Search

Your search keyword '"Coyner, Aaron S."' showing total 126 results
Start Over Author "Coyner, Aaron S."
126 results on '"Coyner, Aaron S."'

1. Genome-wide association identifies novel ROP risk loci in a multiethnic cohort

Author

Li, Xiaohui, Owen, Leah A, Taylor, Kent D, Ostmo, Susan, Chen, Yii-Der Ida, Coyner, Aaron S, Sonmez, Kemal, Hartnett, M Elizabeth, Guo, Xiuqing, Ipp, Eli, Roll, Kathryn, Genter, Pauline, Chan, RV Paul, DeAngelis, Margaret M, Chiang, Michael F, Campbell, J Peter, and Rotter, Jerome I

Subjects
Biological Sciences, Biomedical and Clinical Sciences, Genetics, Ophthalmology and Optometry, Human Genome, Biotechnology, Eye Disease and Disorders of Vision, Perinatal Period - Conditions Originating in Perinatal Period, Prevention, Pediatric, Rare Diseases, Clinical Research, Aetiology, 2.1 Biological and endogenous factors, Eye, Infant, Newborn, Humans, Genome-Wide Association Study, Retinopathy of Prematurity, Ethnicity, Genetic Predisposition to Disease, Polymorphism, Single Nucleotide, i-ROP Consortium, Biological sciences, Biomedical and clinical sciences
Abstract

We conducted a genome-wide association study (GWAS) in a multiethnic cohort of 920 at-risk infants for retinopathy of prematurity (ROP), a major cause of childhood blindness, identifying 1 locus at genome-wide significance level (p

Published
2024

2. A generalized framework to predict continuous scores from medical ordinal labels

Author

Hoebel, Katharina V., Lemay, Andreanne, Campbell, John Peter, Ostmo, Susan, Chiang, Michael F., Bridge, Christopher P., Li, Matthew D., Singh, Praveer, Coyner, Aaron S., and Kalpathy-Cramer, Jayashree

Subjects
Electrical Engineering and Systems Science - Image and Video Processing, Computer Science - Computer Vision and Pattern Recognition, Computer Science - Machine Learning
Abstract

Many variables of interest in clinical medicine, like disease severity, are recorded using discrete ordinal categories such as normal/mild/moderate/severe. These labels are used to train and evaluate disease severity prediction models. However, ordinal categories represent a simplification of an underlying continuous severity spectrum. Using continuous scores instead of ordinal categories is more sensitive to detecting small changes in disease severity over time. Here, we present a generalized framework that accurately predicts continuously valued variables using only discrete ordinal labels during model development. We found that for three clinical prediction tasks, models that take the ordinal relationship of the training labels into account outperformed conventional multi-class classification models. Particularly the continuous scores generated by ordinal classification and regression models showed a significantly higher correlation with expert rankings of disease severity and lower mean squared errors compared to the multi-class classification models. Furthermore, the use of MC dropout significantly improved the ability of all evaluated deep learning approaches to predict continuously valued scores that truthfully reflect the underlying continuous target variable. We showed that accurate continuously valued predictions can be generated even if the model development only involves discrete ordinal labels. The novel framework has been validated on three different clinical prediction tasks and has proven to bridge the gap between discrete ordinal labels and the underlying continuously valued variables.

Published
2023

3. Use of an Artificial Intelligence-Generated Vascular Severity Score Improved Plus Disease Diagnosis in Retinopathy of Prematurity

Author

Coyner, Aaron S., Young, Benjamin K., Ostmo, Susan R., Grigorian, Florin, Ells, Anna, Hubbard, Baker, Rodriguez, Sarah H., Rishi, Pukhraj, Miller, Aaron M., Bhatt, Amit R., Agarwal-Sinha, Swati, Sears, Jonathan, Chan, R. V. Paul, Chiang, Michael F., Kalpathy-Cramer, Jayashree, Binenbaum, Gil, and Campbell, J. Peter

