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Metin N Gurcan - One of the best experts on this subject based on the ideXlab platform.
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digital otoscopy videos versus composite images a reader study to compare the accuracy of ent physicians
Laryngoscope, 2020Co-Authors: Hamidullah Binol, Theodoros N Teknos, Garth F Essig, Charles A Elmaraghy, Nazhat Tajschaal, Jay Shah, Jameson K Mattingly, Michael S Harris, Muhammad Khalid Khan Niazi, Metin N GurcanAbstract:Objectives/hypothesis With the increasing emphasis on developing effective telemedicine approaches in Otolaryngology, this study explored whether a single composite image stitched from a digital otoscopy video provides acceptable diagnostic information to make an accurate diagnosis, as compared with that provided by the full video. Study design Diagnostic survey analysis. Methods Five Ear, Nose, and Throat (ENT) physicians reviewed the same set of 78 digital Otoscope eardrum videos from four eardrum conditions: normal, effusion, retraction, and tympanosclerosis, along with the composite images generated by a SelectStitch method that selectively uses video frames with computer-assisted selection, as well as a Stitch method that incorporates all the video frames. Participants provided a diagnosis for each item along with a rating of diagnostic confidence. Diagnostic accuracy for each pathology of SelectStitch was compared with accuracy when reviewing the entire video clip and when reviewing the Stitch image. Results There were no significant differences in diagnostic accuracy for physicians reviewing SelectStitch images and full video clips, but both provided better diagnostic accuracy than Stitch images. The inter-reader agreement was moderate. Conclusions Equal to using full video clips, composite images of eardrums generated by SelectStitch provided sufficient information for ENTs to make the correct diagnoses for most pathologies. These findings suggest that use of a composite eardrum image may be sufficient for telemedicine approaches to ear diagnosis, eliminating the need for storage and transmission of large video files, along with future applications for improved documentation in electronic medical record systems, patient/family counseling, and clinical training. Level of evidence 3 Laryngoscope, 2020.
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digital otoscopy videos versus composite images a reader study to compare the accuracy of ent physicians
medRxiv, 2020Co-Authors: Hamidullah Binol, Theodoros N Teknos, Garth F Essig, Charles A Elmaraghy, Nazhat Tajschaal, Khalid Khan M Niazi, Jay Shah, Jameson K Mattingly, Michael S Harris, Metin N GurcanAbstract:Objectives: With the increasing emphasis on developing effective telemedicine approaches in Otolaryngology, this study explored whether a single composite image stitched from a digital otoscopy video provides acceptable diagnostic information to make an accurate diagnosis, as compared with that provided by the full video. Methods: Five Ear, Nose, and Throat (ENT) physicians reviewed the same set of 78 digital Otoscope eardrum videos from four eardrum conditions: normal, effusion, retraction, and tympanosclerosis, along with the composite images generated by a SelectStitch method that selectively uses video frames with computer-assisted selection, as well as a Stitch method that incorporates all the video frames. Participants provided a diagnosis for each item along with a rating of diagnostic confidence. Diagnostic accuracy for each pathology of SelectStitch was compared with accuracy when reviewing the entire video clip and when reviewing the Stitch image. Results: There were no significant differences in diagnostic accuracy for physicians reviewing SelectStitch images and full video clips, but both provided better diagnostic accuracy than Stitch images. The inter-reader agreement was moderate. Conclusion: Equal to using full video clips, composite images of eardrums generated by SelectStitch provided sufficient information for ENTs to make the correct diagnoses for most pathologies. These findings suggest that use of a composite eardrum image may be sufficient for telemedicine approaches to ear diagnosis, eliminating the need for storage and transmission of large video files, along with future applications for improved documentation in electronic medical record systems, patient/family counseling, and clinical training.
