Diagnosis Of Middle Ear Disease Research Articles

Contemporary mechanics of conductive hearing loss

The middle ear plays a critical role for the conversion of acoustic energy to mechanical vibrations that subsequently enter the cochlea. It is middle ear impedance matching through ossicular coupling that has enabled land-dwelling vertebrates to hear soft airborne sounds. Conductive hearing loss may result from damage to the delicate middle ear structures following infection, trauma or rapid pressure changes. An understanding of the mechanics of the middle ear significantly improves the oto-surgeon's ability to effectively diagnose conductive hearing loss, localize the responsible lesion and then effectively correct the conduction abnormality. This article reviews some of the basic knowledge of middle ear mechanics for sound transmission, highlights recent advances in developing new techniques to assist in diagnosis of middle ear disease, and finally sheds light on future research aimed at improving the diagnosis and management of middle ear pathology.

Artificial Intelligence and Telehealth as Diagnostic Approach to Middle Ear Disease- Advances in Otology

As with other subspecialities, remote otoscopy commonly regarded as telehealth is being studied as a new approach for diagnosis of middle ear diseases. As in covid-19 pandemic, otolaryngologists were the most affected health care professionals as aerolized particles were a hurdle to conduct routine upper airway and ear canal examination, telehealth gain popularity, also to justify shortage of PPE. Beside this, reduction in post operative follow up visits, and cost savings in terms of greater access for rural populations were the major attractions to adopt remote otoscopy in ENT.

Diagnosis of Middle Ear Diseases Based on Convolutional Neural Network

An otoscope is traditionally used to examine the eardrum and ear canal. A diagnosis of otitis media (OM) relies on the experience of clinicians. If an examiner lacks experience, the examination may be difficult and time-consuming. This paper presents an ear disease classification method using middle ear images based on a convolutional neural network (CNN). Especially the segmentation and classification networks are used to classify an otoscopic image into six classes: normal, acute otitis media (AOM), otitis media with effusion (OME), chronic otitis media (COM), congenital cholesteatoma (CC) and traumatic perforations (TMPs). The Mask R-CNN is utilized for the segmentation network to extract the region of interest (ROI) from otoscopic images. The extracted ROIs are used as guiding features for the classification. The classification is based on transfer learning with an ensemble of two CNN classifiers: EfficientNetB0 and Inception-V3. The proposed model was trained with a 5-fold cross-validation technique. The proposed method was evaluated and achieved a classification accuracy of 97.29%.

Smartphone-based artificial intelligence using a transfer learning algorithm for the detection and diagnosis of middle ear diseases: A retrospective deep learning study.

SummaryBackgroundMiddle ear diseases such as otitis media and middle ear effusion, for which diagnoses are often delayed or misdiagnosed, are among the most common issues faced by clinicians providing primary care for children and adolescents. Artificial intelligence (AI) has the potential to assist clinicians in the detection and diagnosis of middle ear diseases through imaging.MethodsOtoendoscopic images obtained by otolaryngologists from Taipei Veterans General Hospital in Taiwan between Jany 1, 2011 to Dec 31, 2019 were collected retrospectively and de-identified. The images were entered into convolutional neural network (CNN) training models after data pre-processing, augmentation and splitting. To differentiate sophisticated middle ear diseases, nine CNN-based models were constructed to recognize middle ear diseases. The best-performing models were chosen and ensembled in a small CNN for mobile device use. The pretrained model was converted into the smartphone-based program, and the utility was evaluated in terms of detecting and classifying ten middle ear diseases based on otoendoscopic images. A class activation map (CAM) was also used to identify key features for CNN classification. The performance of each classifier was determined by its accuracy, precision, recall, and F1-score.FindingsA total of 2820 clinical eardrum images were collected for model training. The programme achieved a high detection accuracy for binary outcomes (pass/refer) of otoendoscopic images and ten different disease categories, with an accuracy reaching 98.0% after model optimisation. Furthermore, the application presented a smooth recognition process and a user-friendly interface and demonstrated excellent performance, with an accuracy of 97.6%. A fifty-question questionnaire related to middle ear diseases was designed for practitioners with different levels of clinical experience. The AI-empowered mobile algorithm's detection accuracy was generally superior to that of general physicians, resident doctors, and otolaryngology specialists (36.0%, 80.0% and 90.0%, respectively). Our results show that the proposed method provides sufficient treatment recommendations that are comparable to those of specialists.InterpretationWe developed a deep learning model that can detect and classify middle ear diseases. The use of smartphone-based point-of-care diagnostic devices with AI-empowered automated classification can provide real-world smart medical solutions for the diagnosis of middle ear diseases and telemedicine.FundingThis study was supported by grants from the Ministry of Science and Technology (MOST110-2622-8-075-001, MOST110-2320-B-075-004-MY3, MOST-110-2634-F-A49 -005, MOST110-2745-B-075A-001A and MOST110-2221-E-075-005), Veterans General Hospitals and University System of Taiwan Joint Research Program (VGHUST111-G6-11-2, VGHUST111c-140), and Taipei Veterans General Hospital (V111E-002-3).