Published
2024
Full Text
View/download PDF

4. P Score: A Reference Image-Based Clinical Grading Scale for Vascular Change in Retinopathy of Prematurity

Author

Chiang, Michael F., Quinn, Graham E., Fielder, Alistair R., Ostmo, Susan R., Chan, R. V. Paul, Berrocal, Audina, Binenbaum, Gil, Blair, Michael, Campbell, J. Peter, Capone, Antonio, Jr., Chen, Yi, Dai, Shuan, Ells, Anna, Fleck, Brian W., Good, William V., Hartnett, M. Elizabeth, Holmstrom, Gerd, Kusaka, Shunji, Kychenthal, Andrés, Lepore, Domenico, Lorenz, Birgit, Martinez-Castellanos, Maria Ana, O¨zdek, Şengu¨l, Ademola-Popoola, Dupe, Reynolds, James D., Shah, Parag K., Shapiro, Michael, Stahl, Andreas, Toth, Cynthia, Vinekar, Anand, Visser, Linda, Wallace, David K., Wu, Wei-Chi, Zhao, Peiquan, Zin, Andrea, Baran, Francine, Barry, Gerard, Bhatt, Amit, Davitt, Bradley, de Alba Campomanes, Alejandra, Eldib, Amgad, Geddie, Brooke E., Haider, Kathryn, Jensen, Anne, Khitri, Monica, Pihlblad, Matthew, Repka, Michael, Robbins, Shira, Rodriguez, Sarah, Coyner, Aaron S., He, Jocelyn, Ying, Gui-shuang, and Ostmo, Susan

Published
2024
Full Text
View/download PDF

6. Not Color Blind: AI Predicts Racial Identity from Black and White Retinal Vessel Segmentations

Author

Coyner, Aaron S., Singh, Praveer, Brown, James M., Ostmo, Susan, Chan, R. V. Paul, Chiang, Michael F., Kalpathy-Cramer, Jayashree, and Campbell, J. Peter

Subjects
Computer Science - Computer Vision and Pattern Recognition
Abstract

Background: Artificial intelligence (AI) may demonstrate racial bias when skin or choroidal pigmentation is present in medical images. Recent studies have shown that convolutional neural networks (CNNs) can predict race from images that were not previously thought to contain race-specific features. We evaluate whether grayscale retinal vessel maps (RVMs) of patients screened for retinopathy of prematurity (ROP) contain race-specific features. Methods: 4095 retinal fundus images (RFIs) were collected from 245 Black and White infants. A U-Net generated RVMs from RFIs, which were subsequently thresholded, binarized, or skeletonized. To determine whether RVM differences between Black and White eyes were physiological, CNNs were trained to predict race from color RFIs, raw RVMs, and thresholded, binarized, or skeletonized RVMs. Area under the precision-recall curve (AUC-PR) was evaluated. Findings: CNNs predicted race from RFIs near perfectly (image-level AUC-PR: 0.999, subject-level AUC-PR: 1.000). Raw RVMs were almost as informative as color RFIs (image-level AUC-PR: 0.938, subject-level AUC-PR: 0.995). Ultimately, CNNs were able to detect whether RFIs or RVMs were from Black or White babies, regardless of whether images contained color, vessel segmentation brightness differences were nullified, or vessel segmentation widths were normalized. Interpretation: AI can detect race from grayscale RVMs that were not thought to contain racial information. Two potential explanations for these findings are that: retinal vessels physiologically differ between Black and White babies or the U-Net segments the retinal vasculature differently for various fundus pigmentations. Either way, the implications remain the same: AI algorithms have potential to demonstrate racial bias in practice, even when preliminary attempts to remove such information from the underlying images appear to be successful., Comment: 31 pages, 6 figures

