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Paderno A, Bedi N, Rau A, Holsinger CF. Computer Vision and Videomics in Otolaryngology-Head and Neck Surgery: Bridging the Gap Between Clinical Needs and the Promise of Artificial Intelligence. Otolaryngol Clin North Am 2024; 57:703-718. [PMID: 38981809 DOI: 10.1016/j.otc.2024.05.005] [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: 07/11/2024]
Abstract
This article discusses the role of computer vision in otolaryngology, particularly through endoscopy and surgery. It covers recent applications of artificial intelligence (AI) in nonradiologic imaging within otolaryngology, noting the benefits and challenges, such as improving diagnostic accuracy and optimizing therapeutic outcomes, while also pointing out the necessity for enhanced data curation and standardized research methodologies to advance clinical applications. Technical aspects are also covered, providing a detailed view of the progression from manual feature extraction to more complex AI models, including convolutional neural networks and vision transformers and their potential application in clinical settings.
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Affiliation(s)
- Alberto Paderno
- IRCCS Humanitas Research Hospital, via Manzoni 56, Rozzano, Milan 20089, Italy; Department of Biomedical Sciences, Humanitas University, Via Rita Levi Montalcini 4, Pieve Emanuele, Milan 20072, Italy.
| | - Nikita Bedi
- Division of Head and Neck Surgery, Department of Otolaryngology, Stanford University, Palo Alto, CA, USA
| | - Anita Rau
- Department of Biomedical Data Science, Stanford University, Palo Alto, CA, USA
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Pedersen M, Larsen CF, Madsen B, Eeg M. Localization and quantification of glottal gaps on deep learning segmentation of vocal folds. Sci Rep 2023; 13:878. [PMID: 36650265 PMCID: PMC9845318 DOI: 10.1038/s41598-023-27980-y] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/01/2022] [Accepted: 01/11/2023] [Indexed: 01/19/2023] Open
Abstract
The entire glottis has mostly been the focus in the tracking of the vocal folds, both manually and automatically. From a treatment point of view, the various regions of the glottis are of specific interest. The aim of the study was to test if it was possible to supplement an existing convolutional neural network (CNN) with post-network calculations for the localization and quantification of posterior glottal gaps during phonation, usable for vocal fold function analysis of e.g. laryngopharyngeal reflux findings. 30 subjects/videos with insufficient closure in the rear glottal area and 20 normal subjects/videos were selected from our database, recorded with a commercial high-speed video setup (HSV with 4000 frames per second), and segmented with an open-source CNN for validating voice function. We made post-network calculations to localize and quantify the 10% and 50% distance lines from the rear part of the glottis. The results showed a significant difference using the algorithm at the 10% line distance between the two groups of p < 0.0001 and no difference at 50%. These novel results show that it is possible to use post-network calculations on CNNs for the localization and quantification of posterior glottal gaps.
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Kaluza J, Niebudek-Bogusz E, Malinowski J, Strumillo P, Pietruszewska W. Assessment of Vocal Fold Stiffness by Means of High-Speed Videolaryngoscopy with Laryngotopography in Prediction of Early Glottic Malignancy: Preliminary Report. Cancers (Basel) 2022; 14:4697. [PMID: 36230618 PMCID: PMC9563419 DOI: 10.3390/cancers14194697] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/02/2022] [Revised: 09/07/2022] [Accepted: 09/19/2022] [Indexed: 11/16/2022] Open
Abstract
One of the most important challenges in laryngological practice is the early diagnosis of laryngeal cancer. Detection of non-vibrating areas affected by neoplastic lesions of the vocal folds can be crucial in the recognition of early cancerogenous infiltration. Glottal pathologies associated with abnormal vibration patterns of the vocal folds can be detected and quantified using High-speed Videolaryngoscopy (HSV), also in subjects with severe voice disorders, and analyzed with the aid of computer image processing procedures. We present a method that enables the assessment of vocal fold pathologies with the use of HSV. The calculated laryngotopographic (LTG) maps of the vocal folds based on HSV allowed for a detailed characterization of vibration patterns and abnormalities in different regions of the vocal folds. We verified our methods with HSV recordings from 31 subjects with a normophonic voice and benign and malignant vocal fold lesions. We proposed the novel Stiffness Asymmetry Index (SAI) to differentiate between early glottis cancer (SAI = 0.65 ± 0.18) and benign vocal fold masses (SAI = 0.16 ± 0.13). Our results showed that these glottal pathologies might be noninvasively distinguished prior to histopathological examination. However, this needs to be confirmed by further research on larger groups of benign and malignant laryngeal lesions.
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Affiliation(s)
- Justyna Kaluza
- Institute of Electronics, Lodz University of Technology, 90-924 Lodz, Poland
| | - Ewa Niebudek-Bogusz
- Department of Otolaryngology, Head and Neck Oncology, Medical University of Lodz, 90-001 Lodz, Poland
| | - Jakub Malinowski
- Department of Otolaryngology, Head and Neck Oncology, Medical University of Lodz, 90-001 Lodz, Poland
| | - Pawel Strumillo
- Institute of Electronics, Lodz University of Technology, 90-924 Lodz, Poland
| | - Wioletta Pietruszewska
- Department of Otolaryngology, Head and Neck Oncology, Medical University of Lodz, 90-001 Lodz, Poland
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Long-term performance assessment of fully automatic biomedical glottis segmentation at the point of care. PLoS One 2022; 17:e0266989. [PMID: 36129922 PMCID: PMC9491538 DOI: 10.1371/journal.pone.0266989] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/29/2022] [Accepted: 07/25/2022] [Indexed: 12/04/2022] Open
Abstract
Deep Learning has a large impact on medical image analysis and lately has been adopted for clinical use at the point of care. However, there is only a small number of reports of long-term studies that show the performance of deep neural networks (DNNs) in such an environment. In this study, we measured the long-term performance of a clinically optimized DNN for laryngeal glottis segmentation. We have collected the video footage for two years from an AI-powered laryngeal high-speed videoendoscopy imaging system and found that the footage image quality is stable across time. Next, we determined the DNN segmentation performance on lossy and lossless compressed data revealing that only 9% of recordings contain segmentation artifacts. We found that lossy and lossless compression is on par for glottis segmentation, however, lossless compression provides significantly superior image quality. Lastly, we employed continual learning strategies to continuously incorporate new data into the DNN to remove the aforementioned segmentation artifacts. With modest manual intervention, we were able to largely alleviate these segmentation artifacts by up to 81%. We believe that our suggested deep learning-enhanced laryngeal imaging platform consistently provides clinically sound results, and together with our proposed continual learning scheme will have a long-lasting impact on the future of laryngeal imaging.
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Deep Learning-Aided Automated Pneumonia Detection and Classification Using CXR Scans. COMPUTATIONAL INTELLIGENCE AND NEUROSCIENCE 2022; 2022:7474304. [PMID: 35936981 PMCID: PMC9351538 DOI: 10.1155/2022/7474304] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 04/20/2022] [Accepted: 07/08/2022] [Indexed: 12/15/2022]
Abstract
The COVID-19 pandemic has caused a worldwide catastrophe and widespread devastation that reeled almost all countries. The pandemic has mounted pressure on the existing healthcare system and caused panic and desperation. The gold testing standard for COVID-19 detection, reverse transcription-polymerase chain reaction (RT-PCR), has shown its limitations with 70% accuracy, contributing to the incorrect diagnosis that exaggerated the complexities and increased the fatalities. The new variations further pose unseen challenges in terms of their diagnosis and subsequent treatment. The COVID-19 virus heavily impacts the lungs and fills the air sacs with fluid causing pneumonia. Thus, chest X-ray inspection is a viable option if the inspection detects COVID-19-induced pneumonia, hence confirming the exposure of COVID-19. Artificial intelligence and machine learning techniques are capable of examining chest X-rays in order to detect patterns that can confirm the presence of COVID-19-induced pneumonia. This research used CNN and deep learning techniques to detect COVID-19-induced pneumonia from chest X-rays. Transfer learning with fine-tuning ensures that the proposed work successfully classifies COVID-19-induced pneumonia, regular pneumonia, and normal conditions. Xception, Visual Geometry Group 16, and Visual Geometry Group 19 are used to realize transfer learning. The experimental results were promising in terms of precision, recall, F1 score, specificity, false omission rate, false negative rate, false positive rate, and false discovery rate with a COVID-19-induced pneumonia detection accuracy of 98%. Experimental results also revealed that the proposed work has not only correctly identified COVID-19 exposure but also made a distinction between COVID-19-induced pneumonia and regular pneumonia, as the latter is a very common disease, while COVID-19 is more lethal. These results mitigated the concern and overlap in the diagnosis of COVID-19-induced pneumonia and regular pneumonia. With further integrations, it can be employed as a potential standard model in differentiating the various lung-related infections, including COVID-19.