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decision fusion on image analysis and tympanometry to detect eardrum abnormalities
Medical Imaging 2020: Computer-Aided Diagnosis, 2020Co-Authors: Hamidullah Binol, Aaron C Moberly, Theodoros N Teknos, Garth F Essig, Charles A Elmaraghy, Nazhat Tajschaal, Khalid Khan M Niazi, Jay Shah, Metin N GurcanAbstract:Ear diseases are frequently occurring conditions affecting the majority of the pediatric population, potentially resulting in hearing loss and communication disabilities. The current standard of care in diagnosing ear diseases includes a visual examination of the tympanic membrane (TM) by a medical expert with a range of available Otoscopes. However, visual examination is subjective and depends on various factors, including the experience of the expert. This work proposes a decision fusion mechanism to combine predictions obtained from digital otoscopy images and biophysical measurements (obtained through tympanometry) for the detection of eardrum abnormalities. Our database consisted of 73 tympanometry records along with digital otoscopy videos. For the tympanometry aspect, we trained a random forest classifier (RF) using raw tympanometry attributes. Additionally, we mimicked a clinician’s decision on tympanometry findings using the normal range of the tympanogram values provided by a clinical guide. Moreover, we re-trained Inception-ResNet-v2 to classify TM images selected from each otoscopic video. After obtaining predictions from each of three different sources, we performed a majority voting-based decision fusion technique to reach the final decision. Experimental results show that the proposed decision fusion method improved the classification accuracy, positive predictive value, and negative predictive value in comparison to the single classifiers. The results revealed that the accuracies are 64.4% for the clinical evaluations of tympanometry, 76.7% for the computerized analysis of tympanometry data, and 74.0% for the TM image analysis while our decision fusion methodology increases the classification accuracy to 84.9%. To the best of our knowledge, this is the first study to fuse the data from digital otoscopy and tympanometry. Preliminary results suggest that fusing information from different sources of sensors may provide complementary information for accurate and computerized diagnosis of TM-related abnormalities.
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autoscope automated otoscopy image analysis to diagnose ear pathology and use of clinically motivated eardrum features
Proceedings of SPIE, 2017Co-Authors: Caglar Senaras, Aaron C Moberly, Theodoros N Teknos, Garth F Essig, Charles A Elmaraghy, Nazhat Tajschaal, Metin N GurcanAbstract:In this study, we propose an automated otoscopy image analysis system called Autoscope. To the best of our knowledge, Autoscope is the first system designed to detect a wide range of eardrum abnormalities by using high-resolution Otoscope images and report the condition of the eardrum as “normal” or “abnormal.” In order to achieve this goal, first, we developed a preprocessing step to reduce camera-specific problems, detect the region of interest in the image, and prepare the image for further analysis. Subsequently, we designed a new set of clinically motivated eardrum features (CMEF). Furthermore, we evaluated the potential of the visual MPEG-7 descriptors for the task of tympanic membrane image classification. Then, we fused the information extracted from the CMEF and state-of-the-art computer vision features (CVF), which included MPEG-7 descriptors and two additional features together, using a state of the art classifier. In our experiments, 247 tympanic membrane images with 14 different types of abnormality were used, and Autoscope was able to classify the given tympanic membrane images as normal or abnormal with 84.6% accuracy.
Tulio A. Valdez - One of the best experts on this subject based on the ideXlab platform.
