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Clinical and Experimental Otorhinolaryngology > Volume 13(4); 2020 > Article
Tama, Kim, Kim, Kim, and Lee: Recent Advances in the Application of Artificial Intelligence in Otorhinolaryngology-Head and Neck Surgery

Abstract

This study presents an up-to-date survey of the use of artificial intelligence (AI) in the field of otorhinolaryngology, considering opportunities, research challenges, and research directions. We searched PubMed, the Cochrane Central Register of Controlled Trials, Embase, and the Web of Science. We initially retrieved 458 articles. The exclusion of non-English publications and duplicates yielded a total of 90 remaining studies. These 90 studies were divided into those analyzing medical images, voice, medical devices, and clinical diagnoses and treatments. Most studies (42.2%, 38/90) used AI for image-based analysis, followed by clinical diagnoses and treatments (24 studies). Each of the remaining two subcategories included 14 studies. Machine learning and deep learning have been extensively applied in the field of otorhinolaryngology. However, the performance of AI models varies and research challenges remain.

INTRODUCTION

Artificial intelligence (AI) refers to the ability of machines to mimic human intelligence without explicit programming; AI can solve tasks that require complex decision-making [1,2]. Recent advances in computing power and big data handling have encouraged the use of AI to aid or substitute for conventional approaches. The results of AI applications are promising, and have attracted the attention of researchers and practitioners. In 2015, some AI applications began to outperform human intelligence: ResNet performed better than humans in the ImageNet Large Scale Visual Recognition Competition 2015 [3], and AlphaGo became the first computer Go program to beat a professional Go player in October 2015 [4]. Such technical advances have promising implications for medical applications, particularly because the amount of medical data is doubling every 73 days in 2020 [5]. As such, it is expected that AI will revolutionize healthcare because of its ability to handle data at a massive scale. Currently, AI-based medical platforms support diagnosis, treatment, and prognostic assessments at many healthcare facilities worldwide. The applications of AI include drug development, patient monitoring, and personalized treatment. For example, IBM Watson is a pioneering AI-based medical technology platform used by over 230 organizations worldwide. IBM Watson has consistently outperformed humans in several case studies. In 2016, IBM Watson diagnosed a rare form of leukemia by referring to a dataset of 20 million oncology records [6]. It is clear that the use of AI will fundamentally revolutionize medicine. Frost and Sullivan (a research company) forecast that AI will boost medical outcomes by 30%–40% and reduce treatment costs by up to 50%. The AI healthcare market is expected to attain a value of USD 31.3 billion by 2025 [7].
Otorhinolaryngologists use many instruments to examine patients. Since the early 1990s, AI has been increasingly used to analyze radiological and pathological images, audiometric data, and cochlear implant (CI). Performance [8-10]. As various methods of AI analysis have been developed and refined, the practical scope of AI in the otorhinolaryngological field has been broadened (e.g., virtual reality technology [11-13]). Therefore, it is essential for otorhinolaryngologists to understand the capabilities and limitations of AI. In addition, a data-driven approach to healthcare requires clinicians to ask the right questions and to fit well into interdisciplinary teams [8].
Herein, we review the basics of AI, its current status, and future opportunities for AI in the field of otorhinolaryngology. We seek to answer two questions: “Which areas of otorhinolaryngology have benefited most from AI?” and “ What does the future hold?”

MACHINE LEARNING AND DEEP LEARNING

AI has fascinated medical researchers and practitioners since the advent of machine learning (ML) and deep learning (DL) (two forms of AI) in 1990 and 2010, respectively. A flowchart of the literature search and study selection is presented in Fig. 1. Importantly, AI, ML, and DL overlap (Fig. 2). There is no single definition of AI; its purpose is to automate tasks that generally require the application of human intelligence [14]. Such tasks include object detection and recognition, visual understanding, and decision-making. Generally, AI incorporates both ML and DL, as well as many other techniques that are difficult to map onto recognized learning paradigms. ML is a data-driven technique that blends computer science with statistics, optimization, and probability [15]. An ML algorithm requires (1) input data, (2) examples of correct predictions, and (3) a means of validating algorithm performance. ML uses input data to build a model (i.e., a pattern) that allows humans to draw inferences [16,17]. DL is a subfield of ML, in which tens or hundreds of representative layers are learned with the aid of neural networks. A neural network is a learning structure that features several neurons; when combined with an activation function, a neural network delivers non-linear predictions. Unlike traditional ML algorithms, which typically only extract features, DL processes raw data to define the representations required for classification [18]. DL has been incorporated in many AI applications, including those for medical purposes [19]. The applications of DL thus far include image classification, speech recognition, autonomous driving, and text-to-speech conversion; in these domains, the performance of DL is at least as good as that of humans. Given the significant roles played by ML and DL in the medical field, clinicians must understand both the advantages and limitations of data-driven analytical tools.

