A Scoping Review of Artificial Intelligence Research in Rhinology

Using Machine Learning Models to Diagnose Chronic Rhinosinusitis: Analysis of Pre-Treatment Patient-Generated Health Data to Predict Cardinal Symptoms and Sinonasal Inflammation

BackgroundThe diagnosis of chronic rhinosinusitis (CRS) relies upon patient-reported symptoms and objective nasal endoscopy and/or computed tomography (CT) findings. Many patients, at the time of evaluation by an otolaryngologist or rhinologist, lack objective findings confirming CRS and do not have this disease.ObjectiveWe hypothesized that a machine learning model (MLM) could predict probable CRS using patient-reported data acquired prior to rhinologist-directed treatment. We leveraged patient-generated health data using a machine learning approach to predict: (1) the primary endpoint of sinonasal inflammation on CT evidenced by a Lund-Mackay score (LMS) ≥ 5 and (2) the secondary endpoint of LMS ≥ 5 and ≥2 cardinal symptoms of CRS.Methods543 patients were evaluated at a tertiary care rhinology clinic and subsequently underwent CT imaging with LMS. Patient-reported outcome measures and additional patient data were collected via an electronic platform prior to in-person evaluation. Three MLMs, a random forest classifier, a deep neural network, and an extreme gradient Boost (XGBoost) algorithm, were trained on predictors drawn from patient-generated health data and tested on a naïve test set (90:10 training:test set split). Cross-validation was executed, and model performance compared between algorithms and with linear regression techniques.Results57 predictors were extracted from the patient-generated health data. The best model (XGBoost) achieved an area-under-the-curve (AUC) of 71.3% (accuracy 74.5%, sensitivity 38.9%, specificity 91.9%) in predicting the primary endpoint, and an AUC of 79.8% (accuracy 85.5%, sensitivity 36.4%, specificity 97.7%) in predicting the secondary endpoint. This exceeded the performance of a linear regression model.ConclusionA MLM using patient-generated health data accurately predicted patients with probable CRS (≥2 cardinal symptoms and LMS ≥ 5). With further validation on a larger cohort, such a tool could potentially be used by otolaryngologists to inform clinical utility of diagnostic imaging and for screening prior to subspecialty Rhinology referral.

Radiomics of the Paranasal Sinuses: A Systematic Review of Computer-Assisted Techniques to Assess Computed Tomography Radiological Data

BACKGROUND

Radiomics is a quantitative approach to medical imaging, aimed to extract features into large datasets. By using artificial intelligence (AI) methodologies, large radiomic data can be analysed and translated into meaningful clinical applications. In rhinology, there is heavy reliance on computed tomography (CT) imaging of the paranasal sinus for diagnostics and assessment of treatment outcomes. Currently, there is an emergence of literature detailing radiomics use in rhinology.

OBJECTIVE

This systematic review aims to assess the current techniques used to analyze radiomic data from paranasal sinus CT imaging.

METHODS

A systematic search was performed using Ovid MEDLINE and EMBASE databases from January 1, 2019 until March 16, 2024 using the Preferred Reporting Items for Systematic Reviews and Meta-analyses (PRISMA) checklist and Cochrane Library Systematic Reviews for Diagnostic and Prognostic Studies. The QUADAS-2 and PROBAST tools were utilized to assess risk of bias.

RESULTS

Our search generated 1456 articles with 10 articles meeting eligibility criteria. Articles were divided into 2 categories, diagnostic (n = 7) and prognostic studies (n = 3). The number of radiomic features extracted ranged 4 to 1409, with analysis including non-AI-based statistical analyses (n = 3) or machine learning algorithms (n = 7). The diagnostic or prognostic utility of radiomics analyses were rated as excellent (n = 3), very good (n = 2), good (n = 2), or not reported (n = 3) based upon area under the curve receiver operating characteristic (AUC-ROC) or accuracy. The average radiomics quality score was 36.95%.

CONCLUSION

Radiomics is an evolving field which can augment our understanding of rhinology diseases, however there are currently only minimal quality studies with limited clinical utility.