Published
2021

10. Evaluation of an Artificial Intelligence System for Retinopathy of Prematurity Screening in Nepal and Mongolia

Author

Cole, Emily, Valikodath, Nita G., Al-Khaled, Tala, Bajimaya, Sanyam, KC, Sagun, Chuluunbat, Tsengelmaa, Munkhuu, Bayalag, Jonas, Karyn E., Chuluunkhuu, Chimgee, MacKeen, Leslie D., Yap, Vivien, Hallak, Joelle, Ostmo, Susan, Wu, Wei-Chi, Coyner, Aaron S., Singh, Praveer, Kalpathy-Cramer, Jayashree, Chiang, Michael F., Campbell, J. Peter, and Chan, R. V. Paul

Published
2022
Full Text
View/download PDF

13. Automated Fundus Image Quality Assessment in Retinopathy of Prematurity Using Deep Convolutional Neural Networks

Author

Chiang, Michael F., Ostmo, Susan, Kim, Sang Jin, Sonmez, Kemal, Campbell, J. Peter, Chan, R. V. Paul, Jonas, Karyn, Horowitz, Jason, Coki, Osode, Eccles, Cheryl-Ann, Sarna, Leora, Orlin, Anton, Berrocal, Audina, Negron, Catherin, Denser, Kimberly, Cumming, Kristi, Osentoski, Tammy, Check, Tammy, Zajechowski, Mary, Lee, Thomas, Kruger, Evan, McGovern, Kathryn, Simmons, Charles, Murthy, Raghu, Galvis, Sharon, Rotter, Jerome, Chen, Ida, Li, Xiaohui, Taylor, Kent, Roll, Kaye, Kalpathy-Cramer, Jayashree, Chang, Ken, Beers, Andrew, Erdogmus, Deniz, Ioannidis, Stratis, Martinez-Castellanos, Maria Ana, Salinas-Longoria, Samantha, Romero, Rafael, Arriola, Andrea, Olguin-Manriquez, Francisco, Meraz-Gutierrez, Miroslava, Dulanto-Reinoso, Carlos M., Montero-Mendoza, Cristina, Coyner, Aaron S., Swan, Ryan, Brown, James M., Jonas, Karyn E., and Chan, R.V. Paul

Published
2019
Full Text
View/download PDF

19. Efficacy of Smartphone-Based Telescreening for Retinopathy of Prematurity With and Without Artificial Intelligence in India

Author

Young, Benjamin K., primary, Cole, Emily D., additional, Shah, Parag K., additional, Ostmo, Susan, additional, Subramaniam, Prema, additional, Venkatapathy, Narendran, additional, Tsai, Andrew S. H., additional, Coyner, Aaron S., additional, Gupta, Aditi, additional, Singh, Praveer, additional, Chiang, Michael F., additional, Kalpathy-Cramer, Jayashree, additional, Chan, R. V. Paul, additional, and Campbell, J. Peter, additional

Published
2023
Full Text
View/download PDF

20. Assessment of retinopathy of prematurity regression and reactivation using an artificial intelligence–based vascular severity score