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Wang L, Wu B, Wang X, Zhu Q, Xu K. Endoscopic image luminance enhancement based on the inverse square law for illuminance and retinex. Int J Med Robot 2022; 18:e2396. [PMID: 35318786 DOI: 10.1002/rcs.2396] [Citation(s) in RCA: 6] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/25/2021] [Revised: 03/20/2022] [Accepted: 03/20/2022] [Indexed: 12/16/2022]
Abstract
BACKGROUND In a single-port robotic system where the 3D endoscope possesses two bending segments, only point light sources can be integrated at the tip due to space limitations. However, point light sources usually provide non-uniform illumination, causing the endoscopic images to appear bright in the centre and dark near the corners. METHODS Based on the inverse square law for illuminance, an initial luminance weighting is first proposed to increase the image luminance uniformity. Then, a saturation-based model is proposed to finalise the luminance weighting to avoid overexposure and colour discrepancy, while the single-scale retinex (SSR) scheme is employed for noise control. RESULTS Via qualitative and quantitative comparisons, the proposed method performs effectively in enhancing the luminance and uniformity of endoscopic images, in terms of both visual perception and objective assessment. CONCLUSIONS The proposed method can effectively reduce the image degradation caused by point light sources.
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Affiliation(s)
- Longfei Wang
- School of Mechanical Engineering, Shanghai Jiao Tong University, Shanghai, China
| | - Baibo Wu
- School of Mechanical Engineering, Shanghai Jiao Tong University, Shanghai, China
| | - Xiang Wang
- School of Mechanical Engineering, Shanghai Jiao Tong University, Shanghai, China
| | - Qingyi Zhu
- Department of Urology, The Second Affiliated Hospital of Nanjing Medical University, Nanjing, China
| | - Kai Xu
- School of Mechanical Engineering, Shanghai Jiao Tong University, Shanghai, China
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Dasgupta A, Bakshi A, Mukherjee S, Das K, Talukdar S, Chatterjee P, Mondal S, Das P, Ghosh S, Som A, Roy P, Kundu R, Sarkar A, Biswas A, Paul K, Basak S, Manna K, Saha C, Mukhopadhyay S, Bhattacharyya NP, De RK. Epidemiological challenges in pandemic coronavirus disease (COVID-19): Role of artificial intelligence. WILEY INTERDISCIPLINARY REVIEWS. DATA MINING AND KNOWLEDGE DISCOVERY 2022; 12:e1462. [PMID: 35942397 PMCID: PMC9350133 DOI: 10.1002/widm.1462] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 06/04/2020] [Revised: 03/28/2022] [Accepted: 04/28/2022] [Indexed: 05/02/2023]
Abstract
World is now experiencing a major health calamity due to the coronavirus disease (COVID-19) pandemic, caused by the severe acute respiratory syndrome coronavirus clade 2. The foremost challenge facing the scientific community is to explore the growth and transmission capability of the virus. Use of artificial intelligence (AI), such as deep learning, in (i) rapid disease detection from x-ray or computed tomography (CT) or high-resolution CT (HRCT) images, (ii) accurate prediction of the epidemic patterns and their saturation throughout the globe, (iii) forecasting the disease and psychological impact on the population from social networking data, and (iv) prediction of drug-protein interactions for repurposing the drugs, has attracted much attention. In the present study, we describe the role of various AI-based technologies for rapid and efficient detection from CT images complementing quantitative real-time polymerase chain reaction and immunodiagnostic assays. AI-based technologies to anticipate the current pandemic pattern, prevent the spread of disease, and face mask detection are also discussed. We inspect how the virus transmits depending on different factors. We investigate the deep learning technique to assess the affinity of the most probable drugs to treat COVID-19. This article is categorized under:Application Areas > Health CareAlgorithmic Development > Biological Data MiningTechnologies > Machine Learning.
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Affiliation(s)
- Abhijit Dasgupta
- Department of Data Science, School of Interdisciplinary StudiesUniversity of Kalyani, KalyaniNadiaWest BengalIndia
| | - Abhisek Bakshi
- Department of Information TechnologyBengal Institute of TechnologyKolkataWest BengalIndia
| | - Srijani Mukherjee
- Department of Data Science, School of Interdisciplinary StudiesUniversity of Kalyani, KalyaniNadiaWest BengalIndia
| | - Kuntal Das
- Department of Data Science, School of Interdisciplinary StudiesUniversity of Kalyani, KalyaniNadiaWest BengalIndia
| | - Soumyajeet Talukdar
- Department of Data Science, School of Interdisciplinary StudiesUniversity of Kalyani, KalyaniNadiaWest BengalIndia
| | - Pratyayee Chatterjee
- Department of Data Science, School of Interdisciplinary StudiesUniversity of Kalyani, KalyaniNadiaWest BengalIndia
| | - Sagnik Mondal
- Department of Data Science, School of Interdisciplinary StudiesUniversity of Kalyani, KalyaniNadiaWest BengalIndia
| | - Puspita Das
- Department of Data Science, School of Interdisciplinary StudiesUniversity of Kalyani, KalyaniNadiaWest BengalIndia
| | - Subhrojit Ghosh
- Department of Data Science, School of Interdisciplinary StudiesUniversity of Kalyani, KalyaniNadiaWest BengalIndia
| | - Archisman Som
- Department of Data Science, School of Interdisciplinary StudiesUniversity of Kalyani, KalyaniNadiaWest BengalIndia
| | - Pritha Roy
- Department of Data Science, School of Interdisciplinary StudiesUniversity of Kalyani, KalyaniNadiaWest BengalIndia
| | - Rima Kundu
- Department of Data Science, School of Interdisciplinary StudiesUniversity of Kalyani, KalyaniNadiaWest BengalIndia
| | - Akash Sarkar
- Department of Data Science, School of Interdisciplinary StudiesUniversity of Kalyani, KalyaniNadiaWest BengalIndia
| | - Arnab Biswas
- Department of Data Science, School of Interdisciplinary StudiesUniversity of Kalyani, KalyaniNadiaWest BengalIndia
| | - Karnelia Paul
- Department of BiotechnologyUniversity of CalcuttaKolkataWest BengalIndia
| | - Sujit Basak
- Department of Physiology and BiophysicsStony Brook UniversityStony BrookNew YorkUSA
| | - Krishnendu Manna
- Department of Food and NutritionUniversity of Kalyani, KalyaniNadiaWest BengalIndia
| | - Chinmay Saha
- Department of Genome Science, School of Interdisciplinary StudiesUniversity of Kalyani, KalyaniNadiaWest BengalIndia
| | - Satinath Mukhopadhyay
- Department of Endocrinology and MetabolismInstitute of Post Graduate Medical Education and Research and Seth Sukhlal Karnani Memorial HospitalKolkataWest BengalIndia
| | - Nitai P. Bhattacharyya
- Department of Endocrinology and MetabolismInstitute of Post Graduate Medical Education and Research and Seth Sukhlal Karnani Memorial HospitalKolkataWest BengalIndia
| | - Rajat K. De
- Machine Intelligence UnitIndian Statistical InstituteKolkataWest BengalIndia
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A Medical Endoscope Image Enhancement Method Based on Improved Weighted Guided Filtering. MATHEMATICS 2022. [DOI: 10.3390/math10091423] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 02/06/2023]
Abstract
In clinical surgery, the quality of endoscopic images is degraded by noise. Blood, illumination changes, specular reflection, smoke, and other factors contribute to noise, which reduces the quality of an image in an occluded area, affects doctors’ judgment, prolongs the operation duration, and increases the operation risk. In this study, we proposed an improved weighted guided filtering algorithm to enhance endoscopic image tissue. An unsharp mask algorithm and an improved weighted guided filter were used to enhance vessel details and contours in endoscopic images. The scheme of the entire endoscopic image processing, which included detail enhancement, contrast enhancement, brightness enhancement, and highlight area removal, is presented. Compared with other algorithms, the proposed algorithm maintained edges and reduced halos efficiently, and its effectiveness was demonstrated using experiments. The peak signal-to-noise ratio and structural similarity of endoscopic images obtained using the proposed algorithm were the highest. The foreground–background detail variance–background variance improved. The proposed algorithm had a strong ability to suppress noise and could maintain the structure of original endoscopic images, which improved the details of tissue blood vessels. The findings of this study can provide guidelines for developing endoscopy devices.
