen S, Zhang G, Almadhor A, Li R, Li M, Abbas M, Nguyen Le B, Zhang J, Huang Y. Nanocatalysts as fast and powerful medical intervention: Bridging cochlear implant therapies and advanced modelling using Hidden Markov Models (HMMs) for effective treatment of infections. ENVIRONMENTAL RESEARCH 2023:116285. [PMID: 37301496 DOI: 10.1016/j.envres.2023.116285] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 03/04/2023] [Revised: 05/23/2023] [Accepted: 05/29/2023] [Indexed: 06/12/2023]
Abstract
As human population growth and waste from technologically advanced industries threaten to destabilise our delicate ecological equilibrium, the global spotlight intensifies on environmental contamination and climate-related changes. These challenges extend beyond our external environment and have significant effects on our internal ecosystems. The inner ear, which is responsible for balance and auditory perception, is a prime example. When these sensory mechanisms are impaired, disorders such as deafness can develop. Traditional treatment methods, including systemic antibiotics, are frequently ineffective due to inadequate inner ear penetration. Conventional techniques for administering substances to the inner ear fail to obtain adequate concentrations as well. In this context, cochlear implants laden with nanocatalysts emerge as a promising strategy for the targeted treatment of inner ear infections. Coated with biocompatible nanoparticles containing specific nanocatalysts, these implants can degrade or neutralise contaminants linked to inner ear infections. This method enables the controlled release of nanocatalysts directly at the infection site, thereby maximising therapeutic efficacy and minimising adverse effects. In vivo and in vitro studies have demonstrated that these implants are effective at eliminating infections, reducing inflammation, and fostering tissue regeneration in the ear. This study investigates the application of hidden Markov models (HMMs) to nanocatalyst-loaded cochlear implants. The HMM is trained on surgical phases in order to accurately identify the various phases associated with implant utilisation. This facilitates the precision placement of surgical instruments within the ear, with a location accuracy between 91% and 95% and a standard deviation between 1% and 5% for both sites. In conclusion, nanocatalysts serve as potent medicinal instruments, bridging cochlear implant therapies and advanced modelling utilising hidden Markov models for the effective treatment of inner ear infections. Cochlear implants loaded with nanocatalysts offer a promising method to combat inner ear infections and enhance patient outcomes by addressing the limitations of conventional treatments.
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Ngombu S, Binol H, Gurcan MN, Moberly AC. Advances in Artificial Intelligence to Diagnose Otitis Media: State of the Art Review. Otolaryngol Head Neck Surg 2023; 168:635-642. [PMID: 35290142 DOI: 10.1177/01945998221083502] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/08/2021] [Accepted: 02/09/2022] [Indexed: 11/15/2022]
Abstract
OBJECTIVE Otitis media (OM) is a model disease for developing, validating, and implementing artificial intelligence (AI) techniques. We aim to review the state of the art applications of AI used to diagnose OM in pediatric and adult populations. DATA SOURCES Several comprehensive databases were searched to identify all articles that applied AI technologies to diagnose OM. REVIEW METHODS Relevant articles from January 2010 through May 2021 were identified by title and abstract. Articles were excluded if they did not discuss AI in conjunction with diagnosing OM. References of included studies and relevant review articles were cross-referenced to identify any additional studies. CONCLUSION Title and abstract screening resulted in full-text retrieval of 40 articles that met initial screening parameters. Of this total, secondary review articles (n = 7) and commentary-based articles (n = 2) were removed, as were articles that did not specifically discuss AI and OM diagnosis (n = 5), leaving 25 articles for review. Applications of AI technologies specific to diagnosing OM included machine learning and natural language processing (n = 23) and prototype approaches (n = 2). IMPLICATIONS FOR PRACTICE This review emphasizes the utility of AI techniques to automate and aid in diagnosing OM. Although these techniques are still in the development and testing stages, AI has the potential to improve the practice of otolaryngologists and primary care clinicians by increasing the efficiency and accuracy of diagnoses.