Author

Eilts, Sonja K., Pfeil, Johanna M., Poschkamp, Broder, Krohne, Tim U., Eter, Nicole, Barth, Teresa, Guthoff, Rainer, Lagrèze, Wolf, Grundel, Milena, Bründer, Marie-Christine, Busch, Martin, Kalpathy-Cramer, Jayashree, Chiang, Michael F., Chan, R. V. Paul, Coyner, Aaron S., Ostmo, Susan, Campbell, J. Peter, Stahl, Andreas, Pfeil, Johanna M, Bühler, Anima, Daniel, Moritz, Felzmann, Susanne, Gross, Nicolai, Horn, Stefanie, Lagrèze, Wolf A, Molnár, Fanni, Müller, Claudia, Reichl, Sabine, Reiff, Charlotte, Richter, Olga, Stech, Milena, Hentschel, Roland, Stavropolou, Dimitria, Tautz, Juliane, Bartsch, Kerstin, Braunstein, Jennifer, Brinken, Ralf, Brinkmann, Christian Karl, Czauderna, Joanna, Dralle, Wiebke, Gliem, Martin, Goebel, Arno, Heymer, Philipp, Hofmann, Martina, Holz, Frank G, Krohne, Tim U, Kupitz, David, Müller, Philipp, Petrak, Michael, Schmitz, Eva Janine, Schmitz-Valckenberg, Steffen, Schröder, Moritz, Steinberg, Julia, Supé, Julia, Kant, Evelyn, Kunze, Diana, Müller, Andreas, Adorf, Adeline, Alex, Anne, Alten, Florian, Clemens, Christoph R, Falkenau, Silvia, Friedhoff, Caroline, Loos, Desiree Sandra, Mihailovic, Natasa, Termühlen, Julia, Uhlig, Constantin, Hörnig-Franz, Isabell, Rieger-Fackeldey, Esther, Tekaat, Maria, Werner, Claudius, Altmann, Mathias, Blecha, Christiane, Brandl-Rühle, Sabine, Helbig, Horst, Hufendiek, Karsten, Jägle, Herbert, Konrad, Julia, Kopetzky, Eva, Lehmann, Fabian, Oberacher-Velten, Isabel, Keller-Wackerbauer, Annette, Kittel, Jochen, Segerer, Hugo, Ackermann, Phillip, Benga, Jemina, Guthoff, Tanja, Kleinert, Elena, Mayatepek, Ertan, Schrader, Stefan, Völker, Magdalena, Höhn, Thomas, Lohmeier, Klaus, Sabir, Hemmen, Brevis, Francisco, Mönig, Tina, Schwarz, Simone, Ehmer, Angela, Meltendorf, Synke, Schuart, Claudia, Avenarius, Stefan, Böttger, Ralf, Apel, Christoph, Bergmann, Anne, Herrmann, Karsten, Ockert-Schön, Franziska, Wegener, Sabine, Ehrt, Oliver, Nentwich, Martin, Pressler, Angelika, Rudolph, Günther, Genzel-Boroviczeny, Orsolya, Schmidt, Susanne, Münch, Hans-Georg, Thilmany, Claude, Aisenbrey, Sabine, Bruckmann, Anna, Dimopoulos, Spyridon, Hagemann, Ulrike, Inhoffen, Werner, Partsch, Michael, Schrader, Merle, Süsskind, Daniela, Völker, Michael, Bialkowski, Anja, Müller-Hansen, Ingo, Gerberth, Andrea, Hasselbach, Heike Christine, Lindemann, Solveig, Purtskhvanidze, Konstantine, Raffel, Yvonne, Roider, Johann, Schröder, Greta, Szymanek, Beke, Tode, Jan, Bendiks, Meike, Modlich, Simon, Jandeck, Isabel, Gerding, Heinrich, Smith, Lois E H, Eilts, Sonja K., Pfeil, Johanna M., Poschkamp, Broder, Krohne, Tim U., Eter, Nicole, Barth, Teresa, Guthoff, Rainer, Lagrèze, Wolf, Grundel, Milena, Bründer, Marie-Christine, Busch, Martin, Kalpathy-Cramer, Jayashree, Chiang, Michael F., Chan, R. V. Paul, Coyner, Aaron S., Ostmo, Susan, Campbell, J. Peter, Stahl, Andreas, Pfeil, Johanna M, Bühler, Anima, Daniel, Moritz, Felzmann, Susanne, Gross, Nicolai, Horn, Stefanie, Lagrèze, Wolf A, Molnár, Fanni, Müller, Claudia, Reichl, Sabine, Reiff, Charlotte, Richter, Olga, Stech, Milena, Hentschel, Roland, Stavropolou, Dimitria, Tautz, Juliane, Bartsch, Kerstin, Braunstein, Jennifer, Brinken, Ralf, Brinkmann, Christian Karl, Czauderna, Joanna, Dralle, Wiebke, Gliem, Martin, Goebel, Arno, Heymer, Philipp, Hofmann, Martina, Holz, Frank G, Krohne, Tim U, Kupitz, David, Müller, Philipp, Petrak, Michael, Schmitz, Eva Janine, Schmitz-Valckenberg, Steffen, Schröder, Moritz, Steinberg, Julia, Supé, Julia, Kant, Evelyn, Kunze, Diana, Müller, Andreas, Adorf, Adeline, Alex, Anne, Alten, Florian, Clemens, Christoph R, Falkenau, Silvia, Friedhoff, Caroline, Loos, Desiree Sandra, Mihailovic, Natasa, Termühlen, Julia, Uhlig, Constantin, Hörnig-Franz, Isabell, Rieger-Fackeldey, Esther, Tekaat, Maria, Werner, Claudius, Altmann, Mathias, Blecha, Christiane, Brandl-Rühle, Sabine, Helbig, Horst, Hufendiek, Karsten, Jägle, Herbert, Konrad, Julia, Kopetzky, Eva, Lehmann, Fabian, Oberacher-Velten, Isabel, Keller-Wackerbauer, Annette, Kittel, Jochen, Segerer, Hugo, Ackermann, Phillip, Benga, Jemina, Guthoff, Tanja, Kleinert, Elena, Mayatepek, Ertan, Schrader, Stefan, Völker, Magdalena, Höhn, Thomas, Lohmeier, Klaus, Sabir, Hemmen, Brevis, Francisco, Mönig, Tina, Schwarz, Simone, Ehmer, Angela, Meltendorf, Synke, Schuart, Claudia, Avenarius, Stefan, Böttger, Ralf, Apel, Christoph, Bergmann, Anne, Herrmann, Karsten, Ockert-Schön, Franziska, Wegener, Sabine, Ehrt, Oliver, Nentwich, Martin, Pressler, Angelika, Rudolph, Günther, Genzel-Boroviczeny, Orsolya, Schmidt, Susanne, Münch, Hans-Georg, Thilmany, Claude, Aisenbrey, Sabine, Bruckmann, Anna, Dimopoulos, Spyridon, Hagemann, Ulrike, Inhoffen, Werner, Partsch, Michael, Schrader, Merle, Süsskind, Daniela, Völker, Michael, Bialkowski, Anja, Müller-Hansen, Ingo, Gerberth, Andrea, Hasselbach, Heike Christine, Lindemann, Solveig, Purtskhvanidze, Konstantine, Raffel, Yvonne, Roider, Johann, Schröder, Greta, Szymanek, Beke, Tode, Jan, Bendiks, Meike, Modlich, Simon, Jandeck, Isabel, Gerding, Heinrich, and Smith, Lois E H

