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Purpose: To evaluate the demographic composition of academic glaucoma specialists currently practicing in the United States. Design: Retrospective and observational study. Subjects: Academic glaucoma specialists identified from ophthalmology residency programs listed on the Doximity database. Methods: The American Board of Ophthalmology (ABO) membership directory, Doximity database, publicly available data, and direct communications were used to identify academic glaucoma specialists and their demographics. Information collected included-name, gender, race/ethnicity, geographic location, board certification date, academic affiliation, and academic rank. Ophthalmic age was defined as the number of years since ophthalmology board certification. Underrepresented minority (URM) groups were defined as Hispanics, Black or African Americans, Latinos, American Indians, or Alaskan Natives as defined by San Francisco match. In addition, the temporal, geographic, and academic rank distributions among females and URMs were explored. Main outcome measures: Women and URMs representations among academic glaucoma specialists across academic ranks, geographic regions, as well as ophthalmic age. Results: There were 457 active academic glaucoma specialists identified from 110 institutions in 38 states. Among them, 185 (40.5%) were women and 42 (9.2%) were URM. The proportion of women glaucoma specialists in academia had increased significantly with a rate of 1.049 in odds ratio (OR) per year (p < 0.001). However, there were no significant changes in the proportion of URMs over time. The earliest year of certification was 1,964 for males and 1,974 for females. When controlled for ophthalmic age, there were no significant differences in the distribution of women or URMs between the different academic ranks (p = 0.572 and p = 0.762, respectively). Among assistant professors, women had a significantly higher ophthalmic age compared to men (p < 0.001), but there was no significant difference in ophthalmic age in both the associate and full professor groups. There were no significant differences in the geographic distribution of gender (p = 0.516) and URM across United States regions (p = 0.238). Conclusion: The proportion of women among academic glaucoma specialists has significantly increased over the past 5 decades; however, the proportion of URMs has been stagnant in the same period. Enhancing URM representation among academic glaucoma specialists deserves to be a future priority. How to cite this article: Afzali K, Fujimoto DK, Mohammadi SO, et al. Race and Gender Shift among Academic Glaucoma Specialists in the Last 5 Decades. J Curr Glaucoma Pract 2023;17(2):98-103.
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Hepatosplenomegaly is commonly diagnosed by radiologists based on single dimension measurements and heuristic cut-offs. Volumetric measurements may be more accurate for diagnosing organ enlargement. Artificial intelligence techniques may be able to automatically calculate liver and spleen volume and facilitate more accurate diagnosis. After IRB approval, 2 convolutional neural networks (CNN) were developed to automatically segment the liver and spleen on a training dataset comprised of 500 single-phase, contrast-enhanced CT abdomen and pelvis examinations. A separate dataset of ten thousand sequential examinations at a single institution was segmented with these CNNs. Performance was evaluated on a 1% subset and compared with manual segmentations using Sorensen-Dice coefficients and Pearson correlation coefficients. Radiologist reports were reviewed for diagnosis of hepatomegaly and splenomegaly and compared with calculated volumes. Abnormal enlargement was defined as greater than 2 standard deviations above the mean. Median Dice coefficients for liver and spleen segmentation were 0.988 and 0.981, respectively. Pearson correlation coefficients of CNN-derived estimates of organ volume against the gold-standard manual annotation were 0.999 for the liver and spleen (P < 0.001). Average liver volume was 1556.8 ± 498.7 cc and average spleen volume was 194.6 ± 123.0 cc. There were significant differences in average liver and spleen volumes between male and female patients. Thus, the volume thresholds for ground-truth determination of hepatomegaly and splenomegaly were determined separately for each sex. Radiologist classification of hepatomegaly was 65% sensitive, 91% specific, with a positive predictive value (PPV) of 23% and an negative predictive value (NPV) of 98%. Radiologist classification of splenomegaly was 68% sensitive, 97% specific, with a positive predictive value (PPV) of 50% and a negative predictive value (NPV) of 99%. Convolutional neural networks can accurately segment the liver and spleen and may be helpful to improve radiologist accuracy in the diagnosis of hepatomegaly and splenomegaly.