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Shortwave infrared otoscopy for diagnosis of middle ear effusions: a machine-learning-based approach
'Springer Science and Business Media LLC', 2021Co-Authors: Rustin G. Kashani, Marcel C. Młyńczak, David Zarabanda, Paola Solis-pazmino, David M. Huland, Iram N. Ahmad, Surya P. Singh, Tulio A. ValdezAbstract:Abstract Otitis media, a common disease marked by the presence of fluid within the middle ear space, imparts a significant global health and economic burden. Identifying an effusion through the tympanic membrane is critical to diagnostic success but remains challenging due to the inherent limitations of visible light otoscopy and user interpretation. Here we describe a powerful diagnostic approach to otitis media utilizing advancements in otoscopy and machine learning. We developed an Otoscope that visualizes middle ear structures and fluid in the shortwave infrared region, holding several advantages over traditional approaches. Images were captured in vivo and then processed by a novel machine learning based algorithm. The model predicts the presence of effusions with greater accuracy than current techniques, offering specificity and sensitivity over 90%. This platform has the potential to reduce costs and resources associated with otitis media, especially as improvements are made in shortwave imaging and machine learning
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iphone Otoscopes currently available but reliable for tele otoscopy in the hands of parents
International Journal of Pediatric Otorhinolaryngology, 2018Co-Authors: Manan Udayan Shah, Tulio A. Valdez, Maheep Sohal, Christopher R GrindleAbstract:Abstract Objectives Tele-otoscopy has been validated for tympanostomy surveillance and remote diagnosis when images are recorded by trained professionals. The CellScope iPhone Otoscope is a device that may be used for tele-otoscopy and it enables parents to record their children's ear examinations and send the films for remote physician diagnosis. This study aims to determine the ability to diagnose, and the reliability of the diagnosis when utilizing video exams obtained by a parent versus video exams obtained by an otolaryngologist. Methods Parents of children ages 17 years or younger attempted recordings of the tympanic membrane of their children with the CellScope after a video tutorial; a physician subsequently used the device to record the same ear. Recordings occurred prior to standard pediatric otolaryngology office evaluation. Later, a remote pediatric otolaryngologist attempted diagnosis solely based on the videos, blinded to whether the examination was filmed by a parent or physician. Interrater reliability between video diagnosis and original diagnosis on pneumatic otoscopy was measured, and objective tympanic membrane landmarks visualized on the films were recorded. Results Eighty ears were enrolled and recorded. There was low interrater agreement (k = 0.42) between diagnosis based on parent videos as compared with pneumatic otoscopy. There was high agreement (k = 0.71) between diagnosis based on physician videos and pneumatic otoscopy. Physician videos and parent videos had only slight agreement on objective landmarks identified (k = 0.087). Conclusions iPhone otoscopy provides reliable tele-otoscopy images in when used by trained professionals but, currently, images obtained by parents are not suitable for use in diagnosis.
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a short wave infrared Otoscope for middle ear disease diagnostics conference presentation
Proceedings of SPIE, 2016Co-Authors: Jessica A Carr, Tulio A. Valdez, Oliver T Bruns, Moungi G BawendiAbstract:Otitis media, a range of inflammatory conditions of the middle ear, is the second most common illness diagnosed in children. However, the diagnosis can be challenging, particularly in pediatric patients. Otitis media is commonly over-diagnosed and over-treated and has been identified as one of the primary factors in increased antibiotic resistance. We describe the development of a short-wave infrared (SWIR) Otoscope for objective middle ear effusion diagnosis. The SWIR Otoscope can unambiguously detect the presence of middle ear fluid based on its strong light absorption in the SWIR. This absorption causes a stark, visual contrast between the presence and absence of fluid behind the tympanic membrane. Additionally, when there is no middle ear fluid, the deeper tissue penetration of SWIR light allows the SWIR Otoscope to better visualize middle ear anatomy through the tympanic membrane than is possible with visible light. We demonstrate that in healthy, adult human ears, SWIR otoscopy can image a range of middle ear anatomy, including landmarks of the entire ossicular chain, the promontory, the round window niche, and the chorda tympani. We suggest that SWIR otoscopy can provide valuable diagnostic information complementary to that provided by visible pneumotoscopy in the diagnosis of middle ear effusions, otitis media, and other maladies of the middle ear.