Published
2023

21. Association of Optical Coherence Tomography–Measured Fibrovascular Ridge Thickness and Clinical Disease Stage in Retinopathy of Prematurity

Author

Nguyen, Thanh-Tin P., primary, Ni, Shuibin, additional, Ostmo, Susan, additional, Rajagopalan, Archeta, additional, Coyner, Aaron S., additional, Woodward, Mani, additional, Chiang, Michael F., additional, Jia, Yali, additional, Huang, David, additional, Campbell, J. Peter, additional, and Jian, Yifan, additional

Published
2022
Full Text
View/download PDF

26. External Validation of a Retinopathy of Prematurity Screening Model Using Artificial Intelligence in 3 Low- and Middle-Income Populations

Author

Coyner, Aaron S., primary, Oh, Minn A., additional, Shah, Parag K., additional, Singh, Praveer, additional, Ostmo, Susan, additional, Valikodath, Nita G., additional, Cole, Emily, additional, Al-Khaled, Tala, additional, Bajimaya, Sanyam, additional, K.C., Sagun, additional, Chuluunbat, Tsengelmaa, additional, Munkhuu, Bayalag, additional, Subramanian, Prema, additional, Venkatapathy, Narendran, additional, Jonas, Karyn E., additional, Hallak, Joelle A., additional, Chan, R.V. Paul, additional, Chiang, Michael F., additional, Kalpathy-Cramer, Jayashree, additional, and Campbell, J. Peter, additional

