Abstract:<p class="abstract"><strong>Background:</strong> <span lang="EN-IN">Concha bullosa is a common anatomical variation in nasal cavity, responsible for headache, Rhinogenic origin is an important cause for headache wrongly managed now a day, which may be treated by medical or surgical intervention, proper diagnosis and patient’s selection is very important to achieve good results with surgery of CB. The aim of this study is to evaluate the efficacy and assess the clinical benefits outcome … Show more
“…Ultimately, it is preferential to treat concha bullosa during the initial functional endoscopic sinus surgery for three main reasons: (1) surgical intervention for concha bullosa has shown to significantly improve patient symptomology (nasal obstruction, facial pain, headache, recurrent rhinitis and olfactory function); 21,22 (2) it decreases the need to subject the patient to risks associated with general anaesthesia for a second sinus surgery to remove the concha bullosa later in life; and (3) the relatively low risk of developing synechiae (between 0–27 per cent depending on the technique) as a result of concha bullosa surgical intervention. 23 Deep learning convolutional neural networks are a form of artificial intelligence commonly used for image classification and diagnosisAlthough artificial intelligence has demonstrated significant promise in other medical specialties such as radiology and dermatology, limited research exists for its applicability to ENT surgeryThis study demonstrates that convolutional neural networks appear to be effective in determining whether concha bullosa is present based on computed tomography sinus scansThis finding carries significant implications in surgical planning for sinus surgery and post-operative outcomes…”
ObjectiveConvolutional neural networks are a subclass of deep learning or artificial intelligence that are predominantly used for image analysis and classification. This proof-of-concept study attempts to train a convolutional neural network algorithm that can reliably determine if the middle turbinate is pneumatised (concha bullosa) on coronal sinus computed tomography images.MethodConsecutive high-resolution computed tomography scans of the paranasal sinuses were retrospectively collected between January 2016 and December 2018 at a tertiary rhinology hospital in Australia. The classification layer of Inception-V3 was retrained in Python using a transfer learning method to interpret the computed tomography images. Segmentation analysis was also performed in an attempt to increase diagnostic accuracy.ResultsThe trained convolutional neural network was found to have diagnostic accuracy of 81 per cent (95 per cent confidence interval: 73.0–89.0 per cent) with an area under the curve of 0.93.ConclusionA trained convolutional neural network algorithm appears to successfully identify pneumatisation of the middle turbinate with high accuracy. Further studies can be pursued to test its ability in other clinically important anatomical variants in otolaryngology and rhinology.
“…Ultimately, it is preferential to treat concha bullosa during the initial functional endoscopic sinus surgery for three main reasons: (1) surgical intervention for concha bullosa has shown to significantly improve patient symptomology (nasal obstruction, facial pain, headache, recurrent rhinitis and olfactory function); 21,22 (2) it decreases the need to subject the patient to risks associated with general anaesthesia for a second sinus surgery to remove the concha bullosa later in life; and (3) the relatively low risk of developing synechiae (between 0–27 per cent depending on the technique) as a result of concha bullosa surgical intervention. 23 Deep learning convolutional neural networks are a form of artificial intelligence commonly used for image classification and diagnosisAlthough artificial intelligence has demonstrated significant promise in other medical specialties such as radiology and dermatology, limited research exists for its applicability to ENT surgeryThis study demonstrates that convolutional neural networks appear to be effective in determining whether concha bullosa is present based on computed tomography sinus scansThis finding carries significant implications in surgical planning for sinus surgery and post-operative outcomes…”
ObjectiveConvolutional neural networks are a subclass of deep learning or artificial intelligence that are predominantly used for image analysis and classification. This proof-of-concept study attempts to train a convolutional neural network algorithm that can reliably determine if the middle turbinate is pneumatised (concha bullosa) on coronal sinus computed tomography images.MethodConsecutive high-resolution computed tomography scans of the paranasal sinuses were retrospectively collected between January 2016 and December 2018 at a tertiary rhinology hospital in Australia. The classification layer of Inception-V3 was retrained in Python using a transfer learning method to interpret the computed tomography images. Segmentation analysis was also performed in an attempt to increase diagnostic accuracy.ResultsThe trained convolutional neural network was found to have diagnostic accuracy of 81 per cent (95 per cent confidence interval: 73.0–89.0 per cent) with an area under the curve of 0.93.ConclusionA trained convolutional neural network algorithm appears to successfully identify pneumatisation of the middle turbinate with high accuracy. Further studies can be pursued to test its ability in other clinically important anatomical variants in otolaryngology and rhinology.
“…Parmar et al used a CNN to identify middle turbinate concha bullosa on sinonasal CT scans with an accuracy of 81% 9 . This is a practical and surgically addressable radiological finding as treating concha bullosa has been shown to significantly improve nasal obstruction 24 …”
BackgroundClassifying sphenoid pneumatisation is an important but often overlooked task in reporting sinus CT scans. Artificial intelligence (AI) and one of its key methods, convolutional neural networks (CNNs), can create algorithms that can learn from data without being programmed with explicit rules and have shown utility in radiological image classification.ObjectiveTo determine if a trained CNN can accurately classify sphenoid sinus pneumatisation on CT sinus imaging.MethodsSagittal slices through the natural ostium of the sphenoid sinus were extracted from retrospectively collected bone‐window CT scans of the paranasal sinuses for consecutive patients over 6 years. Two blinded Otolaryngology residents reviewed each image and classified the sphenoid sinus pneumatisation as either conchal, presellar or sellar. An AI algorithm was developed using the Microsoft Azure Custom Vision deep learning platform to classify the pattern of pneumatisation.ResultsSeven hundred eighty images from 400 patients were used to train the algorithm, which was then tested on a further 118 images from 62 patients. The algorithm achieved an accuracy of 93.2% (95% confidence interval [CI] 87.1–97.0), 87.3% (95% CI 79.9–92.7) and 85.6% (95% CI 78.0–91.4) in correctly identifying conchal, presellar and sellar sphenoid pneumatisation, respectively. The overall weighted accuracy of the CNN was 85.9%.ConclusionThe CNN described demonstrated a moderately accurate classification of sphenoid pneumatisation subtypes on CT scans. The use of CNN‐based assistive tools may enable surgeons to achieve safer operative planning through routine automated reporting allowing greater resources to be directed towards the identification of pathology.
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