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Punn NS, Agarwal S. CHS-Net: A Deep Learning Approach for Hierarchical Segmentation of COVID-19 via CT Images. Neural Process Lett 2022; 54:3771-3792. [PMID: 35310011 PMCID: PMC8924740 DOI: 10.1007/s11063-022-10785-x] [Citation(s) in RCA: 6] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 02/24/2022] [Indexed: 01/19/2023]
Abstract
The pandemic of novel severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) also known as COVID-19 has been spreading worldwide, causing rampant loss of lives. Medical imaging such as computed tomography (CT), X-ray, etc., plays a significant role in diagnosing the patients by presenting the visual representation of the functioning of the organs. However, for any radiologist analyzing such scans is a tedious and time-consuming task. The emerging deep learning technologies have displayed its strength in analyzing such scans to aid in the faster diagnosis of the diseases and viruses such as COVID-19. In the present article, an automated deep learning based model, COVID-19 hierarchical segmentation network (CHS-Net) is proposed that functions as a semantic hierarchical segmenter to identify the COVID-19 infected regions from lungs contour via CT medical imaging using two cascaded residual attention inception U-Net (RAIU-Net) models. RAIU-Net comprises of a residual inception U-Net model with spectral spatial and depth attention network (SSD) that is developed with the contraction and expansion phases of depthwise separable convolutions and hybrid pooling (max and spectral pooling) to efficiently encode and decode the semantic and varying resolution information. The CHS-Net is trained with the segmentation loss function that is the defined as the average of binary cross entropy loss and dice loss to penalize false negative and false positive predictions. The approach is compared with the recently proposed approaches and evaluated using the standard metrics like accuracy, precision, specificity, recall, dice coefficient and Jaccard similarity along with the visualized interpretation of the model prediction with GradCam++ and uncertainty maps. With extensive trials, it is observed that the proposed approach outperformed the recently proposed approaches and effectively segments the COVID-19 infected regions in the lungs.
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Qjidaa M, Ben-Fares A, Amakdouf H, El Mallahi M, Alami BE, Maaroufi M, Lakhssassi A, Qjidaa H. Recognizing COVID-19 from chest X-ray images for people in rural and remote areas based on deep transfer learning model. MULTIMEDIA TOOLS AND APPLICATIONS 2022; 81:13115-13135. [PMID: 35221780 PMCID: PMC8863907 DOI: 10.1007/s11042-022-12030-y] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 11/23/2020] [Revised: 03/23/2021] [Accepted: 01/04/2022] [Indexed: 05/09/2023]
Abstract
In this article, we propose Deep Transfer Learning (DTL) Model for recognizing covid-19 from chest x-ray images. The latter is less expensive, easily accessible to populations in rural and remote areas. In addition, the device for acquiring these images is easy to disinfect, clean and maintain. The main challenge is the lack of labeled training data needed to train convolutional neural networks. To overcome this issue, we propose to leverage Deep Transfer Learning architecture pre-trained on ImageNet dataset and trained Fine-Tuning on a dataset prepared by collecting normal, COVID-19, and other chest pneumonia X-ray images from different available databases. We take the weights of the layers of each network already pre-trained to our model and we only train the last layers of the network on our collected COVID-19 image dataset. In this way, we will ensure a fast and precise convergence of our model despite the small number of COVID-19 images collected. In addition, for improving the accuracy of our global model will only predict at the output the prediction having obtained a maximum score among the predictions of the seven pre-trained CNNs. The proposed model will address a three-class classification problem: COVID-19 class, pneumonia class, and normal class. To show the location of the important regions of the image which strongly participated in the prediction of the considered class, we will use the Gradient Weighted Class Activation Mapping (Grad-CAM) approach. A comparative study was carried out to show the robustness of the prediction of our model compared to the visual prediction of radiologists. The proposed model is more efficient with a test accuracy of 98%, an f1 score of 98.33%, an accuracy of 98.66% and a sensitivity of 98.33% at the time when the prediction by renowned radiologists could not exceed an accuracy of 63.34% with a sensitivity of 70% and an f1 score of 66.67%.
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Affiliation(s)
- Mamoun Qjidaa
- Faculty of Medicine and Pharmacy, Department of Radiology, Sidi Mohamed Ben Abdellah University, Fez, Morocco
| | - Anass Ben-Fares
- Faculty of Sciences Dhar El Mahraz, Laboratory of Computer Science Signals Automation and Cognitivism, Sidi Mohamed Ben Abdellah University, Fez, Morocco
| | - Hicham Amakdouf
- Faculty of Sciences Dhar El Mahraz, Laboratory of Computer Science Signals Automation and Cognitivism, Sidi Mohamed Ben Abdellah University, Fez, Morocco
| | - Mostafa El Mallahi
- High Normal School, Mathematics and Computer Sciences Department, Laboratory of Computer Science and Interdisciplinary Physics, Sidi Mohamed Ben Abdellah University, Fez, Morocco
| | - Badre-eddine Alami
- Faculty of Medicine and Pharmacy, Department of Radiology, Sidi Mohamed Ben Abdellah University, Fez, Morocco
| | - Mustapha Maaroufi
- Faculty of Medicine and Pharmacy, Department of Radiology, Sidi Mohamed Ben Abdellah University, Fez, Morocco
| | - Ahmed Lakhssassi
- Laboratory of Engineering and Advanced Micro Systems, University of Quebec in Outaouais, Gatineau, Canada
| | - Hassan Qjidaa
- Faculty of Sciences Dhar El Mahraz, Laboratory of Computer Science Signals Automation and Cognitivism, Sidi Mohamed Ben Abdellah University, Fez, Morocco
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Yao P, Usman M, Chen YH, German A, Andreadis K, Mages K, Rameau A. Applications of Artificial Intelligence to Office Laryngoscopy: A Scoping Review. Laryngoscope 2021; 132:1993-2016. [PMID: 34582043 DOI: 10.1002/lary.29886] [Citation(s) in RCA: 23] [Impact Index Per Article: 7.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/03/2021] [Revised: 09/15/2021] [Accepted: 09/17/2021] [Indexed: 01/16/2023]
Abstract
OBJECTIVES/HYPOTHESIS This scoping review aims to provide a broad overview of the applications of artificial intelligence (AI) to office laryngoscopy to identify gaps in knowledge and guide future research. STUDY DESIGN Scoping Review. METHODS Searches for studies on AI and office laryngoscopy were conducted in five databases. Title and abstract and then full-text screening were performed. Primary research studies published in English of any date were included. Studies were summarized by: AI applications, targeted conditions, imaging modalities, author affiliations, and dataset characteristics. RESULTS Studies focused on vocal fold vibration analysis (43%), lesion recognition (24%), and vocal fold movement determination (19%). The most frequently automated tasks were recognition of vocal fold nodules (19%), polyp (14%), paralysis (11%), paresis (8%), and cyst (7%). Imaging modalities included high-speed laryngeal videos (45%), stroboscopy (29%), and narrow band imaging endoscopy (7%). The body of literature was primarily authored by science, technology, engineering, and math (STEM) specialists (76%) with only 30 studies (31%) involving co-authorship by STEM specialists and otolaryngologists. Datasets were mostly from single institution (84%) and most commonly originated from Germany (23%), USA (16%), Spain (9%), Italy (8%), and China (8%). Demographic information was only reported in 39 studies (40%), with age and sex being the most commonly reported, whereas race/ethnicity and gender were not reported in any studies. CONCLUSION More interdisciplinary collaboration between STEM and otolaryngology research teams improved demographic reporting especially of race and ethnicity to ensure broad representation, and larger and more geographically diverse datasets will be crucial to future research on AI in office laryngoscopy. LEVEL OF EVIDENCE N/A Laryngoscope, 2021.