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Affiliation(s)
- Stephany Ngombu
- Department of Otolaryngology-Head and Neck Surgery, Wexner Medical Center at The Ohio State University, Columbus, Ohio, USA
| | - Hamidullah Binol
- Center for Biomedical Informatics, Wake Forest School of Medicine, Winston-Salem, North Carolina, USA
| | - Metin N Gurcan
- Center for Biomedical Informatics, Wake Forest School of Medicine, Winston-Salem, North Carolina, USA
| | - Aaron C Moberly
- Department of Otolaryngology-Head and Neck Surgery, Wexner Medical Center at The Ohio State University, Columbus, Ohio, USA
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Hasan Z, Key S, Habib AR, Wong E, Aweidah L, Kumar A, Sacks R, Singh N. Convolutional Neural Networks in ENT Radiology: Systematic Review of the Literature. Ann Otol Rhinol Laryngol 2023; 132:417-430. [PMID: 35651308 DOI: 10.1177/00034894221095899] [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] [Indexed: 11/16/2022]
Abstract
INTRODUCTION Convolutional neural networks (CNNs) represent a state-of-the-art methodological technique in AI and deep learning, and were specifically created for image classification and computer vision tasks. CNNs have been applied in radiology in a number of different disciplines, mostly outside otolaryngology, potentially due to a lack of familiarity with this technology within the otolaryngology community. CNNs have the potential to revolutionize clinical practice by reducing the time required to perform manual tasks. This literature search aims to present a comprehensive systematic review of the published literature with regard to CNNs and their utility to date in ENT radiology. METHODS Data were extracted from a variety of databases including PubMED, Proquest, MEDLINE Open Knowledge Maps, and Gale OneFile Computer Science. Medical subject headings (MeSH) terms and keywords were used to extract related literature from each databases inception to October 2020. Inclusion criteria were studies where CNNs were used as the main intervention and CNNs focusing on radiology relevant to ENT. Titles and abstracts were reviewed followed by the contents. Once the final list of articles was obtained, their reference lists were also searched to identify further articles. RESULTS Thirty articles were identified for inclusion in this study. Studies utilizing CNNs in most ENT subspecialties were identified. Studies utilized CNNs for a number of tasks including identification of structures, presence of pathology, and segmentation of tumors for radiotherapy planning. All studies reported a high degree of accuracy of CNNs in performing the chosen task. CONCLUSION This study provides a better understanding of CNN methodology used in ENT radiology demonstrating a myriad of potential uses for this exciting technology including nodule and tumor identification, identification of anatomical variation, and segmentation of tumors. It is anticipated that this field will continue to evolve and these technologies and methodologies will become more entrenched in our everyday practice.
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Affiliation(s)
- Zubair Hasan
- Faculty of Medicine and Health, University of Sydney, Camperdown, NSW, Australia
- Department of Otolaryngology - Head and Neck Surgery, Westmead Hospital, Westmead, NSW, Australia
| | - Seraphina Key
- Faculty of Medicine, Nursing and Health Sciences, Monash University, Clayton, VIC, Australia
| | - Al-Rahim Habib
- Faculty of Medicine and Health, University of Sydney, Camperdown, NSW, Australia
- Department of Otolaryngology - Head and Neck Surgery, Westmead Hospital, Westmead, NSW, Australia
- Department of Otolaryngology - Head and Neck Surgery, Princess Alexandra Hospital, Woolloongabba, QLD, Australia
| | - Eugene Wong
- Department of Otolaryngology - Head and Neck Surgery, Westmead Hospital, Westmead, NSW, Australia
| | - Layal Aweidah
- Faculty of Medicine, University of Notre Dame, Darlinghurst, NSW, Australia
| | - Ashnil Kumar
- School of Biomedical Engineering, Faculty of Engineering, University of Sydney, Darlington, NSW, Australia
| | - Raymond Sacks
- Faculty of Medicine and Health, University of Sydney, Camperdown, NSW, Australia
- Department of Otolaryngology - Head and Neck Surgery, Concord Hospital, Concord, NSW, Australia
| | - Narinder Singh
- Faculty of Medicine and Health, University of Sydney, Camperdown, NSW, Australia
- Department of Otolaryngology - Head and Neck Surgery, Westmead Hospital, Westmead, NSW, Australia
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Asaad M, Lu SC, Hassan AM, Kambhampati P, Mitchell D, Chang EI, Yu P, Hanasono MM, Sidey-Gibbons C. The Use of Machine Learning for Predicting Complications of Free-Flap Head and Neck Reconstruction. Ann Surg Oncol 2023; 30:2343-2352. [PMID: 36719569 DOI: 10.1245/s10434-022-13053-3] [Citation(s) in RCA: 6] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/29/2022] [Accepted: 12/22/2022] [Indexed: 02/01/2023]
Abstract
BACKGROUND Machine learning has been increasingly used for surgical outcome prediction, yet applications in head and neck reconstruction are not well-described. In this study, we developed and evaluated the performance of ML algorithms in predicting postoperative complications in head and neck free-flap reconstruction. METHODS We conducted a comprehensive review of patients who underwent microvascular head and neck reconstruction between January 2005 and December 2018. Data were used to develop and evaluate nine supervised ML algorithms in predicting overall complications, major recipient-site complication, and total flap loss. RESULTS We identified 4000 patients who met inclusion criteria. Overall, 33.7% of patients experienced a complication, 26.5% experienced a major recipient-site complication, and 1.7% suffered total flap loss. The k-nearest neighbors algorithm demonstrated the best overall performance for predicting any complication (AUROC = 0.61, sensitivity = 0.60). Regularized regression had the best performance for predicting major recipient-site complications (AUROC = 0.68, sensitivity = 0.66), and decision trees were the best predictors of total flap loss (AUROC = 0.66, sensitivity = 0.50). CONCLUSIONS ML accurately identified patients at risk of experiencing postsurgical complications, including total flap loss. Predictions from ML models may provide insight in the perioperative setting and facilitate shared decision making.