Published
2022
Full Text
View/download PDF

27. Federated Learning for Multicenter Collaboration in Ophthalmology

Author

Lu, Charles, primary, Hanif, Adam, additional, Singh, Praveer, additional, Chang, Ken, additional, Coyner, Aaron S., additional, Brown, James M., additional, Ostmo, Susan, additional, Chan, Robison V. Paul, additional, Rubin, Daniel, additional, Chiang, Michael F., additional, Campbell, John Peter, additional, Kalpathy-Cramer, Jayashree, additional, Kim, Sang Jin, additional, Sonmez, Kemal, additional, Schelonka, Robert, additional, Coyner, Aaron, additional, Chan, R. V. Paul, additional, Jonas, Karyn, additional, Kolli, Bhavana, additional, Horowitz, Jason, additional, Coki, Osode, additional, Eccles, Cheryl-Ann, additional, Sarna, Leora, additional, Orlin, Anton, additional, Berrocal, Audina, additional, Negron, Catherin, additional, Denser, Kimberly, additional, Cumming, Kristi, additional, Osentoski, Tammy, additional, Check, Tammy, additional, Zajechowski, Mary, additional, Lee, Thomas, additional, Nagiel, Aaron, additional, Kruger, Evan, additional, McGovern, Kathryn, additional, Contractor, Dilshad, additional, Havunjian, Margaret, additional, Simmons, Charles, additional, Murthy, Raghu, additional, Galvis, Sharon, additional, Rotter, Jerome, additional, Chen, Ida, additional, Li, Xiaohui, additional, Taylor, Kent, additional, Roll, Kaye, additional, Hartnett, Mary Elizabeth, additional, Owen, Leah, additional, Moshfeghi, Darius, additional, Nunez, Mariana, additional, Wennber-Smith, Zac, additional, Erdogmus, Deniz, additional, Ioannidis, Stratis, additional, Martinez-Castellanos, Maria Ana, additional, Salinas-Longoria, Samantha, additional, Romero, Rafael, additional, Arriola, Andrea, additional, Olguin-Manriquez, Francisco, additional, Meraz-Gutierrez, Miroslava, additional, Dulanto-Reinoso, Carlos M., additional, and Montero-Mendoza, Cristina, additional

Published
2022
Full Text
View/download PDF

28. Federated Learning for Multicenter Collaboration in Ophthalmology

Author

Hanif, Adam, primary, Lu, Charles, additional, Chang, Ken, additional, Singh, Praveer, additional, Coyner, Aaron S., additional, Brown, James M., additional, Ostmo, Susan, additional, Chan, Robison V. Paul, additional, Rubin, Daniel, additional, Chiang, Michael F., additional, Kalpathy-Cramer, Jayashree, additional, Campbell, John Peter, additional, Kim, Sang Jin, additional, Sonmez, Kemal, additional, Schelonka, Robert, additional, Coyner, Aaron, additional, Chan, R.V. Paul, additional, Jonas, Karyn, additional, Kolli, Bhavana, additional, Horowitz, Jason, additional, Coki, Osode, additional, Eccles, Cheryl-Ann, additional, Sarna, Leora, additional, Orlin, Anton, additional, Berrocal, Audina, additional, Negron, Catherin, additional, Denser, Kimberly, additional, Cumming, Kristi, additional, Osentoski, Tammy, additional, Check, Tammy, additional, Zajechowski, Mary, additional, Lee, Thomas, additional, Nagiel, Aaron, additional, Kruger, Evan, additional, McGovern, Kathryn, additional, Contractor, Dilshad, additional, Havunjian, Margaret, additional, Simmons, Charles, additional, Murthy, Raghu, additional, Galvis, Sharon, additional, Rotter, Jerome, additional, Chen, Ida, additional, Li, Xiaohui, additional, Taylor, Kent, additional, Roll, Kaye, additional, Hartnett, Mary Elizabeth, additional, Owen, Leah, additional, Moshfeghi, Darius, additional, Nunez, Mariana, additional, Wennber-Smith, Zac, additional, Erdogmus, Deniz, additional, Ioannidis, Stratis, additional, Martinez-Castellanos, Maria Ana, additional, Salinas-Longoria, Samantha, additional, Romero, Rafael, additional, Arriola, Andrea, additional, Olguin-Manriquez, Francisco, additional, Meraz-Gutierrez, Miroslava, additional, Dulanto-Reinoso, Carlos M., additional, and Montero-Mendoza, Cristina, additional