AI IN THE FIELD OF OTORHINOLARYNGOLOGY

AI aids medical image-based analysis

Medical imaging yields a visual representation of an internal bodily region to facilitate analysis and treatment. Ear, nose, and throat-related diseases are imaged in various manners. Table 1 summarizes the 38 studies that used AI to assist medical image-based analysis in clinical otorhinolaryngology. Nine studies (23.7%) addressed hyperspectral imaging, nine studies (23.7%) analyzed computed tomography, six studies (15.8%) applied AI to magnetic resonance imaging, and one study (2.63%) analyzed panoramic radiography. Laryngoscopic and otoscopic imaging were addressed in three studies each (7.89% each). The remaining seven studies (18.39%) used AI to aid in the analysis of neuroimaging biomarker levels, biopsy specimens, simulated Raman scattering data, ultrasonography and mass spectrometry data, and digitized images. Nearly all AI algorithms comprised convolutional neural networks. Fig. 3 presents a schematic diagram of the application of convolutional neural networks in medical image-based analysis; the remaining algorithms consisted of support vector machines and random forests.

AI aids voice-based analysis

The subfield of voice-based analysis within otorhinolaryngology seeks to improve speech, to detect voice disorders, and to reduce the noise experienced by patients with (CIs; Table 2 lists the 14 studies that used AI for speech-based analyses. Nine (64.29%) sought to improve speech intelligibility or reduce noise for patients with CIs. Two (14.29%) used acoustic signals to detect voice disorders [67] and “hot potato voice” [70]. In other studies, AI was used for symptoms, voice pathologies, or electromyographic signals as a way to detect voice disorders [68,69], or to restore the voice of a patient who had undergone total laryngectomy [71]. Neural networks were favored, followed by k-nearest neighbor methods, support vector machines, and other widely known classifiers (e.g., decision trees and XGBoost). Fig. 4 presents a schematic diagram of the application of convolutional neural networks in medical voice-based analysis.

AI analysis of biosignals detected from medical devices

Medical device-based analyses seek to predict the responses to clinical treatments in order to guide physicians who may wish to choose alternative or more aggressive therapies. AI has been used to assist polysomnography, to explore gene expression profiles, to interpret cellular cartographs, and to evaluate the outputs of non-contact devices. These studies are summarized in Table 3. Of these 14 studies, most (50%, seven studies) focused on analyses of gene expression data. Three studies (21.43%) used AI to examine polysomnography data in an effort to score sleep stages [72,73] or to identify long-term cardiovascular disease [74]. Most algorithms employed ensemble learning (random forests, Gentle Boost, XGBoost, and a general linear model+support vector machine ensemble); this approach was followed by neural networkbased algorithms (convolutional neural networks, autoencoders, and shallow artificial neural networks). Fig. 5 presents a schematic diagram of the application of the autoencoder and the support vector machine in the analysis of gene expression data.

AI for clinical diagnoses and treatments

Clinical diagnoses and treatments consider only symptoms, medical records, and other clinical documentation. We retrieved 24 relevant studies (Table 4). Of the ML algorithms, most used logistic regression for classification, followed by random forests and support vector machines. Notably, many studies used hold-outs to validate new methods. Fig. 6 presents a schematic diagram of the process cycle of utilizing AI for clinical diagnoses and treatments.