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Affiliation(s)
- Malke Asaad
- Department of Plastic Surgery, The University of Texas MD Anderson Cancer Center, Houston, TX, USA
| | - Sheng-Chieh Lu
- Department of Symptom Research, MD Anderson Center for INSPiRED Cancer Care, University of Texas MD Anderson Cancer Center, Houston, TX, USA
| | - Abbas M Hassan
- Department of Plastic Surgery, The University of Texas MD Anderson Cancer Center, Houston, TX, USA
| | - Praneeth Kambhampati
- Department of Plastic Surgery, The University of Texas MD Anderson Cancer Center, Houston, TX, USA
- Baylor College of Medicine, Houston, TX, USA
| | - David Mitchell
- Department of Plastic Surgery, The University of Texas MD Anderson Cancer Center, Houston, TX, USA.
- McGovern Medical School, Houston, TX, USA.
| | - Edward I Chang
- Department of Plastic Surgery, The University of Texas MD Anderson Cancer Center, Houston, TX, USA
| | - Peirong Yu
- Department of Plastic Surgery, The University of Texas MD Anderson Cancer Center, Houston, TX, USA
| | - Matthew M Hanasono
- Department of Plastic Surgery, The University of Texas MD Anderson Cancer Center, Houston, TX, USA
| | - C Sidey-Gibbons
- Department of Symptom Research, MD Anderson Center for INSPiRED Cancer Care, University of Texas MD Anderson Cancer Center, Houston, TX, USA
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Calà F, Frassineti L, Manfredi C, Dejonckere P, Messina F, Barbieri S, Pignataro L, Cantarella G. Machine Learning Assessment of Spasmodic Dysphonia Based on Acoustical and Perceptual Parameters. Bioengineering (Basel) 2023; 10:bioengineering10040426. [PMID: 37106612 PMCID: PMC10135969 DOI: 10.3390/bioengineering10040426] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/22/2023] [Revised: 03/20/2023] [Accepted: 03/24/2023] [Indexed: 03/30/2023] Open
Abstract
Adductor spasmodic dysphonia is a type of adult-onset focal dystonia characterized by involuntary spasms of laryngeal muscles. This paper applied machine learning techniques for the severity assessment of spasmodic dysphonia. To this aim, 7 perceptual indices and 48 acoustical parameters were estimated from the Italian word /a’jwɔle/ emitted by 28 female patients, manually segmented from a standardized sentence and used as features in two classification experiments. Subjects were divided into three severity classes (mild, moderate, severe) on the basis of the G (grade) score of the GRB scale. The first aim was that of finding relationships between perceptual and objective measures with the Local Interpretable Model-Agnostic Explanations method. Then, the development of a diagnostic tool for adductor spasmodic dysphonia severity assessment was investigated. Reliable relationships between G; R (Roughness); B (Breathiness); Spasmodicity; and the acoustical parameters: voiced percentage, F2 median, and F1 median were found. After data scaling, Bayesian hyperparameter optimization, and leave-one-out cross-validation, a k-nearest neighbors model provided 89% accuracy in distinguishing patients among the three severity classes. The proposed methods highlighted the best acoustical parameters that could be used jointly with GRB indices to support the perceptual evaluation of spasmodic dysphonia and provide a tool to help severity assessment of spasmodic dysphonia.