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Multi-color reflectance imaging of middle ear pathology in vivo
Analytical and bioanalytical chemistry, 2015Co-Authors: Tulio A. Valdez, Nicolas Spegazzini, Kaitlyn Longo, Christopher Grindle, Donald Peterson, Rishikesh Pandey, Ishan BarmanAbstract:Otoscopic examination using white-light illumination has remained virtually unchanged for well over a century. However, the limited contrast of white-light otoscopy constrains the ability to make accurate assessment of middle ear pathology and is subject to significant observer variability. Here, we employ a modified Otoscope with multi-color imaging capabilities for superior characterization of the middle ear constituents in vivo and for enhanced diagnosis of acute otitis media and cholesteatoma. In this pilot study, five patients undergoing surgery for tympanostomy tube placement and congenital cholesteatoma excision were imaged using the custom-designed multi-color video-rate reflectance imaging system. We show that the multi-color imaging approach offers an increase in image contrast, thereby enabling clear visualization of the middle ear constituents, especially of the tympanic membrane vascularity. Differential absorption at the multiple wavelengths provides a measure of biochemical and morphological information, and the rapid acquisition and analysis of these images aids in objective evaluation of the middle ear pathology. Our pilot study shows the potential of using label-free narrow-band reflectance imaging to differentiate middle ear pathological conditions from normal middle ear. This technique can aid in obtaining objective and reproducible diagnoses as well as provide assistance in guiding excisional procedures.
Garth F Essig - One of the best experts on this subject based on the ideXlab platform.
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digital otoscopy videos versus composite images a reader study to compare the accuracy of ent physicians
Laryngoscope, 2020Co-Authors: Hamidullah Binol, Theodoros N Teknos, Garth F Essig, Charles A Elmaraghy, Nazhat Tajschaal, Jay Shah, Jameson K Mattingly, Michael S Harris, Muhammad Khalid Khan Niazi, Metin N GurcanAbstract:Objectives/hypothesis With the increasing emphasis on developing effective telemedicine approaches in Otolaryngology, this study explored whether a single composite image stitched from a digital otoscopy video provides acceptable diagnostic information to make an accurate diagnosis, as compared with that provided by the full video. Study design Diagnostic survey analysis. Methods Five Ear, Nose, and Throat (ENT) physicians reviewed the same set of 78 digital Otoscope eardrum videos from four eardrum conditions: normal, effusion, retraction, and tympanosclerosis, along with the composite images generated by a SelectStitch method that selectively uses video frames with computer-assisted selection, as well as a Stitch method that incorporates all the video frames. Participants provided a diagnosis for each item along with a rating of diagnostic confidence. Diagnostic accuracy for each pathology of SelectStitch was compared with accuracy when reviewing the entire video clip and when reviewing the Stitch image. Results There were no significant differences in diagnostic accuracy for physicians reviewing SelectStitch images and full video clips, but both provided better diagnostic accuracy than Stitch images. The inter-reader agreement was moderate. Conclusions Equal to using full video clips, composite images of eardrums generated by SelectStitch provided sufficient information for ENTs to make the correct diagnoses for most pathologies. These findings suggest that use of a composite eardrum image may be sufficient for telemedicine approaches to ear diagnosis, eliminating the need for storage and transmission of large video files, along with future applications for improved documentation in electronic medical record systems, patient/family counseling, and clinical training. Level of evidence 3 Laryngoscope, 2020.