DISCUSSION

We systematically analyzed reports describing the integration of AI in the field of otorhinolaryngology, with an emphasis on how AI may best be implemented in various subfields. Various AI techniques and validation methods have found favor. As described above, advances in 2015 underscored that AI would play a major role in future medicine. Here, we reviewed post-2015 AI applications in the field of otorhinolaryngology. Before 2015, most AI-based technologies focused on CIs [10,75-86]. However, AI applications have expanded greatly in recent years. In terms of image-based analysis, images yielded by rigid endoscopes, laryngoscopes, stroboscopes, computed tomography, magnetic resonance imaging, and multispectral narrow-band imaging [38], as well as hyperspectral imaging [45-52,54], are now interpreted by AI. In voice-based analysis, AI is used to evaluate pathological voice conditions associated with vocal fold disorders, to analyze and decode phonation itself [67], to improve speech perception in noisy conditions, and to improve the hearing of patients with CIs. In medical device-based analyses, AI is used to evaluate tissue and blood test results, as well as the outcomes of otorhinolaryngology-specific tests (e.g., polysomnography) [72,73,122] and audiometry [123,124]. AI has also been used to support clinical diagnoses and treatments, decision-making, the prediction of prognoses [98-100,125,126], disease profiling, the construction of mass spectral databases [43,127-129], the identification or prediction of disease progress [101,105,107-110,130], and the confirmation of diagnoses and the utility of treatments [102-104,112,131].
Although many algorithms have been applied, some are not consistently reliable, and certain challenges remain. AI will presumably become embedded in all tools used for diagnosis, treatment selection, and outcome predictions; thus, AI will be used to analyze images, voices, and clinical records. These are the goals of most studies, but again, the results have been variable and are thus difficult to compare. The limitations include: (1) small training datasets and differences in the sizes of the training and test datasets; (2) differences in validation techniques (notably, some studies have not included data validation); and (3) the use of different performance measures during either classification (e.g., accuracy, sensitivity, specificity, F1, or area under the receiver operating characteristic curve) or regression (e.g., root mean square error, least mean absolute error, R-squared, or log-likelihood ratio).
ML algorithms always require large, labeled training datasets. The lack of such data was often a major limitation of the studies that we reviewed. AI-based predictions in the field of otorhinolaryngology must be rigorously validated. Often, as in the broader medical field, an element of uncertainty compromises an otherwise ideal predictive method, and other research disparities were also apparent in the studies that we reviewed. Recent promising advances in AI include the ensemble learning model, which is more intuitive and interpretable than other models; this model facilitates bias-free AI-based decision-making. The algorithm incorporates a concept of “fairness,” considers ethical and legal issues, and respects privacy during data mining tasks. In summary, although otorhinolaryngology-related AI applications were divided into four categories in the present study, the practical use of a particular AI method depends on the circumstances. AI will be helpful for use in real-world clinical treatment involving complex datasets with heterogeneous variables.

CONCLUSION

We have described several techniques and applications for AI; notably, AI can overcome existing technical limitations in otorhinolaryngology and aid in clinical decision-making. Otorhinolaryngologists have interpreted instrument-derived data for decades, and many algorithms have been developed and applied. However, the use of AI will refine these algorithms, and big health data and information from complex heterogeneous datasets will become available to clinicians, thereby opening new diagnostic, treatment, and research frontiers.

HIGHLIGHTS

▪ Ninety studies that implemented artificial intelligence (AI) in otorhinolaryngology were reviewed and classified.
▪ The studies were divided into four subcategories.
▪ Research challenges regarding future applications of AI in otorhinolaryngology are discussed.

CONFLICT OF INTEREST

No potential conflict of interest relevant to this article was reported.

Notes

AUTHOR CONTRIBUTIONS

Conceptualization: DHK, SWK, SL. Data curation: BAT, DHK, GK. Formal analysis: BAT, DHK, GK. Funding acquisition: DHK, SL. Methodology: BAT, DHK, GK. Project administration: DHK, SWK, SL. Visualization: BAT, DHK, GK. Writing–original draft: BAT, DHK. Writing–review & editing: all authors.

ACKNOWLEDGMENTS

This research was supported by the Basic Science Research Program through an NRF grant funded by the Korean government (MSIT) (No. 2020R1A2C1009744), the Bio Medical Technology Development Program of the NRF funded by the Ministry of Science ICT (No. 2018M3A9E8020856), and the Po-Ca Networking Group funded by the Postech-Catholic Biomedical Engineering Institute (PCBMI) (No. 5-2020-B0001-00046).