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Costantino A, Sampieri C, Pirola F, De Virgilio A, Kim SH. Development of machine learning models for the prediction of positive surgical margins in transoral robotic surgery (TORS). Head Neck 2023; 45:675-684. [PMID: 36541686 DOI: 10.1002/hed.27283] [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: 08/29/2022] [Revised: 11/10/2022] [Accepted: 12/05/2022] [Indexed: 12/24/2022] Open
Abstract
PURPOSE To develop machine learning (ML) models for predicting positive margins in patients undergoing transoral robotic surgery (TORS). METHODS Data from 453 patients with laryngeal, hypopharyngeal, and oropharyngeal squamous cell carcinoma were retrospectively collected at a tertiary referral center to train (n = 316) and validate (n = 137) six two-class supervised ML models employing 14 variables available pre-operatively. RESULTS The accuracy of the six ML models ranged between 0.67 and 0.75, while the measured AUC between 0.68 and 0.75. The ML algorithms showed high specificity (range: 0.75-0.89) and low sensitivity (range: 0.26-0.64) in detecting patients with positive margins after TORS. NPV was higher (range: 0.73-0.83) compared to PPV (range: 0.45-0.63). T classification and tumor site were the most important predictors of positive surgical margins. CONCLUSIONS ML algorithms can identify patients with low risk of positive margins and therefore amenable to TORS.
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Affiliation(s)
- Andrea Costantino
- Department of Biomedical Sciences, Humanitas University, Pieve Emanele (MI), Italy.,Otorhinolaryngology Unit, IRCCS Humanitas Research Hospital, Rozzano (MI), Italy.,Department of Otorhinolaryngology, Yonsei University College of Medicine, Seoul, Korea
| | - Claudio Sampieri
- Department of Otorhinolaryngology, Yonsei University College of Medicine, Seoul, Korea.,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
| | - Francesca Pirola
- Department of Biomedical Sciences, Humanitas University, Pieve Emanele (MI), Italy.,Otorhinolaryngology Unit, IRCCS Humanitas Research Hospital, Rozzano (MI), Italy.,Department of Otorhinolaryngology, Yonsei University College of Medicine, Seoul, Korea
| | - Armando De Virgilio
- Department of Biomedical Sciences, Humanitas University, Pieve Emanele (MI), Italy.,Otorhinolaryngology Unit, IRCCS Humanitas Research Hospital, Rozzano (MI), Italy
| | - Se-Heon Kim
- Department of Otorhinolaryngology, Yonsei University College of Medicine, Seoul, Korea
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Ten Harkel TC, de Jong G, Marres HAM, Ingels KJAO, Speksnijder CM, Maal TJJ. Automatic grading of patients with a unilateral facial paralysis based on the Sunnybrook Facial Grading System - A deep learning study based on a convolutional neural network. Am J Otolaryngol 2023; 44:103810. [PMID: 36871420 DOI: 10.1016/j.amjoto.2023.103810] [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: 12/01/2022] [Revised: 01/19/2023] [Accepted: 02/19/2023] [Indexed: 02/26/2023]
Abstract
PURPOSE In order to assess the severity and the progression of a unilateral peripheral facial palsy the Sunnybrook Facial Grading System (SFGS) is a well-established grading system due to its clinical relevance, sensitivity, and robust measuring method. However, training is required in order to achieve a high inter-rater reliability. This study investigated the automated grading of facial palsy patients based on the SFGS using a convolutional neural network. METHODS A total of 116 patients with a unilateral peripheral facial palsy and 9 healthy subjects were recorded performing the Sunnybrook poses. A separate model was trained for each of the 13 elements of the SFGS and then used to calculate the Sunnybrook subscores and composite score. The performance of the automated grading system was compared to three clinicians experienced in the grading of a facial palsy. RESULTS The inter-rater reliability of the convolutional neural network was within the range of human observers, with an average intra-class correlation coefficient of 0.87 for the composite Sunnybrook score, 0.45 for the resting symmetry subscore, 0.89 for the symmetry of voluntary movement subscore, and 0.77 for the synkinesis subscore. CONCLUSIONS This study showed the potential of the automated SFGS to be implemented in a clinical setting. The automated grading system adhered to the original SFGS, which makes the implementation and interpretation of the automated grading more straightforward. The automated system can be implemented in numerous settings such as online consults in an e-Health environment, since the model used 2D images captured from a video recording.