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digital otoscopy videos versus composite images a reader study to compare the accuracy of ent physicians
medRxiv, 2020Co-Authors: Hamidullah Binol, Theodoros N Teknos, Garth F Essig, Charles A Elmaraghy, Nazhat Tajschaal, Khalid Khan M Niazi, Jay Shah, Jameson K Mattingly, Michael S Harris, Metin N GurcanAbstract:Objectives: With the increasing emphasis on developing effective telemedicine approaches in Otolaryngology, this study explored whether a single composite image stitched from a digital otoscopy video provides acceptable diagnostic information to make an accurate diagnosis, as compared with that provided by the full video. Methods: Five Ear, Nose, and Throat (ENT) physicians reviewed the same set of 78 digital Otoscope eardrum videos from four eardrum conditions: normal, effusion, retraction, and tympanosclerosis, along with the composite images generated by a SelectStitch method that selectively uses video frames with computer-assisted selection, as well as a Stitch method that incorporates all the video frames. Participants provided a diagnosis for each item along with a rating of diagnostic confidence. Diagnostic accuracy for each pathology of SelectStitch was compared with accuracy when reviewing the entire video clip and when reviewing the Stitch image. Results: There were no significant differences in diagnostic accuracy for physicians reviewing SelectStitch images and full video clips, but both provided better diagnostic accuracy than Stitch images. The inter-reader agreement was moderate. Conclusion: Equal to using full video clips, composite images of eardrums generated by SelectStitch provided sufficient information for ENTs to make the correct diagnoses for most pathologies. These findings suggest that use of a composite eardrum image may be sufficient for telemedicine approaches to ear diagnosis, eliminating the need for storage and transmission of large video files, along with future applications for improved documentation in electronic medical record systems, patient/family counseling, and clinical training.
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decision fusion on image analysis and tympanometry to detect eardrum abnormalities
Medical Imaging 2020: Computer-Aided Diagnosis, 2020Co-Authors: Hamidullah Binol, Aaron C Moberly, Theodoros N Teknos, Garth F Essig, Charles A Elmaraghy, Nazhat Tajschaal, Khalid Khan M Niazi, Jay Shah, Metin N GurcanAbstract:Ear diseases are frequently occurring conditions affecting the majority of the pediatric population, potentially resulting in hearing loss and communication disabilities. The current standard of care in diagnosing ear diseases includes a visual examination of the tympanic membrane (TM) by a medical expert with a range of available Otoscopes. However, visual examination is subjective and depends on various factors, including the experience of the expert. This work proposes a decision fusion mechanism to combine predictions obtained from digital otoscopy images and biophysical measurements (obtained through tympanometry) for the detection of eardrum abnormalities. Our database consisted of 73 tympanometry records along with digital otoscopy videos. For the tympanometry aspect, we trained a random forest classifier (RF) using raw tympanometry attributes. Additionally, we mimicked a clinician’s decision on tympanometry findings using the normal range of the tympanogram values provided by a clinical guide. Moreover, we re-trained Inception-ResNet-v2 to classify TM images selected from each otoscopic video. After obtaining predictions from each of three different sources, we performed a majority voting-based decision fusion technique to reach the final decision. Experimental results show that the proposed decision fusion method improved the classification accuracy, positive predictive value, and negative predictive value in comparison to the single classifiers. The results revealed that the accuracies are 64.4% for the clinical evaluations of tympanometry, 76.7% for the computerized analysis of tympanometry data, and 74.0% for the TM image analysis while our decision fusion methodology increases the classification accuracy to 84.9%. To the best of our knowledge, this is the first study to fuse the data from digital otoscopy and tympanometry. Preliminary results suggest that fusing information from different sources of sensors may provide complementary information for accurate and computerized diagnosis of TM-related abnormalities.