Fig. 1.
Flowchart of the literature search and study selection.
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Fig. 2.
Interconnections between artificial intelligence, machine learning, and deep learning.
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Fig. 3.
Artificial intelligence (AI) techniques used for medical image-based analysis.
ceo-2020-00654f3.jpg
Fig. 4.
Artificial intelligence (AI) techniques used for voice-based analysis.
ceo-2020-00654f4.jpg
Fig. 5.
Artificial intelligence (AI) analyses of biosignals detected from medical devices. SVM, support vector machine.
ceo-2020-00654f5.jpg
Fig. 6.
Artificial intelligence (AI) techniques used for clinical diagnoses and treatments. EMR, electronic medical record.
ceo-2020-00654f6.jpg
Table 1.
AI techniques used for medical image-based analysis
Study Analysis modality Objective AI technique Validation method No. of samples in the training dataset No. of samples in the testing dataset Best result
Accuracy (%)/AUC Sensitivity (%)/specificity (%)
[20] CT Anterior ethmoidal artery anatomy CNN: Inception-V3 Hold-out 675 Images from 388 patients 197 Images 82.7/0.86 -
[21] CT Osteomeatal complex occlusion CNN: Inception-V3 - 1.28 Million images from 239 patients - 85.0/0.87 -
[22] CT Chronic otitis media diagnosis CNN: Inception-V3 Hold-out 975 Images 172 Images -/0.92 83.3/91.4
[23] DECT HNSCC lymph nodes RF, GBM Hold-out Training and testing set are randomly chosen with a ratio 70:30 from a total of 412 lymph nodes from 50 patients. 90.0/0.96 89.0/91.0
[24] microCT Intratemporal facial nerve anatomy PCA+SSM - 40 Cadaveric specimens from 21 donors - - -
[25] CT Extranodal extension of HNSCC CNN Hold out 2,875 Lymph nodes 200 Lymph nodes 83.1/0.84 71.0/85.0
[26] CT Prediction of overall survival of head and neck cancer NN, DT, boosting, Bayesian, bagging, RF, MARS, SVM, k-NN, GLM, PLSR 10-CV 101 Head and neck cancer patients, 440 radiomic features -/0.67 -
[27] DECT Benign parotid tumors classification RF Hold-out 882 Images from 42 patients Two-thirds of the samples 92.0/0.97 86.0/100
[28] fMRI Predicting the language outcomes following cochlear implantation SVM LOOCV 22 Training samples, including 15 labeled samples and 7 unlabeled samples 81.3/0.97 77.8/85.7
[29] fMRI Auditory perception SVM 10-CV 42 Images from 6 participants 47.0/- -
[30] MRI Relationship between tinnitus and thicknesses of internal auditory canal and nerves ELM Repeated hold-out 46 Images from 23 healthy subjects and 23 patients. Test was repeated 10 times for three training ratios, i.e., 50%, 60%, and 70%. 94.0/- -
[31] MRI Prediction of treatment outcomes of sinonasal squamous cell carcinomas SVM 9-CV 36 Lesions from 36 patients 92.0/- 100/82.0
[32] Neuroimaging biomarkers Tinnitus SVM 5-CV 102 Images from 46 patients and 56 healthy subjects 80.0/0.86 -
[33] MRI Differentiate sinonasal squamous cell carcinoma from inverted papilloma SVM LOOCV 22 Patients with inverted papilloma and 24 patients with SCC 89.1/- 91.7/86.4
[34] MRI Speech improvement for CI candidates SVM LOOCV 37 Images from 37 children with hearing loss and 40 images from 40 children with normal hearing 84.0/0.84 80.0/88.0