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Affiliation(s)
- Timen C Ten Harkel
- Radboud University Medical Centre, 3D Lab Radboudumc, Nijmegen 6500 HB, the Netherlands; Radboud University Medical Centre, Department of Otorhinolaryngology and Head and Neck Surgery, Nijmegen 6500 HB, the Netherlands.
| | - Guido de Jong
- Radboud University Medical Centre, 3D Lab Radboudumc, Nijmegen 6500 HB, the Netherlands
| | - Henri A M Marres
- Radboud University Medical Centre, Department of Otorhinolaryngology and Head and Neck Surgery, Nijmegen 6500 HB, the Netherlands
| | - Koen J A O Ingels
- Radboud University Medical Centre, Department of Otorhinolaryngology and Head and Neck Surgery, Nijmegen 6500 HB, the Netherlands
| | - Caroline M Speksnijder
- Radboud University Medical Centre, Department of Oral and Maxillofacial Surgery, Nijmegen 6500 HB, the Netherlands; University Medical Center Utrecht, Utrecht University, Department of Oral and Maxillofacial Surgery, Utrecht 3508 GA, the Netherlands
| | - Thomas J J Maal
- Radboud University Medical Centre, 3D Lab Radboudumc, Nijmegen 6500 HB, the Netherlands; Radboud University Medical Centre, Department of Oral and Maxillofacial Surgery, Nijmegen 6500 HB, the Netherlands
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Lin SC, Lin MY, Kang BH, Lin YS, Liu YH, Yin CY, Lin PS, Lin CW. Artificial Neural Network-Assisted Classification of Hearing Prognosis of Sudden Sensorineural Hearing Loss With Vertigo. IEEE JOURNAL OF TRANSLATIONAL ENGINEERING IN HEALTH AND MEDICINE 2023; 11:170-181. [PMID: 36816096 PMCID: PMC9930994 DOI: 10.1109/jtehm.2023.3242339] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 06/11/2022] [Revised: 10/16/2022] [Accepted: 01/30/2023] [Indexed: 07/24/2024]
Abstract
This study aimed to determine the impact on hearing prognosis of the coherent frequency with high magnitude-squared wavelet coherence (MSWC) in video head impulse test (vHIT) among patients with sudden sensorineural hearing loss with vertigo (SSNHLV) undergoing high-dose steroid treatment. This study was a retrospective cohort study. SSNHLV patients treated at our referral center from December 2016 to December 2020 were examined. The cohort comprised 64 patients with SSNHLV undergoing high-dose steroid treatment. MSWC was measured by calculating the wavelet coherence analysis (WCA) at various frequencies from a vHIT. The hearing prognosis were analyzed using a multivariable Cox regression model and convolution neural network (CNN) of WCA. There were 64 patients with a male-to-female ratio of 1:1.67. The greater highest coherent frequency of the posterior semicircular canal (SCC) was associated with the complete recovery (CR) of hearing. After adjustment for other factors, the result remained robust (hazard ratio [HR] 2.11, 95% confidence interval [CI] 1.86-2.35). In the feature extraction with Resnet-50 and proceeding SVM in the horizontal image cropping style, the classification accuracy [STD] for (CR vs. partial + no recovery [PR + NR]), (over-sampling of CR vs. PR + NR), (extensive data extraction of CR vs. PR + NR), and (interpolation of time series of CR vs. PR + NR) were 83.6% [7.4], 92.1% [6.8], 88.9% [7.5], and 91.6% [6.4], respectively. The high coherent frequency of the posterior SCC was a significantly independent factor that was associated with good hearing prognosis in the patients who have SSNHLV. WCA may be provided with comprehensive ability in vestibulo-ocular reflex (VOR) evaluation. CNN could be utilized to classify WCA, predict treatment outcomes, and facilitate vHIT interpretation. Feature extraction in CNN with proceeding SVM and horizontal cropping style of wavelet coherence plot performed better accuracy and offered more stable model for hearing outcomes in patients with SSNHLV than pure CNN classification. Clinical and Translational Impact Statement-High coherent frequency in vHIT results in good hearing outcomes in SSNHLV and facilitates AI classification.