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diagnostic accuracy and confidence for otoscopy are medical students receiving sufficient training
Laryngoscope, 2019Co-Authors: Weston L Niermeyer, Garth F Essig, Ramez Philips, Aaron C MoberlyAbstract:OBJECTIVES/HYPOTHESIS To assess the confidence and abilities of medical students to diagnose specific types of otologic pathology, and to determine how different training experiences in medical school impact these outcomes. STUDY DESIGN Survey analysis. METHODS Sixty third- and fourth-year medical students completed a computerized online survey. Participants answered questions about their otoscopic training experience and confidence, and provided diagnoses for 72 digital images taken with a high-definition video Otoscope that showed common otologic pathologies of the tympanic membrane and middle ear space. RESULTS Most participants (65%) had received less exposure to otoscopic training in medical school than they expected. Confidence in diagnostic ability was low. For diagnostic ability, the mean percent correct across pathologies was 54% ± 7.7%. Medical school year (P = .006), intended specialty (P = .022), and total number of otolaryngology rotations (P = .048) were predictive of diagnostic accuracy on univariable logistic regression analyses, but medical school year (P = .039) was the only significant independent predictor in multivariable analysis. Intended specialty (P = .047) and total number of otolaryngology rotations (P = .035) were predictive of prequiz diagnostic confidence on univariable logistic regression analyses. CONCLUSIONS Medical students were not satisfied with their exposure to otoscopic training. Intended specialty, total number of otolaryngology rotations, and year in medical school predicted diagnostic accuracy. Intended specialty and total number of otolaryngology rotations predicted diagnostic confidence. Additional studies are needed to investigate how training experiences can be improved to optimize otoscopy training during medical school. LEVEL OF EVIDENCE NA Laryngoscope, 129:1891-1897, 2019.
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autoscope automated otoscopy image analysis to diagnose ear pathology and use of clinically motivated eardrum features
Proceedings of SPIE, 2017Co-Authors: Caglar Senaras, Aaron C Moberly, Theodoros N Teknos, Garth F Essig, Charles A Elmaraghy, Nazhat Tajschaal, Metin N GurcanAbstract:In this study, we propose an automated otoscopy image analysis system called Autoscope. To the best of our knowledge, Autoscope is the first system designed to detect a wide range of eardrum abnormalities by using high-resolution Otoscope images and report the condition of the eardrum as “normal” or “abnormal.” In order to achieve this goal, first, we developed a preprocessing step to reduce camera-specific problems, detect the region of interest in the image, and prepare the image for further analysis. Subsequently, we designed a new set of clinically motivated eardrum features (CMEF). Furthermore, we evaluated the potential of the visual MPEG-7 descriptors for the task of tympanic membrane image classification. Then, we fused the information extracted from the CMEF and state-of-the-art computer vision features (CVF), which included MPEG-7 descriptors and two additional features together, using a state of the art classifier. In our experiments, 247 tympanic membrane images with 14 different types of abnormality were used, and Autoscope was able to classify the given tympanic membrane images as normal or abnormal with 84.6% accuracy.
Claude Laurent - One of the best experts on this subject based on the ideXlab platform.
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diagnostic accuracy of a general practitioner with video otoscopy collected by a health care facilitator compared to traditional otoscopy
International Journal of Pediatric Otorhinolaryngology, 2017Co-Authors: Thorbjorn Lundberg, De Wet Swanepoel, Claude Laurent, Leigh Biagio De JagerAbstract:Objective: Video-otoscopy is rapidly developing as a new method to diagnose common ear disease and can be performed by trained health care facilitators as well as by clinicians. This study aimed to ...
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otitis media diagnosis for developing countries using tympanic membrane image analysis
EBioMedicine, 2016Co-Authors: Hermanus Carel Myburgh, De Wet Swanepoel, Claude Laurent, Willemien H Van Zijl, Sten HellstromAbstract:Abstract Background Otitis media is one of the most common childhood diseases worldwide, but because of lack of doctors and health personnel in developing countries it is often misdiagnosed or not diagnosed at all. This may lead to serious, and life-threatening complications. There is, thus a need for an automated computer based image-analyzing system that could assist in making accurate otitis media diagnoses anywhere. Methods A method for automated diagnosis of otitis media is proposed. The method uses image-processing techniques to classify otitis media. The system is trained using high quality pre-assessed images of tympanic membranes, captured by digital video-Otoscopes, and classifies undiagnosed images into five otitis media categories based on predefined signs. Several verification tests analyzed the classification capability of the method. Findings An accuracy of 80.6% was achieved for images taken with commercial video-Otoscopes, while an accuracy of 78.7% was achieved for images captured on-site with a low cost custom-made video-Otoscope. Interpretation The high accuracy of the proposed otitis media classification system compares well with the classification accuracy of general practitioners and pediatricians (~64% to 80%) using traditional Otoscopes, and therefore holds promise for the future in making automated diagnosis of otitis media in medically underserved populations.