The Lamella Ostium Extent Mucosa (LOEM) system: a new classification and pilot study for endoscopic sinus surgery

PURPOSE

This study proposes the Lamella Ostium Extent Mucosa (LOEM) system as a compact and user-friendly classification for endoscopic sinus surgery (ESS), based on surgical bone extension and mucosal management, aiming to resolve inconsistencies in describing surgical techniques and extension levels, and to enhance comparability of outcomes in chronic rhinosinusitis (CRS).

METHODS

LOEM uses a lettering system representing a specific topographical level: L identifies the lamellae, O the ostia, E the opening of the sinus walls, and M the mucosal approach. Eleven CRS surgical cases were independently evaluated by seven rhinologists following a Delphi method in two consecutive rounds. Consensus was assessed using Cohen's kappa.

RESULTS

A substantial agreement was found among the experts (κ = 0.77) in the first round, although the M item only showed fair agreement (κ = 0.37). Clarifications for this item were given in the second round, after which, the overall agreement increased to κ = 0.81 and to κ = 0.79 for the M item. A decrease in agreement from substantial to moderate for O and E items in the second round was found. Test-retest analysis showed an almost perfect agreement (92.96%, κ = 0.82). In this study, a web-based app is provided to assist with the regular use of the LOEM system.

CONCLUSIONS

The LOEM system provides a compact, comprehensive code for ESS, integrating anatomical and functional aspects to represent surgical techniques described so far. This system may be suitable for facilitating communication between surgeons and collecting robust labeled data, hopefully leading to further standardization and validation of surgical approaches in future CRS studies.

The accuracy of deep learning models for diagnosing maxillary fungal ball rhinosinusitis

PURPOSE

To assess the accuracy of deep learning models for the diagnosis of maxillary fungal ball rhinosinusitis (MFB) and to compare the accuracy, sensitivity, specificity, precision, and F1-score with a rhinologist.

METHODS

Data from 1539 adult chronic rhinosinusitis (CRS) patients who underwent paranasal sinus computed tomography (CT) were collected. The overall dataset consisted of 254 MFB cases and 1285 non-MFB cases. The CT images were constructed and labeled to form the deep learning models. Seventy percent of the images were used for training the deep-learning models, and 30% were used for testing. Whole image analysis and instance segmentation analysis were performed using three different architectures: MobileNetv3, ResNet50, and ResNet101 for whole image analysis, and YOLOv5X-SEG, YOLOv8X-SEG, and YOLOv9-C-SEG for instance segmentation analysis. The ROC curve was assessed. Accuracy, sensitivity (recall), specificity, precision, and F1-score were compared between the models and a rhinologist. Kappa agreement was evaluated.

RESULTS

Whole image analysis showed lower precision, recall, and F1-score compared to instance segmentation. The models exhibited an area under the ROC curve of 0.86 for whole image analysis and 0.88 for instance segmentation. In the testing dataset for whole images, the MobileNet V3 model showed 81.00% accuracy, 47.40% sensitivity, 87.90% specificity, 66.80% precision, and a 67.20% F1 score. Instance segmentation yielded the best evaluation with YOLOv8X-SEG showing 94.10% accuracy, 85.90% sensitivity, 95.80% specificity, 88.90% precision, and an 89.80% F1-score. The rhinologist achieved 93.5% accuracy, 84.6% sensitivity, 95.3% specificity, 78.6% precision, and an 81.5% F1-score.

CONCLUSION

Utilizing paranasal sinus CT imaging with enhanced localization and constructive instance segmentation in deep learning models can be the practical promising deep learning system in assisting physicians for diagnosing maxillary fungal ball.

Value of radiomics and deep learning feature fusion models based on dce-mri in distinguishing sinonasal squamous cell carcinoma from lymphoma

Problem

Sinonasal squamous cell carcinoma (SNSCC) and sinonasal lymphoma (SNL) lack distinct clinical manifestations and traditional imaging characteristics, complicating the accurate differentiation between these tumors and the selection of appropriate treatment strategies. Consequently, there is an urgent need for a method that can precisely distinguish between these tumors preoperatively to formulate suitable treatment plans for patients.

