SkullGAN: Synthetic Skull CT Generation with Generative Adversarial Networks

Deep learning offers potential for various healthcare applications involving the human skull but requires extensive datasets of curated medical images. To overcome this challenge, we propose SkullGAN, a generative adversarial network (GAN), to create large datasets of synthetic skull CT slices, reducing reliance on real images and accelerating the integration of machine learning into healthcare. In our method, CT slices of 38 subjects were fed to SkullGAN, a neural network comprising over 200 million parameters. The synthetic skull images generated were evaluated based on three quantitative radiological features: skull density ratio (SDR), mean thickness, and mean intensity. They were further analyzed using t-distributed stochastic neighbor embedding (t-SNE) and by applying the SkullGAN discriminator as a classifier. The results showed that SkullGAN-generated images demonstrated similar key quantitative radiological features to real skulls. Further definitive analysis was undertaken by applying the discriminator of SkullGAN, where the SkullGAN discriminator classified 56.5% of a test set of real skull images and 55.9% of the SkullGAN-generated images as reals (the theoretical optimum being 50%), demonstrating that the SkullGAN-generated skull set is indistinguishable from the real skull set - within the limits of our nonlinear classifier. Therefore, SkullGAN makes it possible to generate large numbers of synthetic skull CT segments, necessary for training neural networks for medical applications involving the human skull. This mitigates challenges associated with preparing large, high-quality training datasets, such as access, capital, time, and the need for domain expertise.Comment: The first two authors contributed equall

Distribution characteristics of air-bone gaps : evidence of bias in manual audiometry

OBJECTIVES : Five databases were mined to examine distributions of airbone gaps obtained by automated and manual audiometry. Differences in distribution characteristics were examined for evidence of influences unrelated to the audibility of test signals. DESIGN : The databases provided air- and bone-conduction thresholds that permitted examination of air-bone gap distributions that were free of ceiling and floor effects. Cases with conductive hearing loss were eliminated based on air-bone gaps, tympanometry, and otoscopy, when available. The analysis is based on 2,378,921 threshold determinations from 721,831 subjects from five databases. RESULTS : Automated audiometry produced air-bone gaps that were normally distributed suggesting that air- and bone-conduction thresholds are normally distributed. Manual audiometry produced air-bone gaps that were not normally distributed and show evidence of biasing effects of assumptions of expected results. In one database, the form of the distributions showed evidence of inclusion of conductive hearing losses. CONCLUSIONS : Thresholds obtained by manual audiometry show tester bias effects from assumptions of the patient’s hearing loss characteristics. Tester bias artificially reduces the variance of bone-conduction thresholds and the resulting air-bone gaps. Because the automated method is free of bias from assumptions of expected results, these distributions are hypothesized to reflect the true variability of air- and boneconduction thresholds and the resulting air-bone gaps.Portions of this work were supported by Grant RC3DC010986 from the National Institute of Deafness and Other Communication Disorders and by contract No. VA118-12-C-0029 from the US Department of Veterans Affairs. The Rehabilitation Research and Development Service of the US Department of Veterans Affairs supported this work through the Auditory and Vestibular Dysfunction Research Enhancement Award Program (REAP) and a Senior Research Career Scientist award to the second author.http://journals.lww.com/ear-hearing2017-03-31hb2016Speech-Language Pathology and Audiolog

Distribution characteristics of normal pure-tone thresholds

OBJECTIVE : This study examined the statistical properties of normal air-conduction thresholds obtained with automated and manual audiometry to test the hypothesis that thresholds are normally distributed and to examine the distributions for evidence of bias in manual testing. DESIGN : Four databases were mined for normal thresholds. One contained audiograms obtained with an automated method. The other three were obtained with manual audiometry. Frequency distributions were examined for four test frequencies (250, 500, 1000, and 2000 Hz). STUDY SAMPLE : The analysis is based on 317 569 threshold determinations of 80 547 subjects from four clinical databases. RESULTS : Frequency distributions of thresholds obtained with automated audiometry are normal in form. Corrected for age, the mean thresholds are within 1.5 dB of reference equivalent threshold sound pressure levels. Frequency distributions of thresholds obtained by manual audiometry are shifted toward higher thresholds. Two of the three datasets obtained by manual audiometry are positively skewed. CONCLUSIONS : The positive shift and skew of the manual audiometry data may result from tester bias. The striking scarcity of thresholds below 0 dB HL suggests that audiologists place less importance on identifying low thresholds than they do for higher-level thresholds. We refer to this as the Good enough bias and suggest that it may be responsible for differences in distributions of thresholds obtained by automated and manual audiometry.By grant RC3DC010986 from the National Institutes of Deafness and Other Communication Disorders. The Rehabilitation Research and Development Service of the U.S. Department of Veterans Affairs supported this work through the Auditory and Vestibular Dysfunction Research Enhancement Award Program (REAP) and a Senior Research Career Scientist.http://www.tandfonline.com/loi/iija202016-05-31hb2016Speech-Language Pathology and Audiolog