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Affiliation(s)
- Sheng-Chiao Lin
- Department of Biomedical EngineeringCollege of Engineering, National Cheng Kung UniversityTainan70101Taiwan
- Department of Otorhinolaryngology—Head and Neck SurgeryKaohsiung Veterans General HospitalKaohsiung813414Taiwan
- School of MedicineNational Defense Medical CenterTaipei11490Taiwan
| | - Ming-Yee Lin
- Department of Otorhinolaryngology—Head and Neck SurgeryKaohsiung Veterans General HospitalKaohsiung813414Taiwan
| | - Bor-Hwang Kang
- Department of Otorhinolaryngology—Head and Neck SurgeryKaohsiung Veterans General HospitalKaohsiung813414Taiwan
- School of MedicineNational Defense Medical CenterTaipei11490Taiwan
| | - Yaoh-Shiang Lin
- Department of Otorhinolaryngology—Head and Neck SurgeryKaohsiung Veterans General HospitalKaohsiung813414Taiwan
- School of MedicineNational Defense Medical CenterTaipei11490Taiwan
| | - Yu-Hsi Liu
- Department of Otorhinolaryngology—Head and Neck SurgeryKaohsiung Veterans General HospitalKaohsiung813414Taiwan
- School of MedicineNational Defense Medical CenterTaipei11490Taiwan
| | - Chi-Yuan Yin
- Department of Otorhinolaryngology—Head and Neck SurgeryKaohsiung Veterans General HospitalKaohsiung813414Taiwan
- Department of Special EducationCollege of Education, National Kaohsiung Normal UniversityKaohsiung80201Taiwan
| | - Po-Shing Lin
- Department of Otorhinolaryngology—Head and Neck SurgeryKaohsiung Veterans General HospitalKaohsiung813414Taiwan
| | - Che-Wei Lin
- Department of Biomedical EngineeringCollege of Engineering, National Cheng Kung UniversityTainan70101Taiwan
- Medical Device Innovation CenterNational Cheng Kung UniversityTainan70101Taiwan
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Miller LE, Goedicke W, Crowson MG, Rathi VK, Naunheim MR, Agarwala AV. Using Machine Learning to Predict Operating Room Case Duration: A Case Study in Otolaryngology. Otolaryngol Head Neck Surg 2023; 168:241-247. [PMID: 35133897 DOI: 10.1177/01945998221076480] [Citation(s) in RCA: 10] [Impact Index Per Article: 10.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/14/2021] [Accepted: 01/07/2022] [Indexed: 12/14/2022]
Abstract
OBJECTIVE Optimizing operating room (OR) efficiency depends on accurate case duration estimates. Machine learning (ML) methods have been used to predict OR case durations in other subspecialties. We hypothesize that ML methods improve projected case lengths over existing non-ML techniques for otolaryngology-head and neck surgery cases. METHODS Deidentified patient information from otolaryngology surgical cases at 1 academic institution were reviewed from 2016 to 2020. Variables collected included patient, surgeon, procedure, and facility data known preoperatively so as to capture all realistic contributors. Available case data were divided into a training and testing data set. Several ML algorithms were evaluated based on best performance of predicted case duration when compared to actual case duration. Performance of all models was compared by the average root mean squared error and mean absolute error (MAE). RESULTS In total, 50,888 otolaryngology surgical cases were evaluated with an average case duration of 98.3 ± 86.9 minutes. Most cases were general otolaryngology (n = 16,620). Case features closely associated with OR duration included procedure performed, surgeon, subspecialty of case, and postoperative destination of the patient. The best-performing ML models were CatBoost and XGBoost, which reduced operative time MAE by 9.6 minutes and 8.5 minutes compared to current methods, respectively. DISCUSSION The incorporation of other easily identifiable features beyond procedure performed and surgeon meaningfully improved our operative duration prediction accuracy. CatBoost provided the best-performing ML model. IMPLICATIONS FOR PRACTICE ML algorithms to predict OR case time duration in otolaryngology can improve case duration accuracy and result in financial benefit.