Transoral ventral tympanic bulla osteotomy in cats: 13 cases (2016–2019)

To analyze the results of transoral ventral bulla osteotomy (TOVBO) in cats. 13 client-owned cats treated by TOVBO between February 2016 and February 2019. Medical records of cats with a diagnosis of middle ear disease (MED) that underwent TOVBO were reviewed. The procedure was similar to the one described for dogs. Short-term follow-up was obtained via clinical examination before discharge and at day 15 postoperatively. Long-term follow-up was performed via telephone interview. 13 cats (age range, 8 months to 12 years) underwent unilateral (n = 10) or bilateral (3) TOVBO (16) for the treatment of tympanic bulla (TB) infection (10), nasopharyngeal inflammatory polyps (5), or bullet retrieval from the TB (1). There were no intraoperative complications. One cat with a poor preoperative status died at postoperative day 3 from pneumonia. Eight cats experienced postoperative complications including head tilt (n = 2), Horner syndrome (3), loss of appetite (2), and temporary blindness (1). Collected samples confirmed the presence of nasopharyngeal inflammatory polyps (5), or otitis media (8). Six months after surgical intervention, 9 cats were free of MED signs. This oral approach provided a good access to the TB in all cases. The complications observed after TOVBO were similar to those for VBO. In cats, TOVBO seems to be an acceptable and safe minimally invasive alternative to the other approaches of the TB to address MED.

English

BACKGROUND Tympanometry as a definitive test protocol in the diagnosis of middle-ear disease and function remains still a tentative one. Primarily this is the result of cost effectiveness and concerns over referral to an audiology unit. It was also found to be due to the concerns over variability in test protocols and its influence on demographic and environmental factors. The present study was done to simplify the understanding of the different test variables of tympanometry. METHODS The clinical study was conducted in the Department of ENT, Deccan College of Medical Sciences and Princess Esra Hospital, Hyderabad. Out of 134 patients registered with loss of hearing, 68 patients were grouped as preoperative group (136 ears) and 66 patients as postoperative group (132 ears). The patients in both groups were registered at different times and they were not the same. Audiology equipment used was impedance audiometers (Siemens SD 30, interacoustics AT 235 H); portable tympanometer (Welch-Allyn). Preliminary pure tone audiometry was also done to supplement the diagnosis. The test results were reported as: conductive, sensorineural, and mixed hearing loss. The results were reported based on the variables/criteria: admittance, tympanometric peak pressure (TPP), peak amplitude (Ya peak) and the volume of the ear canal. RESULTS There were 39 males (57.35 %) and 29 females (42.64 %) in the preoperative group. There were 41 males (62.12 %) and 25 females (37.87 %) in the postoperative group. Middle ear diseases showing conductive deafness were grouped as four types: a) middle ear effusion (35 patients); b) Eustachian tube dysfunction (31 patients); c) Ossicular chain adhesions (38 patients) d) tympanic membrane perforations and tympanosclerosis (28 patients) and e) otosclerosis (02 patients). CONCLUSIONS Using tympanometric attributes such as admittance, tympanometric peak pressure, peak amplitude (Ya peak) and the volume of the ear canal, it was possible to make accurate diagnosis of middle ear diseases and their exact pathology or make a differential diagnosis. The attributes used gave much accurate measure of the mechano-acoustics than the preset values used regularly by many centres. KEY WORDS Impedance Audiometry (Tympanometry), Middle Ear Diseases, Compliance, Peak Pressure and Tympanometric Peak Pressure (TPP)

An Assistive Role of a Machine Learning Network in Diagnosis of Middle Ear Diseases.