Comprehensive measures of sound exposures in cinemas using smart phones

OBJECTIVES Sensorineural hearing loss from sound overexposure has a considerable prevalence. Identification of sound hazards is crucial, as prevention, due to a lack of definitive therapies, is the sole alternative to hearing aids. One subjectively loud, yet little studied, potential sound hazard is movie theaters. This study uses smart phones to evaluate their applicability as a widely available, validated sound pressure level (SPL) meter. Therefore, this study measures sound levels in movie theaters to determine whether sound levels exceed safe occupational noise exposure limits and whether sound levels in movie theaters differ as a function of movie, movie theater, presentation time, and seat location within the theater. DESIGN Six smart phones with an SPL meter software application were calibrated with a precision SPL meter and validated as an SPL meter. Additionally, three different smart phone generations were measured in comparison to an integrating SPL meter. Two different movies, an action movie and a children's movie, were measured six times each in 10 different venues (n = 117). To maximize representativeness, movies were selected focusing on large release productions with probable high attendance. Movie theaters were selected in the San Francisco, CA, area based on whether they screened both chosen movies and to represent the largest variety of theater proprietors. Measurements were analyzed in regard to differences between theaters, location within the theater, movie, as well as presentation time and day as indirect indicator of film attendance. RESULTS The smart phone measurements demonstrated high accuracy and reliability. Overall, sound levels in movie theaters do not exceed safe exposure limits by occupational standards. Sound levels vary significantly across theaters and demonstrated statistically significant higher sound levels and exposures in the action movie compared to the children's movie. Sound levels decrease with distance from the screen. However, no influence on time of day or day of the week as indirect indicator of film attendance could be found. CONCLUSIONS Calibrated smart phones with an appropriate software application as used in this study can be utilized as a validated SPL meter. Because of the wide availability, smart phones in combination with the software application can provide high quantity recreational sound exposure measurements, which can facilitate the identification of potential noise hazards. Sound levels in movie theaters decrease with distance to the screen, but do not exceed safe occupational noise exposure limits. Additionally, there are significant differences in sound levels across movie theaters and movies, but not in presentation time

Anatomic measures of upper airway structures in obstructive sleep apnea

Objective: Determine if anatomic dimensions of airway structures are associated with airway obstruction in obstructive sleep apnea (OSA) patients. Methods: Twenty-eight subjects with (n = 14) and without (n = 14) OSA as determined by clinical symptoms and sleep studies; volunteer sample. Skeletal and soft tissue dimensions were measured from radiocephalometry and magnetic resonance imaging. The soft palate thickness, mandibular plane-hyoid (MP-H) distance, posterior airway space (PAS) diameters and area, and tongue volume were calculated. Results: Compared to controls, the OSA group demonstrated a significantly longer MP-H distance (P = 0.009) and shorter nasal PAS diameter (P = 0.02). The PAS area was smaller (P = 0.002) and tongue volume larger in the OSA group (P = 0.004). The MP-H distance, PAS measurements, and tongue volume are of clinical relevance in OSA patients. Conclusions: A long MP-H distance, and small PAS diameters and area are significant anatomic measures in OSA; however the most substantial parameter found was a large tongue volume. Keywords: Obstructive sleep apnea, Anatomy, Anatomic measurement, Posterior airway space, Tongue volume, Hyoid positio

Distribution characteristics of normal pure-tone thresholds

OBJECTIVE : This study examined the statistical properties of normal air-conduction thresholds obtained with automated and manual audiometry to test the hypothesis that thresholds are normally distributed and to examine the distributions for evidence of bias in manual testing. DESIGN : Four databases were mined for normal thresholds. One contained audiograms obtained with an automated method. The other three were obtained with manual audiometry. Frequency distributions were examined for four test frequencies (250, 500, 1000, and 2000 Hz). STUDY SAMPLE : The analysis is based on 317 569 threshold determinations of 80 547 subjects from four clinical databases. RESULTS : Frequency distributions of thresholds obtained with automated audiometry are normal in form. Corrected for age, the mean thresholds are within 1.5 dB of reference equivalent threshold sound pressure levels. Frequency distributions of thresholds obtained by manual audiometry are shifted toward higher thresholds. Two of the three datasets obtained by manual audiometry are positively skewed. CONCLUSIONS : The positive shift and skew of the manual audiometry data may result from tester bias. The striking scarcity of thresholds below 0 dB HL suggests that audiologists place less importance on identifying low thresholds than they do for higher-level thresholds. We refer to this as the Good enough bias and suggest that it may be responsible for differences in distributions of thresholds obtained by automated and manual audiometry.By grant RC3DC010986 from the National Institutes of Deafness and Other Communication Disorders. The Rehabilitation Research and Development Service of the U.S. Department of Veterans Affairs supported this work through the Auditory and Vestibular Dysfunction Research Enhancement Award Program (REAP) and a Senior Research Career Scientist.http://www.tandfonline.com/loi/iija202016-05-31hb2016Speech-Language Pathology and Audiolog