Published
2022
Full Text
View/download PDF

30. Single-Examination Risk Prediction of Severe Retinopathy of Prematurity

Author

Coyner, Aaron S., primary, Chen, Jimmy S., additional, Singh, Praveer, additional, Schelonka, Robert L., additional, Jordan, Brian K., additional, McEvoy, Cindy T., additional, Anderson, Jamie E., additional, Chan, R.V. Paul, additional, Sonmez, Kemal, additional, Erdogmus, Deniz, additional, Chiang, Michael F., additional, Kalpathy-Cramer, Jayashree, additional, and Campbell, J. Peter, additional

Published
2021
Full Text
View/download PDF

31. Deepfakes in Ophthalmology

Author

Chen, Jimmy S., primary, Coyner, Aaron S., additional, Chan, R.V. Paul, additional, Hartnett, M. Elizabeth, additional, Moshfeghi, Darius M., additional, Owen, Leah A., additional, Kalpathy-Cramer, Jayashree, additional, Chiang, Michael F., additional, and Campbell, J. Peter, additional

Published
2021
Full Text
View/download PDF

32. Deep Learning for the Diagnosis of Stage in Retinopathy of Prematurity

Author

Chen, Jimmy S., primary, Coyner, Aaron S., additional, Ostmo, Susan, additional, Sonmez, Kemal, additional, Bajimaya, Sanyam, additional, Pradhan, Eli, additional, Valikodath, Nita, additional, Cole, Emily D., additional, Al-Khaled, Tala, additional, Chan, R. V. Paul, additional, Singh, Praveer, additional, Kalpathy-Cramer, Jayashree, additional, Chiang, Michael F., additional, and Campbell, J. Peter, additional

Published
2021
Full Text
View/download PDF

35. Introduction to Machine Learning, Neural Networks, and Deep Learning

Author

Choi, Rene Y., Coyner, Aaron S., Kalpathy-Cramer, Jayashree, Chiang, Michael F., and Campbell, J. Peter

Subjects
Machine Learning, Ophthalmology, Deep Learning, Special Issue, Artificial Intelligence, Neural Networks, Computer
Abstract

Purpose To present an overview of current machine learning methods and their use in medical research, focusing on select machine learning techniques, best practices, and deep learning. Methods A systematic literature search in PubMed was performed for articles pertinent to the topic of artificial intelligence methods used in medicine with an emphasis on ophthalmology. Results A review of machine learning and deep learning methodology for the audience without an extensive technical computer programming background. Conclusions Artificial intelligence has a promising future in medicine; however, many challenges remain. Translational Relevance The aim of this review article is to provide the nontechnical readers a layman's explanation of the machine learning methods being used in medicine today. The goal is to provide the reader a better understanding of the potential and challenges of artificial intelligence within the field of medicine.

Published
2020

36. Automated Fundus Image Quality Assessment in Retinopathy of Prematurity

Author

Coyner, Aaron S, Swan, Ryan, Campbell, J Peter, Kalpathy-Cramer, Jayashree, and Chiang, Michael F

Subjects
genetic structures, sense organs, eye diseases
Abstract

Presentation at the 2019 National Library of Medicine Informatics Training Conference on Automated Fundus Image Quality Assessment in Retinopathy of Prematurity.