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Affiliation(s)
- Peter Yao
- Department of Otolaryngology-Head and Neck Surgery, Sean Parker Institute for the Voice, Weill Cornell Medicine, New York, New York, U.S.A
| | - Moon Usman
- Department of Otolaryngology-Head and Neck Surgery, Sean Parker Institute for the Voice, Weill Cornell Medicine, New York, New York, U.S.A
| | - Yu H Chen
- Department of Otolaryngology-Head and Neck Surgery, Sean Parker Institute for the Voice, Weill Cornell Medicine, New York, New York, U.S.A
| | - Alexander German
- Department of Otolaryngology-Head and Neck Surgery, Sean Parker Institute for the Voice, Weill Cornell Medicine, New York, New York, U.S.A
| | - Katerina Andreadis
- Department of Otolaryngology-Head and Neck Surgery, Sean Parker Institute for the Voice, Weill Cornell Medicine, New York, New York, U.S.A
| | - Keith Mages
- Department of Otolaryngology-Head and Neck Surgery, Sean Parker Institute for the Voice, Weill Cornell Medicine, New York, New York, U.S.A
| | - Anaïs Rameau
- Department of Otolaryngology-Head and Neck Surgery, Sean Parker Institute for the Voice, Weill Cornell Medicine, New York, New York, U.S.A
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12
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Chao Z, Xu W. A New General Maximum Intensity Projection Technology via the Hybrid of U-Net and Radial Basis Function Neural Network. J Digit Imaging 2021; 34:1264-1278. [PMID: 34508300 PMCID: PMC8432629 DOI: 10.1007/s10278-021-00504-8] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/12/2021] [Revised: 07/16/2021] [Accepted: 08/05/2021] [Indexed: 11/29/2022] Open
Abstract
Maximum intensity projection (MIP) technology is a computer visualization method that projects three-dimensional spatial data on a visualization plane. According to the specific purposes, the specific lab thickness and direction can be selected. This technology can better show organs, such as blood vessels, arteries, veins, and bronchi and so forth, from different directions, which could bring more intuitive and comprehensive results for doctors in the diagnosis of related diseases. However, in this traditional projection technology, the details of the small projected target are not clearly visualized when the projected target is not much different from the surrounding environment, which could lead to missed diagnosis or misdiagnosis. Therefore, it is urgent to develop a new technology that can better and clearly display the angiogram. However, to the best of our knowledge, research in this area is scarce. To fill this gap in the literature, in the present study, we propose a new method based on the hybrid of convolutional neural network (CNN) and radial basis function neural network (RBFNN) to synthesize the projection image. We first adopted the U-net to obtain feature or enhanced images to be projected; subsequently, the RBF neural network performed further synthesis processing for these data; finally, the projection images were obtained. For experimental data, in order to increase the robustness of the proposed algorithm, the following three different types of datasets were adopted: the vascular projection of the brain, the bronchial projection of the lung parenchyma, and the vascular projection of the liver. In addition, radiologist evaluation and five classic metrics of image definition were implemented for effective analysis. Finally, compared to the traditional MIP technology and other structures, the use of a large number of different types of data and superior experimental results proved the versatility and robustness of the proposed method.
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Affiliation(s)
- Zhen Chao
- College of Artificial Intelligence and Big Data for Medical Sciences, Shandong First Medical University & Shandong Academy of Medical Sciences, Huaiyin District, 6699 Qingdao Road, Jinan, 250117, Shandong, China.
- Research Lab for Medical Imaging and Digital Surgery, Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences, Shenzhen, China.
- Department of Radiation Convergence Engineering, College of Health Science, Yonsei University, 1 Yonseidae-gil, Wonju, Gangwon, 26493, South Korea.
| | - Wenting Xu
- Department of Radiation Convergence Engineering, College of Health Science, Yonsei University, 1 Yonseidae-gil, Wonju, Gangwon, 26493, South Korea
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13
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Wang S, Kang B, Ma J, Zeng X, Xiao M, Guo J, Cai M, Yang J, Li Y, Meng X, Xu B. A deep learning algorithm using CT images to screen for Corona virus disease (COVID-19). Eur Radiol 2021. [PMID: 33629156 DOI: 10.1101/2020.02.14.20023028] [Citation(s) in RCA: 217] [Impact Index Per Article: 72.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 05/09/2023]
Abstract
OBJECTIVE The outbreak of Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-COV-2) has caused more than 26 million cases of Corona virus disease (COVID-19) in the world so far. To control the spread of the disease, screening large numbers of suspected cases for appropriate quarantine and treatment are a priority. Pathogenic laboratory testing is typically the gold standard, but it bears the burden of significant false negativity, adding to the urgent need of alternative diagnostic methods to combat the disease. Based on COVID-19 radiographic changes in CT images, this study hypothesized that artificial intelligence methods might be able to extract specific graphical features of COVID-19 and provide a clinical diagnosis ahead of the pathogenic test, thus saving critical time for disease control. METHODS We collected 1065 CT images of pathogen-confirmed COVID-19 cases along with those previously diagnosed with typical viral pneumonia. We modified the inception transfer-learning model to establish the algorithm, followed by internal and external validation. RESULTS The internal validation achieved a total accuracy of 89.5% with a specificity of 0.88 and sensitivity of 0.87. The external testing dataset showed a total accuracy of 79.3% with a specificity of 0.83 and sensitivity of 0.67. In addition, in 54 COVID-19 images, the first two nucleic acid test results were negative, and 46 were predicted as COVID-19 positive by the algorithm, with an accuracy of 85.2%. CONCLUSION These results demonstrate the proof-of-principle for using artificial intelligence to extract radiological features for timely and accurate COVID-19 diagnosis. KEY POINTS • The study evaluated the diagnostic performance of a deep learning algorithm using CT images to screen for COVID-19 during the influenza season. • As a screening method, our model achieved a relatively high sensitivity on internal and external CT image datasets. • The model was used to distinguish between COVID-19 and other typical viral pneumonia, both of which have quite similar radiologic characteristics.
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Affiliation(s)
- Shuai Wang
- Department of Biochemistry and Molecular Biology, National Clinical Research Center for Cancer, Key Laboratory of Cancer Prevention and Therapy, Key Laboratory of Breast Cancer Prevention and Therapy, Ministry of Education, Tianjin Clinical Research Center for Cancer, Tianjin Medical University Cancer Institute and Hospital, Tianjin, 300060, China
- Department of Hepatobiliary Oncology, Tianjin Medical University Cancer Institute and Hospital, National Clinical Research Center for Cancer, Tianjin, 300060, China
| | - Bo Kang
- College of Intelligence and Computing, Tianjin University, Tianjin, 300350, China
- National Supercomputer Center in Tianjin, Tianjin, 300457, China
| | - Jinlu Ma
- Department of Radiation Oncology, First Affiliated Hospital, Xi'an Jiaotong University, Xi'an, China
| | - Xianjun Zeng
- Department of Radiology, Nanchang University First Hospital, Nanchang, China
| | - Mingming Xiao
- Department of Biochemistry and Molecular Biology, National Clinical Research Center for Cancer, Key Laboratory of Cancer Prevention and Therapy, Key Laboratory of Breast Cancer Prevention and Therapy, Ministry of Education, Tianjin Clinical Research Center for Cancer, Tianjin Medical University Cancer Institute and Hospital, Tianjin, 300060, China
| | - Jia Guo
- National Supercomputer Center in Tianjin, Tianjin, 300457, China
| | - Mengjiao Cai
- Department of Radiation Oncology, First Affiliated Hospital, Xi'an Jiaotong University, Xi'an, China
| | - Jingyi Yang
- Department of Radiation Oncology, First Affiliated Hospital, Xi'an Jiaotong University, Xi'an, China
| | - Yaodong Li
- Department of Radiology, No.8 Hospital, Xi'an Medical College, Xi'an, China
| | - Xiangfei Meng
- National Supercomputer Center in Tianjin, Tianjin, 300457, China.
| | - Bo Xu
- Department of Biochemistry and Molecular Biology, National Clinical Research Center for Cancer, Key Laboratory of Cancer Prevention and Therapy, Key Laboratory of Breast Cancer Prevention and Therapy, Ministry of Education, Tianjin Clinical Research Center for Cancer, Tianjin Medical University Cancer Institute and Hospital, Tianjin, 300060, China.
- Center for Intelligent Oncology, Chongqing University Cancer Hospital, Chongqing University School of Medicine, Chongqing, China.
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14
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Wang S, Kang B, Ma J, Zeng X, Xiao M, Guo J, Cai M, Yang J, Li Y, Meng X, Xu B. A deep learning algorithm using CT images to screen for Corona virus disease (COVID-19). Eur Radiol 2021. [PMID: 33629156 DOI: 10.1101/2020.02.14.20023028v5] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/23/2022]
Abstract
OBJECTIVE The outbreak of Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-COV-2) has caused more than 26 million cases of Corona virus disease (COVID-19) in the world so far. To control the spread of the disease, screening large numbers of suspected cases for appropriate quarantine and treatment are a priority. Pathogenic laboratory testing is typically the gold standard, but it bears the burden of significant false negativity, adding to the urgent need of alternative diagnostic methods to combat the disease. Based on COVID-19 radiographic changes in CT images, this study hypothesized that artificial intelligence methods might be able to extract specific graphical features of COVID-19 and provide a clinical diagnosis ahead of the pathogenic test, thus saving critical time for disease control. METHODS We collected 1065 CT images of pathogen-confirmed COVID-19 cases along with those previously diagnosed with typical viral pneumonia. We modified the inception transfer-learning model to establish the algorithm, followed by internal and external validation. RESULTS The internal validation achieved a total accuracy of 89.5% with a specificity of 0.88 and sensitivity of 0.87. The external testing dataset showed a total accuracy of 79.3% with a specificity of 0.83 and sensitivity of 0.67. In addition, in 54 COVID-19 images, the first two nucleic acid test results were negative, and 46 were predicted as COVID-19 positive by the algorithm, with an accuracy of 85.2%. CONCLUSION These results demonstrate the proof-of-principle for using artificial intelligence to extract radiological features for timely and accurate COVID-19 diagnosis. KEY POINTS • The study evaluated the diagnostic performance of a deep learning algorithm using CT images to screen for COVID-19 during the influenza season. • As a screening method, our model achieved a relatively high sensitivity on internal and external CT image datasets. • The model was used to distinguish between COVID-19 and other typical viral pneumonia, both of which have quite similar radiologic characteristics.