[35] Endoscopic images Laryngeal soft tissue Weighted voting (UNet+ErfNet) Hold-out 200 Images 100 Images 84.7/- -
[36] Laryngoscope images laryngeal neoplasms CNN Hold-out 14,340 Images from 5,250 patients 5,093 Images from 2,271 patients 96.24/- 92.8/98.9
[37] Laryngoscope images Laryngeal cancer CNN Hold-out 13,721 Images 1,176 Images 86.7/0.92 73.1/92.2
[38] Laryngoscope images Oropharyngeal cariconoma Naive Bayes Hold-out 4 Patients with oropharyngeal cariconoma and 1 healthy subject 16 Patients with oropharyngeal cariconoma and 9 healthy subjects 65.9/- 66.8/64.9
[39] Otoscopic images Otologic diseases CNN Hold-out 734 Images; 80% of the images were used for the training and 20% were used for validation. 84.4/- -
[40] Otoscopic images Otitis media MJSR Hold-out 1,230 Images; 80% of and 20% were used the images were used for the training for validation. 91.41/- 89.48/93.33
[41] Otoscopic images Otoscopic diagnosis AutoML Hold-out 1,277 Images 89 Images 88.7/- 86.1/-
[42] Digitized images H&E-stained tissue of oral cavity squamous cell carcinoma LDA, QDA, RF, SVM Hold-out 50 Images 65 Images 88.0/0.87 78.0/93.0
[43] PESI-MS Intraoperative specimens of HNSCC LR LOOCV 114 Non-cancerous specimens and 141 cancerous specimens 95.35/- -
[44] Biopsy specimen Frozen section of oral cavity cancer SVM LOOCV 176 Specimen pairs from 27 subjects -/0.94 100/88.78
[45] HSI Head and neck cancer classification CNN LOOCV 88 Samples from 50 patients 80.0/- 81.0/78.0
[46] HSI Head and neck cancer classification CNN LOOCV 12 Tumor-bearing samples for 12 mice 91.36/- 86.05/93.36
[47] HSI Oral cancer SVM, LDA, QDA, RF, RUSBoost 10-CV 10 Images from 10 mice 79.0/0.86 79.0/79.0
[48] HSI Head and neck cancer classification LDA, QDA, ensemble LDA, SVM, RF Repeated hold-out 20 Specimens from 20 patients 16 Specimens from 16 patients 94.0/0.97 95.0/90.0
[49] HSI Tissue surface shape reconstruction SSRNet 5-CV 200 SL images 96.81/- 92.5/-
[50] HSI Tumor margin of HNSCC CNN 5-CV 395 Surgical specimens 98.0/0.99 -
[51] HSI Tumor margin of HNSCC LDA 10-CV 16 Surgical specimens 90.0/- 89.0/91.0
[52] HSI Optical biopsy of head and neck cancer CNN LOOCV 21 Surgical gross-tissue specimens 81.0/0.82 81.0/80.0
[53] SRS Frozen section of laryngeal squamous cell carcinoma CNN 5-CV 18,750 Images from 45 patients 100/- -
[54] HSI Cancer margins of ex-vivo human surgical specimens CNN Hold-out 11 Surgical specimens 9 Surgical specimens 81.0/0.86 84.0/77.0
[55] USG Genetic risk stratification of thyroid nodules AutoML Hold-out 556 Images from 21 patients 127 Images 77.4/- 45.0/97.0
[56] CT Concha bullosa on coronal sinus classification CNN: Inception-V3 Hold-out 347 Images (163 concha bullosa images and 184 normal images) 100 Images (50 concha bullosa images and 50 normal images) 81.0/0.93 -
[57] Panoramic radiography Maxillary sinusitis diagnosis AlexNet CNN Hold-out 400 Healthy images and 400 inflamed maxillary sinuses images 60 Healthy and 60 inflamed maxillary sinuses images 87.5/0.875 86.7/88.3