The present study aimed to develop a machine learning network to diagnose middle ear diseases with tympanic membrane images and to identify its assistive role in the diagnostic process. The medical records of subjects who underwent ear endoscopy tests were reviewed. From these records, 2272 diagnostic tympanic membranes images were appropriately labeled as normal, otitis media with effusion (OME), chronic otitis media (COM), or cholesteatoma and were used for training. We developed the “ResNet18 + Shuffle” network and validated the model performance. Seventy-one representative cases were selected to test the final accuracy of the network and resident physicians. We asked 10 resident physicians to make diagnoses from tympanic membrane images with and without the help of the machine learning network, and the change of the diagnostic performance of resident physicians with the aid of the answers from the machine learning network was assessed. The devised network showed a highest accuracy of 97.18%. A five-fold validation showed that the network successfully diagnosed ear diseases with an accuracy greater than 93%. All resident physicians were able to diagnose middle ear diseases more accurately with the help of the machine learning network. The increase in diagnostic accuracy was up to 18% (1.4% to 18.4%). The machine learning network successfully classified middle ear diseases and was assistive to clinicians in the interpretation of tympanic membrane images.

Analysing wideband absorbance immittance in normal and ears with otitis media with effusion using machine learning

Wideband Absorbance Immittance (WAI) has been available for more than a decade, however its clinical use still faces the challenges of limited understanding and poor interpretation of WAI results. This study aimed to develop Machine Learning (ML) tools to identify the WAI absorbance characteristics across different frequency-pressure regions in the normal middle ear and ears with otitis media with effusion (OME) to enable diagnosis of middle ear conditions automatically. Data analysis included pre-processing of the WAI data, statistical analysis and classification model development, and key regions extraction from the 2D frequency-pressure WAI images. The experimental results show that ML tools appear to hold great potential for the automated diagnosis of middle ear diseases from WAI data. The identified key regions in the WAI provide guidance to practitioners to better understand and interpret WAI data and offer the prospect of quick and accurate diagnostic decisions.

Exploring the Influence of Age on 3D Wideband Absorbance Immittance and the Automated Diagnosis of Otitis Media With Effusion Using Machine Learning

Background: Wideband Absorbance Immittance (WAI) has great potential as a diagnostic tool for identifying middle ear dysfunction in the ENT/Audiology clinic. The challenge to its widespread use is the limited understanding and interpretation of WAI results by practitioners because the WAI dataset contains thousands of values. This study investigated energy absorbance at varying frequency-pressure domains in normal and ears with otitis media with effusion (OME) in different age groups using machine learning (ML) tools to determine the accuracy of automated diagnosis of OME. Methods: A total of 1178 sets of WAI data (551 normal middle ears and 627 ears with OME) were divided into three age groups for statistical analysis. ML approaches included classification model development and significant region extraction from the frequency-pressure WAI plots. Findings: Significant differences were found across various frequency-pressure regions between normal and OME ears in the three different age groups. Feature selection using ML classifiers identified areas of importance at mid frequencies and pressures between -50 to +150 daPa in age groups over 3 years. More accurate OME classification was seen with the ML models in the groups over the age of 3. Interpretation: The ML approach provides great potential for the automated diagnosis of middle ear diseases using WAI data. The important discriminative regions extracted using the ML tools provide practical guidance to clinicians to decide whether an ear is normal or OME. There were however significant age influences on ML accuracies, particularly in the age group under 3 years. Funding Information: This work is supported by NIHR (AI Award, 02305), Ser Cymru III Enhancing Competitiveness Infrastructure Award (MA/KW/5554/19), Great Britain Sasakawa Foundation (5826), Cardiff Metropolitan University Research Innovation Award and The Global Academies Research and Innovation Development Fund. Declaration of Interests: None declared. Ethics Approval Statement: This study received ethical approval for data collection from Cardiff School of Sport and Health Sciences Ethical Committee under the Cardiff Metropolitan University ethical guidelines and regulations (Ethical reference number: Sta-3013).

The Use of Machine Learning Tools to Analyse Wideband Absorbance Immittance in Normal and Ears With Otitis Media With Effusion