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Affiliation(s)
- Shuai Wang
- Department of Biochemistry and Molecular Biology, National Clinical Research Center for Cancer, Key Laboratory of Cancer Prevention and Therapy, Key Laboratory of Breast Cancer Prevention and Therapy, Ministry of Education, Tianjin Clinical Research Center for Cancer, Tianjin Medical University Cancer Institute and Hospital, Tianjin, 300060, China.,Department of Hepatobiliary Oncology, Tianjin Medical University Cancer Institute and Hospital, National Clinical Research Center for Cancer, Tianjin, 300060, China
| | - Bo Kang
- College of Intelligence and Computing, Tianjin University, Tianjin, 300350, China.,National Supercomputer Center in Tianjin, Tianjin, 300457, China
| | - Jinlu Ma
- Department of Radiation Oncology, First Affiliated Hospital, Xi'an Jiaotong University, Xi'an, China
| | - Xianjun Zeng
- Department of Radiology, Nanchang University First Hospital, Nanchang, China
| | - Mingming Xiao
- Department of Biochemistry and Molecular Biology, National Clinical Research Center for Cancer, Key Laboratory of Cancer Prevention and Therapy, Key Laboratory of Breast Cancer Prevention and Therapy, Ministry of Education, Tianjin Clinical Research Center for Cancer, Tianjin Medical University Cancer Institute and Hospital, Tianjin, 300060, China
| | - Jia Guo
- National Supercomputer Center in Tianjin, Tianjin, 300457, China
| | - Mengjiao Cai
- Department of Radiation Oncology, First Affiliated Hospital, Xi'an Jiaotong University, Xi'an, China
| | - Jingyi Yang
- Department of Radiation Oncology, First Affiliated Hospital, Xi'an Jiaotong University, Xi'an, China
| | - Yaodong Li
- Department of Radiology, No.8 Hospital, Xi'an Medical College, Xi'an, China
| | - Xiangfei Meng
- National Supercomputer Center in Tianjin, Tianjin, 300457, China.
| | - Bo Xu
- Department of Biochemistry and Molecular Biology, National Clinical Research Center for Cancer, Key Laboratory of Cancer Prevention and Therapy, Key Laboratory of Breast Cancer Prevention and Therapy, Ministry of Education, Tianjin Clinical Research Center for Cancer, Tianjin Medical University Cancer Institute and Hospital, Tianjin, 300060, China. .,Center for Intelligent Oncology, Chongqing University Cancer Hospital, Chongqing University School of Medicine, Chongqing, China.
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15
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Tian L, Hunt B, Bell MAL, Yi J, Smith JT, Ochoa M, Intes X, Durr NJ. Deep Learning in Biomedical Optics. Lasers Surg Med 2021; 53:748-775. [PMID: 34015146 PMCID: PMC8273152 DOI: 10.1002/lsm.23414] [Citation(s) in RCA: 25] [Impact Index Per Article: 8.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/11/2021] [Revised: 04/02/2021] [Accepted: 04/15/2021] [Indexed: 01/02/2023]
Abstract
This article reviews deep learning applications in biomedical optics with a particular emphasis on image formation. The review is organized by imaging domains within biomedical optics and includes microscopy, fluorescence lifetime imaging, in vivo microscopy, widefield endoscopy, optical coherence tomography, photoacoustic imaging, diffuse tomography, and functional optical brain imaging. For each of these domains, we summarize how deep learning has been applied and highlight methods by which deep learning can enable new capabilities for optics in medicine. Challenges and opportunities to improve translation and adoption of deep learning in biomedical optics are also summarized. Lasers Surg. Med. © 2021 Wiley Periodicals LLC.
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Affiliation(s)
- L. Tian
- Department of Electrical and Computer Engineering, Boston University, Boston, MA, USA
| | - B. Hunt
- Thayer School of Engineering, Dartmouth College, Hanover, NH, USA
| | - M. A. L. Bell
- Department of Electrical and Computer Engineering, Johns Hopkins University, Baltimore, MD, USA
- Department of Biomedical Engineering, Johns Hopkins University, Baltimore, MD, USA
- Department of Computer Science, Johns Hopkins University, Baltimore, MD, USA
| | - J. Yi
- Department of Biomedical Engineering, Johns Hopkins University, Baltimore, MD, USA
- Department of Ophthalmology, Johns Hopkins University, Baltimore, MD, USA
| | - J. T. Smith
- Center for Modeling, Simulation, and Imaging in Medicine, Rensselaer Polytechnic Institute, Troy, New York NY 12180
| | - M. Ochoa
- Center for Modeling, Simulation, and Imaging in Medicine, Rensselaer Polytechnic Institute, Troy, New York NY 12180
| | - X. Intes
- Center for Modeling, Simulation, and Imaging in Medicine, Rensselaer Polytechnic Institute, Troy, New York NY 12180
| | - N. J. Durr
- Department of Electrical and Computer Engineering, Johns Hopkins University, Baltimore, MD, USA
- Department of Biomedical Engineering, Johns Hopkins University, Baltimore, MD, USA
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Kist AM, Dürr S, Schützenberger A, Döllinger M. OpenHSV: an open platform for laryngeal high-speed videoendoscopy. Sci Rep 2021; 11:13760. [PMID: 34215788 PMCID: PMC8253769 DOI: 10.1038/s41598-021-93149-0] [Citation(s) in RCA: 13] [Impact Index Per Article: 4.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/04/2020] [Accepted: 06/03/2021] [Indexed: 11/22/2022] Open
Abstract
High-speed videoendoscopy is an important tool to study laryngeal dynamics, to quantify vocal fold oscillations, to diagnose voice impairments at laryngeal level and to monitor treatment progress. However, there is a significant lack of an open source, expandable research tool that features latest hardware and data analysis. In this work, we propose an open research platform termed OpenHSV that is based on state-of-the-art, commercially available equipment and features a fully automatic data analysis pipeline. A publicly available, user-friendly graphical user interface implemented in Python is used to interface the hardware. Video and audio data are recorded in synchrony and are subsequently fully automatically analyzed. Video segmentation of the glottal area is performed using efficient deep neural networks to derive glottal area waveform and glottal midline. Established quantitative, clinically relevant video and audio parameters were implemented and computed. In a preliminary clinical study, we recorded video and audio data from 28 healthy subjects. Analyzing these data in terms of image quality and derived quantitative parameters, we show the applicability, performance and usefulness of OpenHSV. Therefore, OpenHSV provides a valid, standardized access to high-speed videoendoscopy data acquisition and analysis for voice scientists, highlighting its use as a valuable research tool in understanding voice physiology. We envision that OpenHSV serves as basis for the next generation of clinical HSV systems.