Methods

This study aims to construct and validate ML and DL feature models based on Dynamic Contrast-Enhanced (DCE) imaging and to evaluate the clinical value of a radiomics and deep learning (DL) feature fusion model in differentiating between SNSCC and SNL. This study performed a retrospective analysis on the preoperative axial DCE-T1WI MRI images of 90 patients diagnosed with sinonasal tumors, comprising 50 cases of SNSCC and 40 cases of SNL. Data were randomly divided into a training set and a validation set at a 7:3 ratio, and radiomic features were extracted. Concurrently, deep learning features were derived using the optimally pre-trained DL model and integrated with manually extracted radiomic features. Feature sets were selected through independent samples t-test, Mann-Whitney U-test, Pearson correlation coefficient and LASSO regression. Three conventional machine learning (CML) models and three DL models were established, and all radiomic and DL features were merged to create three pre-fusion machine learning models (DLR). Additionally, a post-fusion model (DLRN) was constructed by combining radiomic scores and DL scores. Quantitative metrics such as area under the curve (AUC), sensitivity, and accuracy were employed to identify the optimal feature set and classifier. Furthermore, a deep learning-radiomics nomogram (DLRN) was developed as a clinical decision-support tool.

Results

The feature fusion model of radiomics and DL has higher accuracy in distinguishing SNSCC from SNL than CML or DL alone. The ExtraTrees model based on DLR fusion features of DCE-T1WI had an AUC value of 0.995 in the training set and 0.939 in the validation set.The DLRN model based on the fusion of predictive scores had an AUC value of 0.995 in the training set and 0.911 in the validation set.The DLRN model based on the fusion of predictive scores had an AUC value of 0.995 in the training set and 0.911 in the validation set.

Conclusion

This study, by constructing a feature integration model combining radiomics and deep learning (DL), has demonstrated strong predictive capabilities in the preoperative non-invasive diagnosis of SNSCC and SNL, offering valuable information for tailoring personalized treatment plans for patients.

Artificial intelligence for automatic detection and segmentation of nasal polyposis: a pilot study

PURPOSE

Accurate diagnosis and quantification of polyps and symptoms are pivotal for planning the therapeutic strategy of Chronic rhinosinusitis with nasal polyposis (CRSwNP). This pilot study aimed to develop an artificial intelligence (AI)-based image analysis system capable of segmenting nasal polyps from nasal endoscopy videos.

METHODS

Recorded nasal videoendoscopies from 52 patients diagnosed with CRSwNP between 2019 and 2022 were retrospectively analyzed. Images extracted were manually segmented on the web application Roboflow. A dataset of 342 images was generated and divided into training (80%), validation (10%), and testing (10%) sets. The Ultralytics YOLOv8.0.28 model was employed for automated segmentation.

RESULTS

The YOLOv8s-seg model consisted of 195 layers and required 42.4 GFLOPs for operation. When tested against the validation set, the algorithm achieved a precision of 0.91, recall of 0.839, and mean average precision at 50% IoU (mAP50) of 0.949. For the segmentation task, similar metrics were observed, including a mAP ranging from 0.675 to 0.679 for IoUs between 50% and 95%.

CONCLUSIONS

The study shows that a carefully trained AI algorithm can effectively identify and delineate nasal polyps in patients with CRSwNP. Despite certain limitations like the focus on CRSwNP-specific samples, the algorithm presents a promising complementary tool to existing diagnostic methods.

Artificial Intelligence in Rhinology

Rhinology, allergy, and skull base surgery are fields primed for the integration and implementation of artificial intelligence (AI). The heterogeneity of the disease processes within these fields highlights the opportunity for AI to augment clinical care and promote personalized medicine. Numerous research studies have been published demonstrating the development and clinical potential of AI models within the field. Most describe in silico evaluation models without direct clinical implementation. The major themes of existing studies include diagnostic or clinical decisions support, clustering patients into specific phenotypes or endotypes, predicting post-treatment outcomes, and surgical planning.

The Application of Mask Region-Based Convolutional Neural Networks in the Detection of Nasal Septal Deviation Using Cone Beam Computed Tomography Images: Proof-of-Concept Study

BACKGROUND

Artificial intelligence (AI) models are being increasingly studied for the detection of variations and pathologies in different imaging modalities. Nasal septal deviation (NSD) is an important anatomical structure with clinical implications. However, AI-based radiographic detection of NSD has not yet been studied.

OBJECTIVE

This research aimed to develop and evaluate a real-time model that can detect probable NSD using cone beam computed tomography (CBCT) images.