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Affiliation(s)
- Lauren E Miller
- Department of Otolaryngology-Head and Neck Surgery, Massachusetts Eye and Ear Infirmary, Boston, Massachusetts, USA
| | - William Goedicke
- Department of Otolaryngology-Head and Neck Surgery, Massachusetts Eye and Ear Infirmary, Boston, Massachusetts, USA
| | - Matthew G Crowson
- Department of Otolaryngology-Head and Neck Surgery, Massachusetts Eye and Ear Infirmary, Boston, Massachusetts, USA
| | - Vinay K Rathi
- Department of Otolaryngology-Head and Neck Surgery, Massachusetts Eye and Ear Infirmary, Boston, Massachusetts, USA
| | - Matthew R Naunheim
- Department of Otolaryngology-Head and Neck Surgery, Massachusetts Eye and Ear Infirmary, Boston, Massachusetts, USA
| | - Aalok V Agarwala
- Department of Anesthesia, Massachusetts Eye and Ear Infirmary, Boston, Massachusetts, USA
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Mathews S, Dham R, Dutta A, Jose A. Computational Intelligence in Otorhinolaryngology. JOURNAL OF MARINE MEDICAL SOCIETY 2023. [DOI: 10.4103/jmms.jmms_159_22] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/19/2023] Open
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Tseng YJ, Wang YC, Hsueh PC, Wu CC. Development and validation of machine learning-based risk prediction models of oral squamous cell carcinoma using salivary autoantibody biomarkers. BMC Oral Health 2022; 22:534. [PMID: 36424594 PMCID: PMC9685866 DOI: 10.1186/s12903-022-02607-2] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/10/2022] [Accepted: 11/18/2022] [Indexed: 11/25/2022] Open
Abstract
INTRODUCTION The incidence of oral cavity squamous cell carcinoma (OSCC) continues to rise. OSCC is associated with a low average survival rate, and most patients have a poor disease prognosis because of delayed diagnosis. We used machine learning techniques to predict high-risk cases of OSCC by using salivary autoantibody levels and demographic and behavioral data. METHODS We collected the salivary samples of patients recruited from a teaching hospital between September 2008 and December 2012. Ten salivary autoantibodies, sex, age, smoking, alcohol consumption, and betel nut chewing were used to build prediction models for identifying patients with a high risk of OSCC. The machine learning algorithms applied in the study were logistic regression, random forest, support vector machine with the radial basis function kernel, eXtreme Gradient Boosting (XGBoost), and a stacking model. We evaluated the performance of the models by using the area under the receiver operating characteristic curve (AUC), with simulations conducted 100 times. RESULTS A total of 337 participants were enrolled in this study. The best predictive model was constructed using a stacking algorithm with original forms of age and logarithmic levels of autoantibodies (AUC = 0.795 ± 0.055). Adding autoantibody levels as a data source significantly improved the prediction capability (from 0.698 ± 0.06 to 0.795 ± 0.055, p < 0.001). CONCLUSIONS We successfully established a prediction model for high-risk cases of OSCC. This model can be applied clinically through an online calculator to provide additional personalized information for OSCC diagnosis, thereby reducing the disease morbidity and mortality rates.
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Affiliation(s)
- Yi-Ju Tseng
- grid.260539.b0000 0001 2059 7017Department of Computer Science, National Yang Ming Chiao Tung University, Hsinchu, Taiwan ,grid.2515.30000 0004 0378 8438Computational Health Informatics Program, Boston Children’s Hospital, Boston, MA USA
| | - Yi-Cheng Wang
- grid.145695.a0000 0004 1798 0922Department of Information Management, Chang Gung University, Taoyuan, Taiwan
| | - Pei-Chun Hsueh
- grid.9851.50000 0001 2165 4204Department of Fundamental Oncology, University of Lausanne, Lausanne, Switzerland ,grid.9851.50000 0001 2165 4204Ludwig Institute for Cancer Research, University of Lausanne, Epalinges, Switzerland
| | - Chih-Ching Wu
- grid.145695.a0000 0004 1798 0922Graduate Institute of Biomedical Sciences, Chang Gung University, Taoyuan, Taiwan ,grid.145695.a0000 0004 1798 0922Department of Medical Biotechnology and Laboratory Science, College of Medicine, Chang Gung University, No. 259, Wenhua 1St Rd., Guishan Dist., Taoyuan City, 33302 Taiwan ,grid.413801.f0000 0001 0711 0593Department of Otolaryngology-Head and Neck Surgery, Chang Gung Memorial Hospital, Taoyuan, Taiwan ,grid.145695.a0000 0004 1798 0922Molecular Medicine Research Center, Chang Gung University, Taoyuan, Taiwan ,grid.145695.a0000 0004 1798 0922Research Center for Emerging Viral Infections, College of Medicine, Chang Gung University, Taoyuan, Taiwan
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Keyword-augmented and semi-automatic generation of FESS reports: a proof-of-concept study. Int J Comput Assist Radiol Surg 2022; 18:961-968. [PMID: 36394797 PMCID: PMC10113317 DOI: 10.1007/s11548-022-02791-0] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/07/2022] [Accepted: 11/02/2022] [Indexed: 11/18/2022]
Abstract
Abstract
Introduction
Surgical reports are usually written after a procedure and must often be reproduced from memory. Thus, this is an error-prone, and time-consuming task which increases the workload of physicians. In this proof-of-concept study, we developed and evaluated a software tool using Artificial Intelligence (AI) for semi-automatic intraoperative generation of surgical reports for functional endoscopic sinus surgery (FESS).