Background: Wideband Absorbance Immittance (WAI) has been available for more than a decade, however its clinical use still faces the challenges of limited understanding and poor interpretation of WAI results. This study aimed to develop Machine Learning (ML) tools to identify the WAI absorbance characteristics across different frequency-pressure regions in the normal middle ear and ears with otitis media with effusion (OME) to enable diagnosis of middle ear conditions automatically. Methods: A total of 672 sets of WAI sample data (423 normal middle ears and 249 ears with OME) were collected using the Titan IMP440. Data analysis included pre-processing of the WAI data, statistical analysis and classification model development, together with significant region extraction from the 2D frequency-pressure WAI images. Findings: Statistical analysis at each frequency-pressure point showed 88% of the data points to have significant differences in absorbance values between normal ears and ears with OME. The performance of the examined classifiers to automatically diagnose the OME and normal cases showed the accuracy at around 80%. Feature selection using RF classifiers and tests of statistical significance identified an area of importance at frequencies between 1090Hz to 2310 Hz and pressures from -40 to +90 daPa from the 2D frequency-pressure WAI images. Interpretation: ML tools appear to hold great potential for the automated diagnosis of middle ear diseases from WAI data. The identified key regions in the WAI provide guidance to practitioners to better understand and interpret WAI data and offer the prospect of quick and accurate diagnostic decisions. Funding Statement: This work is supported by Ser Cymru III Enhancing Competitiveness Infrastructure Award (MA/KW/5554/19), Great Britain Sasakawa Foundation (5826), Cardiff Metropolitan University Research Innovation Award and The Global Academies Research and Innovation Development Fund. Declaration of Interests: None declared. Ethics Approval Statement: The study was approved by Cardiff School of Sport and Health Sciences under the Cardiff Metropolitan University Ethics Framework (Ethical reference number: Sta-3013).

Optical coherence tomography: current and future clinical applications in otology.

This article reviews literature on the use of optical coherence tomography (OCT) in otology and provides the reader with a timely update on its current clinical and research applications. The discussion focuses on the principles of OCT, the use of the technology for the diagnosis of middle ear disease and for the delineation of in-vivo cochlear microarchitecture and function. Recent advances in OCT include the measurement of structural and vibratory properties of the tympanic membrane, ossicles and inner ear in healthy and diseased states. Accurate, noninvasive diagnosis of middle ear disease, such as otosclerosis and acute otitis media using OCT, has been validated in clinical studies, whereas inner ear OCT imaging remains at the preclinical stage. The development of recent microscopic, otoscopic and endoscopic systems to address clinical and research problems is reviewed. OCT is a real-time, noninvasive, nonionizing, point-of-care imaging modality capable of imaging ear structures in vivo. Although current clinical systems are mainly focused on middle ear imaging, OCT has also been shown to have the ability to identify inner ear disease, an exciting possibility that will become increasingly relevant with the advent of targeted inner ear therapies.

Eyes in Ears: A Miniature Steerable Digital Endoscope for Trans-Nasal Diagnosis of Middle Ear Disease.

The aim of this work is to design, fabricate and experimentally validate a miniature steerable digital endoscope that can provide comprehensive, high-resolution imaging of the middle ear using a trans-nasal approach. The motivation for this work comes from the high incidence of middle ear diseases, and the current reliance on invasive surgery to diagnose and survey these diseases which typically consists of the eardrum being lifted surgically to directly visualize the middle ear using a trans-canal approach. To enable less-invasive diagnosis and surveillance of middle ear disease, we propose an endoscope that is small enough to pass into the middle ear through the Eustachian tube, with a steerable tip that carries a 1 Megapixel image sensor and fiber-optic illumination to provide high-resolution visualization of critical middle ear structures. The proposed endoscope would enable physicians to diagnose middle ear disease using a non-surgical trans-nasal approach instead, enabling such procedures to be performed in an office setting and greatly reducing invasiveness for the patient. In this work, the computational design of the steerable tip based on computed tomography models of real human middle ear anatomy is presented, and these results informed the fabrication of a clinical-scale steerable endoscope prototype. The prototype was used in a pilot study in three cadaveric temporal bone specimens, where high-quality middle ear visualization was achieved as determined by an unbiased cohort of otolaryngologists. This is the first paper to demonstrate cadaveric validation of a digital, steerable, clinical-scale endoscope for middle ear disease diagnosis, and the experimental results illustrate that the endoscope enables the visualization of critical middle ear structures (such as the epitympanum or sinus tympani) that were seldom or never visualized in prior published trans-Eustachian tube endoscopy feasibility studies.

Smartphone-based spectral imaging otoscope: System development and preliminary study for evaluation of its potential as a mobile diagnostic tool.