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Affiliation(s)
- Andreas M Kist
- Division of Phoniatrics and Pediatric Audiology, Department of Otorhinolaryngology, Head and Neck Surgery, University Hospital Erlangen, Friedrich-Alexander-University Erlangen-Nürnberg, Waldstr. 1, 91054, Erlangen, Germany. .,Department of Artificial Intelligence in Biomedical Engineering, Friedrich-Alexander-University Erlangen-Nürnberg, Henkestr. 91, 91054, Erlangen, Germany.
| | - Stephan Dürr
- Division of Phoniatrics and Pediatric Audiology, Department of Otorhinolaryngology, Head and Neck Surgery, University Hospital Erlangen, Friedrich-Alexander-University Erlangen-Nürnberg, Waldstr. 1, 91054, Erlangen, Germany
| | - Anne Schützenberger
- Division of Phoniatrics and Pediatric Audiology, Department of Otorhinolaryngology, Head and Neck Surgery, University Hospital Erlangen, Friedrich-Alexander-University Erlangen-Nürnberg, Waldstr. 1, 91054, Erlangen, Germany
| | - Michael Döllinger
- Division of Phoniatrics and Pediatric Audiology, Department of Otorhinolaryngology, Head and Neck Surgery, University Hospital Erlangen, Friedrich-Alexander-University Erlangen-Nürnberg, Waldstr. 1, 91054, Erlangen, Germany
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17
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El Asnaoui K, Chawki Y. Using X-ray images and deep learning for automated detection of coronavirus disease. J Biomol Struct Dyn 2021; 39:3615-3626. [PMID: 32397844 DOI: 10.1109/access.2020.3010287] [Citation(s) in RCA: 474] [Impact Index Per Article: 158.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 05/24/2023]
Abstract
Coronavirus is still the leading cause of death worldwide. There are a set number of COVID-19 test units accessible in emergency clinics because of the expanding cases daily. Therefore, it is important to implement an automatic detection and classification system as a speedy elective finding choice to forestall COVID-19 spreading among individuals. Medical images analysis is one of the most promising research areas, it provides facilities for diagnosis and making decisions of a number of diseases such as Coronavirus. This paper conducts a comparative study of the use of the recent deep learning models (VGG16, VGG19, DenseNet201, Inception_ResNet_V2, Inception_V3, Resnet50, and MobileNet_V2) to deal with detection and classification of coronavirus pneumonia. The experiments were conducted using chest X-ray & CT dataset of 6087 images (2780 images of bacterial pneumonia, 1493 of coronavirus, 231 of Covid19, and 1583 normal) and confusion matrices are used to evaluate model performances. Results found out that the use of inception_Resnet_V2 and Densnet201 provide better results compared to other models used in this work (92.18% accuracy for Inception-ResNetV2 and 88.09% accuracy for Densnet201).Communicated by Ramaswamy H. Sarma.
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Affiliation(s)
- Khalid El Asnaoui
- Complex System Engineering and Human System, Mohammed VI Polytechnic University, Benguerir, Morocco
| | - Youness Chawki
- Faculty of Sciences and Techniques, Moulay Ismail University, Errachidia, Morocco
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18
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Gaur L, Bhatia U, Jhanjhi NZ, Muhammad G, Masud M. Medical image-based detection of COVID-19 using Deep Convolution Neural Networks. MULTIMEDIA SYSTEMS 2021; 29:1729-1738. [PMID: 33935377 PMCID: PMC8079233 DOI: 10.1007/s00530-021-00794-6] [Citation(s) in RCA: 21] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 10/09/2020] [Accepted: 04/05/2021] [Indexed: 05/08/2023]
Abstract
The demand for automatic detection of Novel Coronavirus or COVID-19 is increasing across the globe. The exponential rise in cases burdens healthcare facilities, and a vast amount of multimedia healthcare data is being explored to find a solution. This study presents a practical solution to detect COVID-19 from chest X-rays while distinguishing those from normal and impacted by Viral Pneumonia via Deep Convolution Neural Networks (CNN). In this study, three pre-trained CNN models (EfficientNetB0, VGG16, and InceptionV3) are evaluated through transfer learning. The rationale for selecting these specific models is their balance of accuracy and efficiency with fewer parameters suitable for mobile applications. The dataset used for the study is publicly available and compiled from different sources. This study uses deep learning techniques and performance metrics (accuracy, recall, specificity, precision, and F1 scores). The results show that the proposed approach produced a high-quality model, with an overall accuracy of 92.93%, COVID-19, a sensitivity of 94.79%. The work indicates a definite possibility to implement computer vision design to enable effective detection and screening measures.
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Affiliation(s)
- Loveleen Gaur
- Amity International Business School, Amity University, Noida, India
| | - Ujwal Bhatia
- Amity International Business School, Amity University, Noida, India
| | - N. Z. Jhanjhi
- School of Computer Science and Engineering SCE, Taylor’s University, Subang Jaya, Malaysia
| | - Ghulam Muhammad
- Research Chair of Pervasive and Mobile Computing, King Saud University, Riyadh 11543, Saudi Arabia
- Computer Engineering Department, College of Computer and Information Sciences, King Saud University, Riyadh 11543, Saudi Arabia
| | - Mehedi Masud
- Department of Computer Science, College of Computers and Information Technology, Taif University, P.O. Box 11099, Taif, 21944 Saudi Arabia
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19
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Wang S, Kang B, Ma J, Zeng X, Xiao M, Guo J, Cai M, Yang J, Li Y, Meng X, Xu B. A deep learning algorithm using CT images to screen for Corona virus disease (COVID-19). Eur Radiol 2021; 31:6096-6104. [PMID: 33629156 PMCID: PMC7904034 DOI: 10.1007/s00330-021-07715-1] [Citation(s) in RCA: 376] [Impact Index Per Article: 125.3] [Reference Citation Analysis] [Abstract] [Key Words] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/02/2020] [Revised: 12/20/2020] [Accepted: 01/26/2021] [Indexed: 01/17/2023]
Abstract
Objective The outbreak of Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-COV-2) has caused more than 26 million cases of Corona virus disease (COVID-19) in the world so far. To control the spread of the disease, screening large numbers of suspected cases for appropriate quarantine and treatment are a priority. Pathogenic laboratory testing is typically the gold standard, but it bears the burden of significant false negativity, adding to the urgent need of alternative diagnostic methods to combat the disease. Based on COVID-19 radiographic changes in CT images, this study hypothesized that artificial intelligence methods might be able to extract specific graphical features of COVID-19 and provide a clinical diagnosis ahead of the pathogenic test, thus saving critical time for disease control. Methods We collected 1065 CT images of pathogen-confirmed COVID-19 cases along with those previously diagnosed with typical viral pneumonia. We modified the inception transfer-learning model to establish the algorithm, followed by internal and external validation. Results The internal validation achieved a total accuracy of 89.5% with a specificity of 0.88 and sensitivity of 0.87. The external testing dataset showed a total accuracy of 79.3% with a specificity of 0.83 and sensitivity of 0.67. In addition, in 54 COVID-19 images, the first two nucleic acid test results were negative, and 46 were predicted as COVID-19 positive by the algorithm, with an accuracy of 85.2%. Conclusion These results demonstrate the proof-of-principle for using artificial intelligence to extract radiological features for timely and accurate COVID-19 diagnosis. Key Points • The study evaluated the diagnostic performance of a deep learning algorithm using CT images to screen for COVID-19 during the influenza season. • As a screening method, our model achieved a relatively high sensitivity on internal and external CT image datasets. • The model was used to distinguish between COVID-19 and other typical viral pneumonia, both of which have quite similar radiologic characteristics.
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Affiliation(s)
- Shuai Wang
- Department of Biochemistry and Molecular Biology, National Clinical Research Center for Cancer, Key Laboratory of Cancer Prevention and Therapy, Key Laboratory of Breast Cancer Prevention and Therapy, Ministry of Education, Tianjin Clinical Research Center for Cancer, Tianjin Medical University Cancer Institute and Hospital, Tianjin, 300060, China.,Department of Hepatobiliary Oncology, Tianjin Medical University Cancer Institute and Hospital, National Clinical Research Center for Cancer, Tianjin, 300060, China
| | - Bo Kang
- College of Intelligence and Computing, Tianjin University, Tianjin, 300350, China.,National Supercomputer Center in Tianjin, Tianjin, 300457, China
| | - Jinlu Ma
- Department of Radiation Oncology, First Affiliated Hospital, Xi'an Jiaotong University, Xi'an, China
| | - Xianjun Zeng
- Department of Radiology, Nanchang University First Hospital, Nanchang, China
| | - Mingming Xiao
- Department of Biochemistry and Molecular Biology, National Clinical Research Center for Cancer, Key Laboratory of Cancer Prevention and Therapy, Key Laboratory of Breast Cancer Prevention and Therapy, Ministry of Education, Tianjin Clinical Research Center for Cancer, Tianjin Medical University Cancer Institute and Hospital, Tianjin, 300060, China
| | - Jia Guo
- National Supercomputer Center in Tianjin, Tianjin, 300457, China
| | - Mengjiao Cai
- Department of Radiation Oncology, First Affiliated Hospital, Xi'an Jiaotong University, Xi'an, China
| | - Jingyi Yang
- Department of Radiation Oncology, First Affiliated Hospital, Xi'an Jiaotong University, Xi'an, China
| | - Yaodong Li
- Department of Radiology, No.8 Hospital, Xi'an Medical College, Xi'an, China
| | - Xiangfei Meng
- National Supercomputer Center in Tianjin, Tianjin, 300457, China.
| | - Bo Xu
- Department of Biochemistry and Molecular Biology, National Clinical Research Center for Cancer, Key Laboratory of Cancer Prevention and Therapy, Key Laboratory of Breast Cancer Prevention and Therapy, Ministry of Education, Tianjin Clinical Research Center for Cancer, Tianjin Medical University Cancer Institute and Hospital, Tianjin, 300060, China. .,Center for Intelligent Oncology, Chongqing University Cancer Hospital, Chongqing University School of Medicine, Chongqing, China.