Materials and methods
A vocabulary of keywords for developing a neural language model was created. With an encoder-decoder-architecture, artificially coherent sentence structures, as they would be expected in general operation reports, were generated. A first set of 48 conventional operation reports were used for model training. After training, the reports were generated again and compared to those before training. Established metrics were used to measure optimization of the model objectively. A cohort of 16 physicians corrected and evaluated three randomly selected, generated reports in four categories: “quality of the generated operation reports,” “time-saving,” “clinical benefits” and “comparison with the conventional reports.” The corrections of the generated reports were counted and categorized.
Results
Objective parameters showed improvement in performance after training the language model (p < 0.001). 27.78% estimated a timesaving of 1–15 and 61.11% of 16–30 min per day. 66.66% claimed to see a clinical benefit and 61.11% a relevant workload reduction. Similarity in content between generated and conventional reports was seen by 33.33%, similarity in form by 27.78%. 66.67% would use this tool in the future. An average of 23.25 ± 12.5 corrections was needed for a subjectively appropriate surgery report.
Conclusion
The results indicate existing limitations of applying deep learning to text generation of operation reports and show a high acceptance by the physicians. By taking over this time-consuming task, the tool could reduce workload, optimize clinical workflows and improve the quality of patient care. Further training of the language model is needed.
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Compton EC, Cruz T, Andreassen M, Beveridge S, Bosch D, Randall DR, Livingstone D. Developing an Artificial Intelligence Tool to Predict Vocal Cord Pathology in Primary Care Settings. Laryngoscope 2022. [PMID: 36226791 DOI: 10.1002/lary.30432] [Citation(s) in RCA: 7] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/23/2022] [Revised: 08/16/2022] [Accepted: 09/09/2022] [Indexed: 11/12/2022]
Abstract
OBJECTIVES Diagnostic tools for voice disorders are lacking for primary care physicians. Artificial intelligence (AI) tools may add to the armamentarium for physicians, decreasing the time to diagnosis and limiting the burden of dysphonia. METHODS Voice recordings of patients were collected from 2019 to 2021 using smartphones. The Saarbruecken dataset was included for comparison. Audio files were converted to mel-spectrograms using TensorFlow. Diagnostic categories were created to group pathology, including neurological and muscular disorders, inflammatory, mass lesions, and normal. The samples were further separated into sustained/a/and the rainbow passage. RESULTS Two hundred three prospective samples and 1131 samples were used from the Saarbruecken database. The AI detected abnormal pathology with an F1-score of 98%. The artificial neural network (ANN) differentiated key pathologies, including unilateral paralysis, laryngitis, adductor spasmodic dysphonia (ADSD), mass lesions, and normal samples with 39%-87% F-1 scores. The Calgary database models had higher F-1 scores in a head-to-head comparison to the Saarbruecken and combined datasets (87% vs. 58% and 50%). The AI outperformed otolaryngologists using a standardized test set of recordings (83% compared to 55% ± 15%). CONCLUSION An AI tool was created to differentiate pathology by individual or categorical diagnosis with high evaluation metrics. Prospective data should be collected in a controlled fashion to reduce intrinsic variability between recordings. Multi-center data collaborations are imperative to increase the prediction capability of AI tools for detecting vocal cord pathology. We provide proof-of-concept for an AI tool to assist primary care physicians in managing dysphonic patients. LEVEL OF EVIDENCE 3 Laryngoscope, 2022.
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Affiliation(s)
- Evan C Compton
- Section of Otolaryngology-Head and Neck Surgery, Department of Surgery, Cumming School of Medicine, University of Calgary, Calgary, Alberta, Canada
| | - Tim Cruz
- Department of Data Science and Analytics, Faculty of Science, University of Calgary, Calgary, Alberta, Canada
| | - Meri Andreassen
- Section of Otolaryngology-Head and Neck Surgery, Calgary Voice Program, Alberta Health Services, Calgary, Alberta, Canada
| | - Shari Beveridge
- Section of Otolaryngology-Head and Neck Surgery, Calgary Voice Program, Alberta Health Services, Calgary, Alberta, Canada
| | - Doug Bosch
- Section of Otolaryngology-Head and Neck Surgery, Department of Surgery, Cumming School of Medicine, University of Calgary, Calgary, Alberta, Canada
| | - Derrick R Randall
- Section of Otolaryngology-Head and Neck Surgery, Department of Surgery, Cumming School of Medicine, University of Calgary, Calgary, Alberta, Canada
| | - Devon Livingstone
- Section of Otolaryngology-Head and Neck Surgery, Department of Surgery, Cumming School of Medicine, University of Calgary, Calgary, Alberta, Canada
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