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video otoscopy recordings for diagnosis of childhood ear disease using telehealth at primary health care level
Journal of Telemedicine and Telecare, 2014Co-Authors: Leigh Biagio, De Wet Swanepoel, Claude Laurent, Thorbjorn LundbergAbstract:We studied the diagnoses made by an otologist and general practitioner (GP) from video-otoscopy recordings on children made by a telehealth facilitator. The gold standard was otomicroscopy by an ex ...
Deborah Senzer - One of the best experts on this subject based on the ideXlab platform.
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survey of speech language pathology graduate program training in outer and middle ear screening
American Journal of Speech-language Pathology, 2015Co-Authors: Yula C Serpanos, Deborah SenzerAbstract:Purpose The purpose of this study was to determine the national training practices of speech-language pathology graduate programs in outer and middle ear screening. Method Directors of all American Speech-Language-Hearing Association–accredited speech-language pathology graduate programs (N = 254; Council on Academic Accreditation in Audiology and Speech-Language Pathology, 2013) were surveyed on instructional formats in outer and middle ear screening. Results The graduate speech-language pathology program survey yielded 84 (33.1%) responses. Results indicated that some programs do not provide any training in the areas of conventional screening otoscopy using a handheld Otoscope (15.5%; n = 13) or screening tympanometry (11.9%; n = 10), whereas close to one half (46.4%; n = 39) reported no training in screening video otoscopy. Outcomes revealed that approximately one third or more of speech-language pathology graduate programs do not provide experiential opportunities in screening handheld otoscopy (36.9%...
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experiential instruction in graduate level preparation of speech language pathology students in outer and middle ear screening
American Journal of Speech-language Pathology, 2015Co-Authors: Yula C Serpanos, Deborah SenzerAbstract:Purpose This study presents a piloted training model of experiential instruction in outer and middle ear (OE-ME) screening for graduate speech-language pathology students with peer teaching by doctor of audiology (AuD) students. Method Six individual experiential training sessions in screening otoscopy and tympanometry were conducted for 36 graduate-level speech-language pathology students led by a supervised AuD student. Results Postexperiential training, survey outcomes from 24 speech-language pathology students revealed a significant improvement (p = .01) in perceptions of attaining adequate knowledge and comfort in performing screening otoscopy (handheld and video otoscopy) and tympanometry. In a group of matched controls who did not receive experiential training in OE-ME screening (n = 24), ratings on the same learning outcomes survey in otoscopy and tympanometry were significantly poorer (p = .01) compared with students who did receive experiential training. Conclusion A training model of experienti...
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experiential instruction in graduate level preparation of speech language pathology students in outer and middle ear screening
American Journal of Speech-language Pathology, 2015Co-Authors: Yula C Serpanos, Deborah SenzerAbstract:Purpose This study presents a piloted training model of experiential instruction in outer and middle ear (OE-ME) screening for graduate speech-language pathology students with peer teaching by doctor of audiology (AuD) students. Method Six individual experiential training sessions in screening otoscopy and tympanometry were conducted for 36 graduate-level speech-language pathology students led by a supervised AuD student. Results Postexperiential training, survey outcomes from 24 speech-language pathology students revealed a significant improvement (p = .01) in perceptions of attaining adequate knowledge and comfort in performing screening otoscopy (handheld and video otoscopy) and tympanometry. In a group of matched controls who did not receive experiential training in OE-ME screening (n = 24), ratings on the same learning outcomes survey in otoscopy and tympanometry were significantly poorer (p = .01) compared with students who did receive experiential training. Conclusion A training model of experienti...