AI, artificial intelligence; AUC, area under the receiver operating characteristic curve; CT, computed tomography; CNN, convolutional neural network; DECT, dual-energy computed tomography; HNSCC, head and neck squamous cell carcinoma; RF, random forest; GBM, gradient boosting machine; PCA, principle component analysis; SSM, statistical shape model; NN, neural network; DT, decision tree; MARS, multi adaptive regression splines; SVM, support vector machine; k-NN, k-nearest neighbor; GLM, generalized linear model; PLSR, partial least squares and principal component regression; CV, cross-validation; fMRI, functional magnetic resonance imaging; LOOCV, leave-one-out cross-validation; ELM, extreme learning machine; CI, cochlear implant; MJSR, multitask joint sparse representation; LDA, linear discriminant analysis; QDA, quadratic discriminant analysis; PESI-MS, probe electrospray ionization mass spectrometry; LR, logistic regression; HSI, hyperspectral imaging; SSRNet, super-spectral-resolution network; SRS, stimulated Raman scattering; USG, ultrasonography.

Table 2.
AI techniques used for voice-based analysis
Study Analysis modality Objective AI technique Validation method No. of samples in the training dataset No. of samples in the testing dataset Best result
[58] CI Noise reduction NC+DDAE Hold-out 120 Utterances 200 Utterances Accuracy: 99.5%
[59] CI Segregated speech from background noise DNN Hold-out 560×50 Mixtures for each noise type and SNR 160 Noise segments from original unperturbed noise Hit ratio: 84%; false alarm: 7%
[60] CI Improved pitch perception ANN Hold-out 1,500 Pitch pairs 10% of the training material Accuracy: 95%
[61] CI Predicted speech recognition and QoL outcomes k-NN, DT 10-CV A total of 29 patients, including 48% unilateral CI users and 51% bimodal CI users Accuracy: 81%
[62] CI Noise reduction DDAE Hold-out 12,600 Utterances 900 Noisy utterances Accuracy: 36.2%
[63] CI Improved speech intelligibility in unknown noisy environments DNN Hold-out 640,000 Mixtures of sentences and noises - Accuracy: 90.4%
[64] CI Modeling electrode-to-nerve interface ANN Hold-out 360 Sets of fiber activation patterns per electrode 40 Sets of fiber activation patterns per electrode -
[65] CI Provided digital signal processing plug-in for CI WNN Hold-out 120 Consonants and vowels, sampled at 16 kHz; half of data was used as training set and the rest was used as testing set. SNR: 2.496; MSE: 0.086; LLR: 2.323
[66] CI Assessed disyllabic speech test performance in CI k-NN - 60 Patients - Accuracy: 90.83%
[67] Acoustic signals Voice disorders detection CNN 10-CV 451 Images from 10 health adults and 70 adults with voice disorders Accuracy: 90%
[68] Dysphonic symptoms Voice disorders detection ANN Repeated hold-out 100 Cases of neoplasm, 508 cases of benign phonotraumatic, 153 cases of vocal palsy Accuracy: 83%
[69] Pathological voice Voice disorders detection DNN, SVM, GMM 5-CV 60 Normal voice samples and 402 pathological voice samples Accuracy: 94.26%
[70] Acoustic signal Hot potato voice detection SVM Hold-out 2,200 Synthetic voice samples 12 HPV samples from real patients Accuracy: 88.3%
[71] SEMG signals Voice restoration for laryngectomy patients XGBoost Hold-out 75 Utterances using 7 SEMG sensors - Accuracy: 86.4%

AI, artificial intelligence; CI, cochlear implant; NC, noise classifier; DDAE, deep denoising autoencoder; DNN, deep neural network; SNR, signal-to-noise ratio; ANN, artificial neural network; QoL, quality of life; k-NN, k-nearest neighbors; DT, decision tree; CV, cross-validation; WNN, wavelet neural network; MSE, mean square error; LLR, log-likelihood ratio; CNN, convolutional neural network; GMM, Gaussian mixture model; SVM, support vector machine; HPV, human papillomavirus; SEMG, surface electromyographic.

