A contemporary review of machine learning in otolaryngology–head and neck surgery

Crowson, Matthew G.; Ranisau, Jonathan; Eskander, Antoine; Babier, Aaron; Xu, Bin; Kahmke, Russel; Chen, Joseph M.; Chan, Timothy C. Y.

doi:10.1002/lary.27850

Cited by 100 publications

(116 citation statements)

References 35 publications

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“…There are still open challenges in a wide diversity of medical applications, such is the case of otolaryngology [15]. In this context, some efforts focused on head and neck oncology and the use of ML to classify malignant tissue based on radiographic and histopathologic features [16][17][18].…”

Section: Introductionmentioning

confidence: 99%

Computer-aided diagnosis of external and middle ear conditions: A machine learning approach

et al. 2020

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In medicine, a misdiagnosis or the absence of specialists can affect the patient's health, leading to unnecessary tests and increasing the costs of healthcare. In particular, the lack of specialists in otolaryngology in third world countries forces patients to seek medical attention from general practitioners, whom might not have enough training and experience for making correct diagnosis in this field. To tackle this problem, we propose and test a computer-aided system based on machine learning models and image processing techniques for otoscopic examination, as a support for a more accurate diagnosis of ear conditions at primary care before specialist referral; in particular, for myringosclerosis, earwax plug, and chronic otitis media. To characterize the tympanic membrane and ear canal for each condition, we implemented three different feature extraction methods: color coherence vector, discrete cosine transform, and filter bank. We also considered three machine learning algorithms: support vector machine (SVM), k-nearest neighbor (k-NN) and decision trees to develop the ear condition predictor model. To conduct the research, our database included 160 images as testing set and 720 images as training and validation sets of 180 patients. We repeatedly trained the learning models using the training dataset and evaluated them using the validation dataset to thus obtain the best feature extraction method and learning model that produce the highest validation accuracy. The results showed that the SVM and k-NN presented the best performance followed by decision trees model. Finally, we performed a classification stage-i.e., diagnosis-using testing data, where the SVM model achieved an average classification accuracy of 93.9%, average sensitivity of 87.8%, average specificity of 95.9%, and average positive predictive value of 87.7%. The results show that this system might be used for general practitioners as a reference to make better decisions in the ear pathologies diagnosis.

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Section: Introductionmentioning

confidence: 99%

Computer-aided diagnosis of external and middle ear conditions: A machine learning approach

et al. 2020

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“…The accuracy of these algorithms for specific tasks often approaches and sometimes exceeds that of physicians. A recent review of machine learning in otolaryngology outlines the role of machine learning algorithms in our specialty, including the immense potential for computer vision algorithms in otoscopic diagnosis . Awareness and confidence in machine learning algorithms has grown significantly in recent years; a 2019 poll of otolaryngologists conducted through ENTtoday showed 78% of respondents felt that artificial intelligence (AI) will improve the quality of healthcare delivered by our speciality .…”

Section: Introductionmentioning

confidence: 99%

Otoscopic diagnosis using computer vision: An automated machine learning approach

Livingstone

Chau²

2019

The Laryngoscope

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Objective: Access to otolaryngology is limited by lengthy wait lists and lack of specialists, especially in rural and remote areas. The objective of this study was to use an automated machine learning approach to build a computer vision algorithm for otoscopic diagnosis capable of greater accuracy than trained physicians. This algorithm could be used by primary care providers to facilitate timely referral, triage, and effective treatment.Methods: Otoscopic images were obtained from Google Images (Google Inc., Mountain View, CA), from open access repositories, and within otolaryngology clinics associated with our institution. After preprocessing, 1,366 unique images were uploaded to the Google Cloud Vision AutoML platform (Google Inc.) and annotated with one or more of 14 otologic diagnoses. A consensus set of labels for each otoscopic image was attained, and a multilabel classifier architecture algorithm was trained. The performance of the algorithm on an 89-image test set was compared to the performance of physicians from pediatrics, emergency medicine, otolaryngology, and family medicine.Results: For all diagnoses combined, the average precision (positive predictive value) of the algorithm was 90.9%, and the average recall (sensitivity) was 86.1%. The algorithm made 79 correct diagnoses with an accuracy of 88.7%. The average physician accuracy was 58.9%.Conclusion: We have created a computer vision algorithm using automated machine learning that on average rivals the accuracy of the physicians we tested. Fourteen different otologic diagnoses were analyzed. The field of medicine will be changed dramatically by artificial intelligence within the next few decades, and physicians of all specialties must be prepared to guide that process.

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“…The task of surgical guidance for SCC resections in the head and neck has been explored with increasing volume in the past five years using several imaging modalities coupled with machine learning [16]. Some methods propose using fluorescently-tagged monoclonal antibodies that require intravenous administration but have specific optical signatures in the near-infrared (NIR) spectrum, with successful outcomes of studies with 21 patients [3,15], and other methods utilize topical fluorescent dyes for targeting SCC [17,18].…”

Section: Introductionmentioning

confidence: 99%

Hyperspectral Imaging of Head and Neck Squamous Cell Carcinoma for Cancer Margin Detection in Surgical Specimens from 102 Patients Using Deep Learning

Halicek

Dormer

Little

et al. 2019

Cancers

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Surgical resection of head and neck (H and N) squamous cell carcinoma (SCC) may yield inadequate surgical cancer margins in 10 to 20% of cases. This study investigates the performance of label-free, reflectance-based hyperspectral imaging (HSI) and autofluorescence imaging for SCC detection at the cancer margin in excised tissue specimens from 102 patients and uses fluorescent dyes for comparison. Fresh surgical specimens (n = 293) were collected during H and N SCC resections (n = 102). The tissue specimens were imaged with reflectance-based HSI and autofluorescence imaging and afterwards with two fluorescent dyes for comparison. A histopathological ground truth was made. Deep learning tools were developed to detect SCC with new patient samples (inter-patient) and machine learning for intra-patient tissue samples. Area under the curve (AUC) of the receiver-operator characteristic was used as the main evaluation metric. Additionally, the performance was estimated in mm increments circumferentially from the tumor-normal margin. In intra-patient experiments, HSI classified conventional SCC with an AUC of 0.82 up to 3 mm from the cancer margin, which was more accurate than proflavin dye and autofluorescence (both p < 0.05). Intra-patient autofluorescence imaging detected human papilloma virus positive (HPV+) SCC with an AUC of 0.99 at 3 mm and greater accuracy than proflavin dye (p < 0.05). The inter-patient results showed that reflectance-based HSI and autofluorescence imaging outperformed proflavin dye and standard red, green, and blue (RGB) images (p < 0.05). In new patients, HSI detected conventional SCC in the larynx, oropharynx, and nasal cavity with 0.85–0.95 AUC score, and autofluorescence imaging detected HPV+ SCC in tonsillar tissue with 0.91 AUC score. This study demonstrates that label-free, reflectance-based HSI and autofluorescence imaging methods can accurately detect the cancer margin in ex-vivo specimens within minutes. This non-ionizing optical imaging modality could aid surgeons and reduce inadequate surgical margins during SCC resections.

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A contemporary review of machine learning in otolaryngology–head and neck surgery

Cited by 100 publications

References 35 publications

Computer-aided diagnosis of external and middle ear conditions: A machine learning approach

Computer-aided diagnosis of external and middle ear conditions: A machine learning approach

Otoscopic diagnosis using computer vision: An automated machine learning approach

Hyperspectral Imaging of Head and Neck Squamous Cell Carcinoma for Cancer Margin Detection in Surgical Specimens from 102 Patients Using Deep Learning

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