We develop a novel smartphone-based spectral imaging otoscope for telemedicine and examine its capability for the mobile diagnosis of middle ear diseases. The device was applied to perform spectral imaging and analysis of an ear-mimicking phantom and a normal and abnormal tympanic membrane for evaluation of its potential for the mobile diagnosis. Spectral classified images were obtained via online spectral analysis in a remote server. The phantom experimental results showed that it allowed us to distinguish four different fluids located behind a semitransparent membrane. Also, in the spectral classified images of normal ears (n = 3) and an ear with chronic otitis media (n = 1), the normal and abnormal regions in each ear could be quantitatively distinguished with high contrast. These preliminary results thus suggested that it might have the potentials for providing quantitative information for the mobile diagnosis of various middle ear diseases.

High-Speed Holographic Shape and Full-Field Displacement Measurements of the Tympanic Membrane in Normal and Experimentally Simulated Pathological Ears.

To improve the understanding of the middle-ear hearing mechanism and assist in the diagnosis of middle-ear diseases, we are developing a high-speed digital holographic (HDH) system to measure the shape and acoustically-induced transient displacements of the tympanic membrane (TM). In this paper, we performed measurements on cadaveric human ears with simulated common middle-ear pathologies. The frequency response function (FRF) of the normalized displacement by the stimulus (sound pressure) at each measured pixel point of the entire TM surface was calculated and the complex modal indicator function (CMIF) of the middle-ear system based on FRFs of the entire TM surface motions was used to differentiate different middle-ear pathologies. We also observed changes in the TM shape and the surface motion pattern before and after various middle-ear manipulations. The observations of distinguishable TM shapes and motion patterns in both time and frequency domains between normal and experimentally simulated pathological ears support the development of a quantitative clinical holography-based apparatus for diagnosing middle-ear pathologies.

Automated Classification of the Tympanic Membrane Using a Convolutional Neural Network

Precise evaluation of the tympanic membrane (TM) is required for accurate diagnosis of middle ear diseases. However, making an accurate assessment is sometimes difficult. Artificial intelligence is often employed for image processing, especially for performing high level analysis such as image classification, segmentation and matching. In particular, convolutional neural networks (CNNs) are increasingly used in medical image recognition. This study demonstrates the usefulness and reliability of CNNs in recognizing the side and perforation of TMs in medical images. CNN was constructed with typically six layers. After random assignment of the available images to the training, validation and test sets, training was performed. The accuracy of the CNN model was consequently evaluated using a new dataset. A class activation map (CAM) was used to evaluate feature extraction. The CNN model accuracy of detecting the TM side in the test dataset was 97.9%, whereas that of detecting the presence of perforation was 91.0%. The side of the TM and the presence of a perforation affect the activation sites. The results show that CNNs can be a useful tool for classifying TM lesions and identifying TM sides. Further research is required to consider real-time analysis and to improve classification accuracy.

Model-based hearing diagnostics based on wideband tympanometry measurements utilizing fuzzy arithmetic

Today's audiometric methods for the diagnosis of middle ear disease are often based on a comparison of measurements with standard curves, that represent the statistical range of normal hearing responses. Because of large inter-individual variances in the middle ear, especially in wideband tympanometry (WBT), specificity and quantitative evaluation are greatly restricted. A new model-based approach could transform today's predominantly qualitative hearing diagnostics into a quantitative and tailored, patient-specific diagnosis, by evaluating WBT measurements with the aid of a middle-ear model. For this particular investigation, a finite element model of a human ear was used. It consisted of an acoustic ear canal and a tympanic cavity model, a middle-ear with detailed nonlinear models of the tympanic membrane and annular ligament, and a simplified inner-ear model. This model has made it possible for us to simulate pathologies like the stiffening of ligaments or joints, because we can simply change the corresponding mechanical parameters of the model. On the other hand, it is also possible to identify pathologies from measurements, by analyzing the parameters obtained by a system identification procedure. This reduces the number of required model parameters through sensitivity studies and parameter clustering. Uncertainties due to the lack of knowledge, subjectivity in numerical implementation and model simplification are taken into account by the application of fuzzy arithmetic. The most confident parameter set can be determined by applying an inverse fuzzy method on the measurement data. The principle and the benefits of this model-based approach are illustrated by the example of a two-mass oscillator, and also by the simulation of the energy absorbance of an ear with malleus fixation, where the parameter changes that are introduced can be determined quantitatively through the system identification.