Table 3.
AI analysis of biosignals detected from medical device
Study Analysis modality Objective AI technique Validation method No. of samples in the training dataset No. of samples in the testing dataset Best result
[73] EEG signal of PSG Sleep stage scoring CNN 5-CV 294 Sleep studies; 122 composed the training set, 20 composed the validation set, and 152 were used in the testing set. Accuracy: 81.81%; F1 score: 81.50%; Cohen’s Kappa: 72.76%
[72] EEG, EMG, EOG signals of PSG Sleep stage scoring CNN Hold-out 42,560 Hours of PSG data from 5,213 patients 580 PSGs Accuracy: 86%; F1 score: 81.0%; Cohen’s Kappa: 82.0%
[74] Sleep heart rate variability in PSG Long-term cardiovascular outcome prediction XGBoost 5-CV 1,252 Patients with cardio vascular disease and 859 patients with non-cardio vascular disease Accuracy: 75.3%
[87] Sleep breathing sound using an air-conduction microphone AHI prediction Gaussian process, SVM, RF, LiR 10-CV 116 Patients with OSA CC: 0.83; LMAE: 9.54 events/hr; RMSE: 13.72 events/hr
[88] Gene signature Thyroid cancer lymph node metastasis and recurrence rediction LDA 6-CV 363 Samples 72 Samples AUC: 0.86; sensitivity: 86%; specificity: 62%; PPV: 93%; NPV: 42%
[89] Gene expression profile Response prediction to chemotherapy in patient with HNSCC SVM LOOCV 16 TPF-sensitive patients and 13 non-TPF-sensitive patients Sensitivity: 88.3%; specificity: 88.9%
[90] Mucus cytokines SNOT-22 scores prediction of CRS patients RF, LiR - 147 Patients with 65 patients with postoperative follow-up R2: 0.398
[91] Cellular cartography Single-cell resolution mapping of the organ of Corti Gentle boost, RF, CNN Hold-out 20,416 Samples 19,594 Samples Recall: 99.3%; precision: 99.3%; F1: 93.3%
[92] RNA sequencing, miRNA sequencing, methylation data HNSCC progress prediction Autoencoder and SVM 2×5-CV 360 Samples from TCGA C-index: 0.73; Brier score: 0.22
[93] DNA repair defect HNSCC progress prediction CART 10×5-CV 180 HPV-negative HNSCC patients AUC: 1.0
[94] PESI-MS Identified TGF-β signaling in HNSCC LDA LOOCV A total of 240 and 90 mass spectra from TGF-β-unstimulated and stimulated HNSCC cells, respectively Accuracy: 98.79%
[95] Next generation sequencing of RNA Classified the risk of malignancy in cytologically indeterminate thyroid nodules Ensemble of elastic net GLM and SVM 40×5-CV A total of 10,196 genes, among which are 1,115 core genes Sensitivity: 91%; specificity: 68%
[96] Gene expression profile HPV-positive oropharyngeal squamous cell carcinoma detection LR 500-CV 146 Genes from patients with node-negative disease and node-positive disease AUC: 0.93
[97] miRNA expression profile Sensorineural hearing loss prediction DF, DJ, LR, NN LOOCV 16 Patients were included. Accuracy: 100%

AI, artificial intelligence; EEG, electroencephalogram; PSG, polysomnography; CNN, convolutional neural network; CV, cross-validation; EMG, electromyography; EOG, electrooculogram; AHI, apnea-hypopnea index; SVM, support vector machine; RF, random forest; LiR, linear regression; OSA, obstructive sleep apnea; CC, correlation coefficient; LMAE, least mean absolute error; RMSE, root mean squared error; LDA, linear discriminant analysis; AUC, area under the receiver operating characteristic curve; PPV, positive predictive value; NPV, negative predictive value; HNSCC, head and neck squamous cell carcinoma; LOOCV, leave-one-out cross validation; TPF, docetaxel, cisplatin, and 5-fluorouracil; SNOT-22, 22-item sinonasal outcome test; CRS, chronic rhinosinusitis; miRNA, microRNA; TCGA, the cancer genome atlas; CART, classification and regression trees; HPV, human papillomavirus; PESI-MS, probe electrospray ionization mass spectrometry; TGF-β, transforming growth factor beta; GLM, generalized linear model; LR, logistic regression; DF, decision forest; DJ, decision jungle; NN, neural network.