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20
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Ibrahim AU, Ozsoz M, Serte S, Al-Turjman F, Yakoi PS. Pneumonia Classification Using Deep Learning from Chest X-ray Images During COVID-19. Cognit Comput 2021:1-13. [PMID: 33425044 PMCID: PMC7781428 DOI: 10.1007/s12559-020-09787-5] [Citation(s) in RCA: 60] [Impact Index Per Article: 20.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/19/2020] [Accepted: 10/21/2020] [Indexed: 12/15/2022]
Abstract
The outbreak of the novel corona virus disease (COVID-19) in December 2019 has led to global crisis around the world. The disease was declared pandemic by World Health Organization (WHO) on 11th of March 2020. Currently, the outbreak has affected more than 200 countries with more than 37 million confirmed cases and more than 1 million death tolls as of 10 October 2020. Reverse-transcription polymerase chain reaction (RT-PCR) is the standard method for detection of COVID-19 disease, but it has many challenges such as false positives, low sensitivity, expensive, and requires experts to conduct the test. As the number of cases continue to grow, there is a high need for developing a rapid screening method that is accurate, fast, and cheap. Chest X-ray (CXR) scan images can be considered as an alternative or a confirmatory approach as they are fast to obtain and easily accessible. Though the literature reports a number of approaches to classify CXR images and detect the COVID-19 infections, the majority of these approaches can only recognize two classes (e.g., COVID-19 vs. normal). However, there is a need for well-developed models that can classify a wider range of CXR images belonging to the COVID-19 class itself such as the bacterial pneumonia, the non-COVID-19 viral pneumonia, and the normal CXR scans. The current work proposes the use of a deep learning approach based on pretrained AlexNet model for the classification of COVID-19, non-COVID-19 viral pneumonia, bacterial pneumonia, and normal CXR scans obtained from different public databases. The model was trained to perform two-way classification (i.e., COVID-19 vs. normal, bacterial pneumonia vs. normal, non-COVID-19 viral pneumonia vs. normal, and COVID-19 vs. bacterial pneumonia), three-way classification (i.e., COVID-19 vs. bacterial pneumonia vs. normal), and four-way classification (i.e., COVID-19 vs. bacterial pneumonia vs. non-COVID-19 viral pneumonia vs. normal). For non-COVID-19 viral pneumonia and normal (healthy) CXR images, the proposed model achieved 94.43% accuracy, 98.19% sensitivity, and 95.78% specificity. For bacterial pneumonia and normal CXR images, the model achieved 91.43% accuracy, 91.94% sensitivity, and 100% specificity. For COVID-19 pneumonia and normal CXR images, the model achieved 99.16% accuracy, 97.44% sensitivity, and 100% specificity. For classification CXR images of COVID-19 pneumonia and non-COVID-19 viral pneumonia, the model achieved 99.62% accuracy, 90.63% sensitivity, and 99.89% specificity. For the three-way classification, the model achieved 94.00% accuracy, 91.30% sensitivity, and 84.78%. Finally, for the four-way classification, the model achieved an accuracy of 93.42%, sensitivity of 89.18%, and specificity of 98.92%.
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Affiliation(s)
| | - Mehmet Ozsoz
- Department of Biomedical Engineering, Near East University, Nicosia, Mersin 10, Turkey
| | - Sertan Serte
- Department of Electrical Engineering, Near East University, Nicosia, Mersin 10, Turkey
| | - Fadi Al-Turjman
- Department of Artificial Intelligence, Research Center for AI and IoT, Near East University, Nicosia, Mersin 10, Turkey
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21
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kumaraguru T, Abirami P, Darshan K, Angeline Kirubha S, Latha S, Muthu P. Smart access development for classifying lung disease with chest x-ray images using deep learning. MATERIALS TODAY: PROCEEDINGS 2021; 47:76-79. [PMID: 33880332 PMCID: PMC8049837 DOI: 10.1016/j.matpr.2021.03.650] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 03/19/2021] [Accepted: 03/26/2021] [Indexed: 10/25/2022]
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22
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Punn NS, Agarwal S. Automated diagnosis of COVID-19 with limited posteroanterior chest X-ray images using fine-tuned deep neural networks. APPL INTELL 2020; 51:2689-2702. [PMID: 34764554 PMCID: PMC7568031 DOI: 10.1007/s10489-020-01900-3] [Citation(s) in RCA: 73] [Impact Index Per Article: 18.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/17/2023]
Abstract
The novel coronavirus 2019 (COVID-19) is a respiratory syndrome that resembles pneumonia. The current diagnostic procedure of COVID-19 follows reverse-transcriptase polymerase chain reaction (RT-PCR) based approach which however is less sensitive to identify the virus at the initial stage. Hence, a more robust and alternate diagnosis technique is desirable. Recently, with the release of publicly available datasets of corona positive patients comprising of computed tomography (CT) and chest X-ray (CXR) imaging; scientists, researchers and healthcare experts are contributing for faster and automated diagnosis of COVID-19 by identifying pulmonary infections using deep learning approaches to achieve better cure and treatment. These datasets have limited samples concerned with the positive COVID-19 cases, which raise the challenge for unbiased learning. Following from this context, this article presents the random oversampling and weighted class loss function approach for unbiased fine-tuned learning (transfer learning) in various state-of-the-art deep learning approaches such as baseline ResNet, Inception-v3, Inception ResNet-v2, DenseNet169, and NASNetLarge to perform binary classification (as normal and COVID-19 cases) and also multi-class classification (as COVID-19, pneumonia, and normal case) of posteroanterior CXR images. Accuracy, precision, recall, loss, and area under the curve (AUC) are utilized to evaluate the performance of the models. Considering the experimental results, the performance of each model is scenario dependent; however, NASNetLarge displayed better scores in contrast to other architectures, which is further compared with other recently proposed approaches. This article also added the visual explanation to illustrate the basis of model classification and perception of COVID-19 in CXR images.
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23
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Gómez P, Kist AM, Schlegel P, Berry DA, Chhetri DK, Dürr S, Echternach M, Johnson AM, Kniesburges S, Kunduk M, Maryn Y, Schützenberger A, Verguts M, Döllinger M. BAGLS, a multihospital Benchmark for Automatic Glottis Segmentation. Sci Data 2020; 7:186. [PMID: 32561845 PMCID: PMC7305104 DOI: 10.1038/s41597-020-0526-3] [Citation(s) in RCA: 26] [Impact Index Per Article: 6.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/09/2019] [Accepted: 05/15/2020] [Indexed: 02/06/2023] Open
Abstract
Laryngeal videoendoscopy is one of the main tools in clinical examinations for voice disorders and voice research. Using high-speed videoendoscopy, it is possible to fully capture the vocal fold oscillations, however, processing the recordings typically involves a time-consuming segmentation of the glottal area by trained experts. Even though automatic methods have been proposed and the task is particularly suited for deep learning methods, there are no public datasets and benchmarks available to compare methods and to allow training of generalizing deep learning models. In an international collaboration of researchers from seven institutions from the EU and USA, we have created BAGLS, a large, multihospital dataset of 59,250 high-speed videoendoscopy frames with individually annotated segmentation masks. The frames are based on 640 recordings of healthy and disordered subjects that were recorded with varying technical equipment by numerous clinicians. The BAGLS dataset will allow an objective comparison of glottis segmentation methods and will enable interested researchers to train their own models and compare their methods.