METHODS

Coronal section images were obtained from 204 full-volume CBCT scans. The scans were classified as normal and deviated by 2 maxillofacial radiologists. The images were then used to train and test the AI model. Mask region-based convolutional neural networks (Mask R-CNNs) comprising 3 different backbones-ResNet50, ResNet101, and MobileNet-were used to detect deviated nasal septum in 204 CBCT images. To further improve the detection, an image preprocessing technique (contrast enhancement [CEH]) was added.

RESULTS

The best-performing model-CEH-ResNet101-achieved a mean average precision of 0.911, with an area under the curve of 0.921.

CONCLUSIONS

The performance of the model shows that the model is capable of detecting nasal septal deviation. Future research in this field should focus on additional preprocessing of images and detection of NSD based on multiple planes using 3D images.

Utilizing radiomics for differential diagnosis of inverted papilloma and chronic rhinosinusitis with polyps based on unenhanced CT scans

To evaluate whether radiomics models based on unenhanced paranasal sinuses CT images could be a useful tool for differentiating inverted papilloma (IP) from chronic rhinosinusitis with polyps (CRSwNP). This retrospective study recruited 240 patients with CRSwNP and 106 patients with IP from three centers. 253 patients from Qilu Hospital were randomly divided into the training set (n = 151) and the internal validation set (n = 102) with a ratio of 6:4. 93 patients from the other two centers were used as the external validation set. The patients with the unilateral disease (n = 115) from Qilu Hospital were selected to further develop a subgroup analysis. Lesion segmentation was manually delineated in CT images. Least absolute shrinkage and selection operator algorithm was performed for feature reduction and selection. Decision tree, support vector machine, random forest, and adaptive boosting regressor were employed to establish the differential diagnosis models. 43 radiomic features were selected for modeling. Among the models, RF achieved the best results, with an AUC of 0.998, 0.943, and 0.934 in the training set, the internal validation set, and the external validation set, respectively. In the subgroup analysis, RF achieved an AUC of 0.999 in the training set and 0.963 in the internal validation set. The proposed radiomics models offered a non-invasion and accurate differential approach between IP and CRSwNP and has some significance in guiding clinicians determining the best treatment plans, as well as predicting the prognosis.

Artificial intelligence in maxillofacial and facial plastic and reconstructive surgery

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.

Artificial intelligence in head and neck surgery: Potential applications and future perspectives

Artificial intelligence (AI) has the potential to improve the surgical treatment of patients with head and neck cancer. AI algorithms can analyse a wide range of data, including images, voice, molecular expression and raw clinical data. In the field of oncology, there are numerous AI practical applications, including diagnostics and treatment. AI can also develop predictive models to assess prognosis, overall survival, the likelihood of occult metastases, risk of complications and hospital length of stay.

International Consensus Statement on Allergy and Rhinology: Sinonasal Tumors

BACKGROUND

Sinonasal neoplasms, whether benign and malignant, pose a significant challenge to clinicians and represent a model area for multidisciplinary collaboration in order to optimize patient care. The International Consensus Statement on Allergy and Rhinology: Sinonasal Tumors (ICSNT) aims to summarize the best available evidence and presents 48 thematic and histopathology-based topics spanning the field.

METHODS

In accordance with prior International Consensus Statement on Allergy and Rhinology documents, ICSNT assigned each topic as an Evidence-Based Review with Recommendations, Evidence-Based Review, and Literature Review based on the level of evidence. An international group of multidisciplinary author teams were assembled for the topic reviews using the Preferred Reporting Items for Systematic Reviews and Meta-Analyses format, and completed sections underwent a thorough and iterative consensus-building process. The final document underwent rigorous synthesis and review prior to publication.

RESULTS

The ICSNT document consists of four major sections: general principles, benign neoplasms and lesions, malignant neoplasms, and quality of life and surveillance. It covers 48 conceptual and/or histopathology-based topics relevant to sinonasal neoplasms and masses. Topics with a high level of evidence provided specific recommendations, while other areas summarized the current state of evidence. A final section highlights research opportunities and future directions, contributing to advancing knowledge and community intervention.

CONCLUSION

As an embodiment of the multidisciplinary and collaborative model of care in sinonasal neoplasms and masses, ICSNT was designed as a comprehensive, international, and multidisciplinary collaborative endeavor. Its primary objective is to summarize the existing evidence in the field of sinonasal neoplasms and masses.