Extratympanic observation of middle ear structure using a refractive index matching material (glycerol) and an infrared camera

High-resolution computed tomography has been used mainly in the diagnosis of middle ear disease, such as high-jugular bulb, congenital cholesteatoma, and ossicular disruption. However, certain diagnoses are confirmed through exploratory tympanotomy. There are few noninvasive methods available to observe the middle ear. The purpose of this study was to investigate the effect of glycerol as a refractive index matching material and an infrared (IR) camera system for extratympanic observation. 30% glycerol was used as a refractive index matching material in five fresh cadavers. Each material was divided into four subgroups; GN (glycerol no) group, GO (glycerol out) group, GI (glycerol in) group, and GB (glycerol both) group. A printed letter and middle ear structures on the inside tympanic membrane were observed using a visible and IR ray camera system. In the GB group, there were marked a transilluminated letter or an ossicle on the inside tympanic membrane. In particular, a footplate of stapes was even transilluminated using the IR camera system in the GB group. This method can be useful in the diagnosis of diseases of the middle ear if it is clinically applied through further studies.

Optical coherence tomography in otolaryngology

Optical coherence tomography is an imaging technique based on coherence interferometry. It is used in many medical fields due to its non-invasive imaging capabilities with micrometer resolution. The aim of the authors was to review the applicability of the optical coherence tomography in otolaryngology. Literature data and their own studies show that optical coherence tomography is a reliable method for identifying and targeted biopsy of inflammated, pre-malignant or cancer tissue in human laryngeal and pharyngeal mucosa. Another emerging field for optical coherence tomography is diagnosis of middle ear diseases, especially stapes ankylosis and cholesteatoma.

Diagnosis of middle ear disease using tympanograms and digital imaging

The study investigates the ability of physicians with different levels of training to accurately identify middle ear (ME) pathology using tympanograms and tympanic membrane (TM) digital photographs. Groups of medical students, internal medicine residents, and attending and resident otolaryngologists were presented with a set of 50 matched tympanograms and photographs of TMs. The physicians were asked to differentiate between a normal and an abnormal-appearing TM. The sensitivity and specificity of diagnosis of ME pathology by physicians were compared with the gold standard, examining the ME cavity for pathology during myringotomy. The sensitivity did not vary among physicians with different levels of training; however, specificity improved with an increase in physician experience. This study establishes a baseline for accuracy of diagnosis of ME pathology using tympanograms and photographs of TMs as compared with binocular otomicroscopy and the gold standard, myringotomy. B-2.

Diagnosis of middle ear disease using tympanograms and digital imaging

Otolaryngology–Head and Neck SurgeryVolume 129, Issue 2 p. P132-P132 Tuesday, September 23, 2003, 9:30 am to 10:30 am Room OCCC 202 Scientific Sessions: General 9:30 am Diagnosis of Middle Ear Disease Using Tympanograms and Digital Imaging Stanley E Thawley MD, Stanley E Thawley MDSearch for more papers by this authorMichael D Poole moderators MD PhD, Michael D Poole moderators MD PhDSearch for more papers by this authorAnna Aronzon presenter MD, Anna Aronzon presenter MD Philadelphia, PASearch for more papers by this authorAdam T Ross MD, Adam T Ross MD Philadelphia, PASearch for more papers by this authorKen Kazahaya MD, Ken Kazahaya MD Philadelphia, PASearch for more papers by this authorMasaru Ishii MD PhD, Masaru Ishii MD PhD Baltimore, MDSearch for more papers by this author Stanley E Thawley MD, Stanley E Thawley MDSearch for more papers by this authorMichael D Poole moderators MD PhD, Michael D Poole moderators MD PhDSearch for more papers by this authorAnna Aronzon presenter MD, Anna Aronzon presenter MD Philadelphia, PASearch for more papers by this authorAdam T Ross MD, Adam T Ross MD Philadelphia, PASearch for more papers by this authorKen Kazahaya MD, Ken Kazahaya MD Philadelphia, PASearch for more papers by this authorMasaru Ishii MD PhD, Masaru Ishii MD PhD Baltimore, MDSearch for more papers by this author First published: 17 May 2016 https://doi.org/10.1016/S0194-59980301108-2Read the full textAboutPDF ToolsExport citationAdd to favoritesTrack citation ShareShare Give accessShare full text accessShare full-text accessPlease review our Terms and Conditions of Use and check box below to share full-text version of article.I have read and accept the Wiley Online Library Terms and Conditions of UseShareable LinkUse the link below to share a full-text version of this article with your friends and colleagues. Learn more.Copy URL Share a linkShare onFacebookTwitterLinkedInRedditWechat No abstract is available for this article. Volume129, Issue2August 2003Pages P132-P132 RelatedInformation