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Affiliation(s)
- Pablo Gómez
- Division of Phoniatrics and Pediatric Audiology, Department of Otorhinolaryngology, Head and Neck Surgery, University Hospital Erlangen, Friedrich-Alexander University Erlangen-Nürnberg, Waldstraße 1, 91054, Erlangen, Germany.
| | - Andreas M Kist
- Division of Phoniatrics and Pediatric Audiology, Department of Otorhinolaryngology, Head and Neck Surgery, University Hospital Erlangen, Friedrich-Alexander University Erlangen-Nürnberg, Waldstraße 1, 91054, Erlangen, Germany.
| | - Patrick Schlegel
- Division of Phoniatrics and Pediatric Audiology, Department of Otorhinolaryngology, Head and Neck Surgery, University Hospital Erlangen, Friedrich-Alexander University Erlangen-Nürnberg, Waldstraße 1, 91054, Erlangen, Germany
| | - David A Berry
- Department of Head and Neck Surgery, David Geffen School of Medicine at the University of California, Los Angeles, Los Angeles, California, USA
| | - Dinesh K Chhetri
- Department of Head and Neck Surgery, David Geffen School of Medicine at the University of California, Los Angeles, Los Angeles, California, USA
| | - Stephan Dürr
- Division of Phoniatrics and Pediatric Audiology, Department of Otorhinolaryngology, Head and Neck Surgery, University Hospital Erlangen, Friedrich-Alexander University Erlangen-Nürnberg, Waldstraße 1, 91054, Erlangen, Germany
| | - Matthias Echternach
- Division of Phoniatrics and Pediatric Audiology, Department of Otorhinolaryngology, Munich University Hospital (LMU), Munich, Germany
| | - Aaron M Johnson
- NYU Voice Center, Department of Otolaryngology - Head and Neck Surgery, New York University School of Medicine, New York, New York, USA
| | - Stefan Kniesburges
- Division of Phoniatrics and Pediatric Audiology, Department of Otorhinolaryngology, Head and Neck Surgery, University Hospital Erlangen, Friedrich-Alexander University Erlangen-Nürnberg, Waldstraße 1, 91054, Erlangen, Germany
| | - Melda Kunduk
- Department of Communication Sciences and Disorders, Louisiana State University, Baton Rouge, Louisiana, USA
| | - Youri Maryn
- European Institute for ORL-HNS, Department of Otorhinolaryngology and Head & Neck Surgery, Sint-Augustinus GZA, Wilrijk, Belgium
- Department of Speech, Language and Hearing sciences, University of Ghent, Ghent, Belgium
- Faculty of Education, Health and Social Work, University College Ghent, Ghent, Belgium
- Faculty of Psychology and Educational Sciences, School of Logopedics, Université Catholique de Louvain, Louvain-la-Neuve, Belgium
- Faculty of Medicine and Health Sciences, University of Antwerp, Antwerp, Belgium
| | - Anne Schützenberger
- Division of Phoniatrics and Pediatric Audiology, Department of Otorhinolaryngology, Head and Neck Surgery, University Hospital Erlangen, Friedrich-Alexander University Erlangen-Nürnberg, Waldstraße 1, 91054, Erlangen, Germany
| | - Monique Verguts
- European Institute for ORL-HNS, Department of Otorhinolaryngology and Head & Neck Surgery, Sint-Augustinus GZA, Wilrijk, Belgium
- Department of Otorhinolaryngology and Voice Disorders, Diest General Hospital, Diest, Belgium
| | - Michael Döllinger
- Division of Phoniatrics and Pediatric Audiology, Department of Otorhinolaryngology, Head and Neck Surgery, University Hospital Erlangen, Friedrich-Alexander University Erlangen-Nürnberg, Waldstraße 1, 91054, Erlangen, Germany
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24
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Transfer learning for informative-frame selection in laryngoscopic videos through learned features. Med Biol Eng Comput 2020; 58:1225-1238. [DOI: 10.1007/s11517-020-02127-7] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/12/2019] [Accepted: 01/07/2020] [Indexed: 02/06/2023]
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25
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Abstract
This review provides a comprehensive compilation, from a digital image processing point of view of the most important techniques currently developed to characterize and quantify the vibration behaviour of the vocal folds, along with a detailed description of the laryngeal image modalities currently used in the clinic. The review presents an overview of the most significant glottal-gap segmentation and facilitative playbacks techniques used in the literature for the mentioned purpose, and shows the drawbacks and challenges that still remain unsolved to develop robust vocal folds vibration function analysis tools based on digital image processing.
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26
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Maryn Y, Verguts M, Demarsin H, van Dinther J, Gomez P, Schlegel P, Döllinger M. Intersegmenter Variability in High-Speed Laryngoscopy-Based Glottal Area Waveform Measures. Laryngoscope 2019; 130:E654-E661. [PMID: 31840827 DOI: 10.1002/lary.28475] [Citation(s) in RCA: 14] [Impact Index Per Article: 2.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/23/2019] [Accepted: 11/26/2019] [Indexed: 12/31/2022]
Abstract
OBJECTIVES/HYPOTHESIS High-speed videoendoscopy (HSV) has potential to objectively quantify vibratory vocal fold characteristics during phonation. Glottal Analysis Tools (GAT) version 2018, developed in Erlangen, Germany, is software for determining various glottal area waveform (GAW) quantities. Before having GAT analyze HSV videos, segmenters have to define glottis manually across videos in a semiautomatic segmentation protocol. Such interventions are hypothesized to induce variability of subsequent GAW measure computation across segmenters and may attenuate GAT measures' reliability to a certain point. This study explored intersegmenter variability in GAT's GAW measures based on semiautomatic image processing. STUDY DESIGN Cohort study of rater reliability. METHODS In total, 20 HSV videos from normophonic and dysphonic subjects with various laryngeal disorders were selected for this study and segmented by three trained segmenters. They separately segmented glottis areas in the same frame sets of the videos. Upon analysis of GAW, GAT offers 46 measures related to topologic GAW dynamic characteristics, GAW periodicity and perturbation characteristics, and GAW harmonic components. To address GAT's reliability, intersegmenter-based variability in these measures was examined with intraclass correlation coefficient (ICC). RESULTS In general, ICC behavior of the 46 GAW measures across three raters was highly acceptable. ICC of one parameter was moderate (0.5 < ICC < 0.75), good for seven parameters (0.75 < ICC < 0.9), and excellent for 38 parameters (0.9 < ICC). CONCLUSIONS Overall, high ICC values confirm clinical applicability of GAT for objective and quantitative assessment of HSV. Small intersegmenter differences with actual small parameter differences suggest that manual or semiautomatic segmentation in GAT does not noticeably influence clinical assessment outcome. To guarantee the software's performance, we suggest segmentation training before clinical application. LEVEL OF EVIDENCE 2b Laryngoscope, 130:E654-E661, 2020.
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Affiliation(s)
- Youri Maryn
- Department of Otorhinolaryngology-Head and Neck Surgery, European Institute for Otorhinolaryngology-Head and Neck Surgery, GasthuisZusters Antwerpen Sint-Augustinus, Wilrijk/Antwerp, Belgium.,Department of Speech, Language, and Hearing Sciences, University of Ghent, Ghent, Belgium.,Faculty of Education, Health, and Social Work, University College of Ghent, Ghent, Belgium.,Faculty of Psychology and Educational Sciences, School of Logopedics, Université Catholique de Louvain, Louvain-la-Neuve, Belgium.,Faculty of Medicine and Health Sciences, University of Antwerp, Antwerp, Belgium.,Phonanium, Lokeren, Belgium
| | - Monique Verguts
- Department of Otorhinolaryngology-Head and Neck Surgery, European Institute for Otorhinolaryngology-Head and Neck Surgery, GasthuisZusters Antwerpen Sint-Augustinus, Wilrijk/Antwerp, Belgium.,Department of Otorhinolaryngology and Voice Disorders, Diest General Hospital, Diest, Belgium
| | - Hannelore Demarsin
- Department of Otorhinolaryngology-Head and Neck Surgery, European Institute for Otorhinolaryngology-Head and Neck Surgery, GasthuisZusters Antwerpen Sint-Augustinus, Wilrijk/Antwerp, Belgium
| | - Joost van Dinther
- Department of Otorhinolaryngology-Head and Neck Surgery, European Institute for Otorhinolaryngology-Head and Neck Surgery, GasthuisZusters Antwerpen Sint-Augustinus, Wilrijk/Antwerp, Belgium
| | - Pablo Gomez
- Division of Phoniatrics and Pediatric Audiology, Department of Otorhinolaryngology-Head and Neck Surgery, University Hospital Erlangen, Friedrich-Alexander University Erlangen-Nürnberg, Erlangen, Germany
| | - Patrick Schlegel
- Division of Phoniatrics and Pediatric Audiology, Department of Otorhinolaryngology-Head and Neck Surgery, University Hospital Erlangen, Friedrich-Alexander University Erlangen-Nürnberg, Erlangen, Germany
| | - Michael Döllinger
- Division of Phoniatrics and Pediatric Audiology, Department of Otorhinolaryngology-Head and Neck Surgery, University Hospital Erlangen, Friedrich-Alexander University Erlangen-Nürnberg, Erlangen, Germany
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