Table 4.
AI techniques used for clinical diagnoses and treatments
Study Analysis modality Objective AI technique Validation method No. of samples in the training dataset No. of samples in the testing dataset Best result
[98] Hearing aids Hearing gain prediction CRDN Hold-out 2,182 Patients that were diagnosed with hearing loss; the percentages of randomly sampled training, validation, and test sets were 40%, 30%, and 30%, respectively. MAPE: 9.2%
[99] Hearing aids Predicted CI outcomes RF LOOCV 121 Postlingually deaf adults with CI MAE: 6.1; Pearson’s correlation coefficient: 0.96
[100] Clinical data SSHL prediction DBN, LR, SVM, MLP 4-CV 1,220 Unilateral SSHL patients Accuracy: 77.58%; AUC: 0.84
[101] Clinical data including demographics and risk factors Determined the risk of head and neck cancer LR Hold-out 1,005 Patients, containing 932 patients with no cancer outcome and 73 patients with cancer outcome 235 Patients, containing 212 patients with no cancer outcome and 23 patients with cancer outcome AUC: 0.79
[102] Clinical data including symptom Peritonsillar abscess diagnosis prediction NN Hold-out 641 Patients 275 Patients Accuracy: 72.3%; sensitivity: 6.0%; specificity: 50%
[103] Vestibular test batteries Vestibular function assessment DT, RF, LR, AdaBoost, SVM Hold-out 5,774 Individuals 100 Individuals Accuracy: 93.4%
[104] Speakers and microphones within existing smartphones Middle ear fluid detection LR LOOCV 98 Patient ears AUC: 0.9; sensitivity: 84.6%; specificity: 81.9%
[105] Cancer data survival 5-Year survival patients with oral cavity squamous cell carcinoma DF, DJ, LR, NN Hold-out 26,452 Patients 6,613 Patients AUC: 0.8; accuracy: 71%; precision: 71%; recall: 68%
[106] Histological data Occult lymph node metastases identification in clinically oral cavity squamous cell RF, SVM, LR, C5.0 Hold-out 56 Patients 112 Patients AUC: 0.89; accuracy: 88.0%; NPV: >95%
[107] Clinicopathologic data Head and neck free tissue transfer surgical complications prediction GBDT Hold-out 291 Patients 73 Patients Specificity: 62.0%; sensitivity: 60.0%; F1: 60.0%
[108] Clinicopathologic data Delayed adjuvant radiation prediction RF Hold-out 61,258 Patients 15,315 Patients Accuracy: 64.4%; precision: 58.5%
[109] Clinicopathologic data Occult nodal metastasis prediction in oral cavity squamous cell carcinoma LR, RF, SVM, GBM Hold-out 1,570 Patients 391 Patients AUC: 0.71; sensitivity: 75.3%; specificity: 49.2%
[110] Dataset of the center of pressure sway during foam posturography Peripheral vestibular dysfunction prediction GBDT, bagging, LR CV 75 Patients with vestibular dysfunction and 163 healthy controls AUC: 0.9; recall: 0.84
[111] TEOAE signals Meniere’s disease hearing outcome prediction SVM 5-CV 30 Unilateral patients Accuracy: 82.7%
[112] Semantic and syntactic patterns in clinical documentation Vestibular diagnoses NLP+Naïve Bayes 10-CV 866 Physician-generated histories from vestibular patients Sensitivity: 93.4%; specificity: 98.2%; AUC: 1.0
[113] Endoscopic imaging Nasal polyps diagnosis ResNet50, Xception, and Inception V3 Hold-out 23,048 Patches (167 patients) as training set, 1,577 patches (12 patients) as internal validation set, and 1,964 patches (16 patients) as external test set Inception V3: AUC: 0.974
[114] Intradermal skin tests Allergic rhinitis diagnosis Associative classifier 10-CV 872 Patients with allergic symptoms Accuracy: 88.31%
[115] Clinical data Identified phenotype and mucosal eosinophilia endotype subgroups of patients with medical refractory CRS Cluster analysis - 46 Patients with CRS without nasal polyps and 67 patients with nasal polyps -
[116] Clinical data Prognostic information of patient with CRS Discriminant analysis - 690 Patients -
[117] Clinical data Identified phenotypic subgroups of CRS patients Discriminant analysis - 382 Patients -
[118] Clinical data Characterization of distinguishing clinical features between subgroups of patients with CRS Cluster analysis - 97 Surgical patients with CRS -
[119] Clinical data Identified features of CRS without nasal polyposis Cluster analysis - 145 Patients of CRS without nasal polyposis -
[120] Clinical data Identified inflammatory endotypes of CRS Cluster analysis - 682 Cases (65% with CRS without nasal polyps) -
[121] Clinical data Identified features of CRS with nasal polyps Cluster analysis - 375 Patients -

AI, artificial intelligence; CRDN, cascade recurring deep network; MAPE, mean absolute percentage error; RF, random forest; LOOCV, leave-one-out cross validation; CI, cochlear implant; MAE, mean absolute error; SSHL, sudden sensorineural hearing loss; DBN, deep belief network; LR, logistic regression; SVM, support vector machine; MLP, multilayer perceptron; CV, cross-validation; AUC, area under the receiver operating characteristic curve; NN, neural network; DT, decision tree; DF, decision forest; DJ, decision jungle; NPV, negative predictive value; GBDT, gradient boosted decision trees; GBM, gradient boosting machine; TEOAE, transient-evoked otoacoustic emission; NLP, natural language processing; CRS, chronic rhinosinusitis.

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