Published
2019
Full Text
View/download PDF

38. Automated Fundus Image Quality Assessment in Retinopathy of Prematurity Using Deep Convolutional Neural Networks

Author

Coyner, Aaron S., primary, Swan, Ryan, additional, Campbell, J. Peter, additional, Ostmo, Susan, additional, Brown, James M., additional, Kalpathy-Cramer, Jayashree, additional, Kim, Sang Jin, additional, Jonas, Karyn E., additional, Chan, R.V. Paul, additional, Chiang, Michael F., additional, Sonmez, Kemal, additional, Chan, R. V. Paul, additional, Jonas, Karyn, additional, Horowitz, Jason, additional, Coki, Osode, additional, Eccles, Cheryl-Ann, additional, Sarna, Leora, additional, Orlin, Anton, additional, Berrocal, Audina, additional, Negron, Catherin, additional, Denser, Kimberly, additional, Cumming, Kristi, additional, Osentoski, Tammy, additional, Check, Tammy, additional, Zajechowski, Mary, additional, Lee, Thomas, additional, Kruger, Evan, additional, McGovern, Kathryn, additional, Simmons, Charles, additional, Murthy, Raghu, additional, Galvis, Sharon, additional, Rotter, Jerome, additional, Chen, Ida, additional, Li, Xiaohui, additional, Taylor, Kent, additional, Roll, Kaye, additional, Chang, Ken, additional, Beers, Andrew, additional, Erdogmus, Deniz, additional, Ioannidis, Stratis, additional, Martinez-Castellanos, Maria Ana, additional, Salinas-Longoria, Samantha, additional, Romero, Rafael, additional, Arriola, Andrea, additional, Olguin-Manriquez, Francisco, additional, Meraz-Gutierrez, Miroslava, additional, Dulanto-Reinoso, Carlos M., additional, and Montero-Mendoza, Cristina, additional

Published
2019
Full Text
View/download PDF

40. SCLERAL PITS IN CHOROIDEREMIA:Implications for Retinal Gene Therapy

Author

Al-Qahtani, Abdullah A, Ba-Ali, Shakoor, Alabduljalil, Talal, Coyner, Aaron S, Patel, Rachel C, Weleber, Richard G, Girach, Aniz, Christensen, Søren K, Larsen, Michael, Pennesi, Mark E, Yang, Paul, Al-Qahtani, Abdullah A, Ba-Ali, Shakoor, Alabduljalil, Talal, Coyner, Aaron S, Patel, Rachel C, Weleber, Richard G, Girach, Aniz, Christensen, Søren K, Larsen, Michael, Pennesi, Mark E, and Yang, Paul

Abstract

PURPOSE: We report a novel finding on spectral domain optical coherence tomography in patients with choroideremia, which we describe as scleral pits (SCPs).METHODS: Cross-sectional observational case series of 36 patients with choroideremia, who underwent ophthalmic examination and multimodal imaging, including optical coherence tomography of the macula. Optical coherence tomography images were reviewed for SCP, which were defined as discrete tracts of hyporeflectivity that traverse the sclera with or without the involvement of Bruch membrane, retinal pigment epithelium, and retina. Unpaired two-tailed t-test with Welch correction was used for statistical analysis.RESULTS: Of the 36 patients, 19 had SCP in at least one eye. Scleral pits were confined to areas of advanced chorioretinal degeneration and never involved the foveola. Type 1 SCP affected only the sclera, whereas Type 2 SCP also involved the Bruch membrane and the retinal pigment epithelium. Type 3 SCP additionally had a full-thickness retinal defect. Patients with SCP were significantly older (51 ± 2 vs. 33 ± 4 years; P < 0.05) and had lower best-corrected visual acuity (20/160 vs. 20/30 or 0.9 ± 0.2 vs. 0.2 ± 0.07 logarithm of the minimum angle of resolution; P < 0.05) than patients without SCP. Patients with SCP had a greater myopic refractive error compared with patients without SCP (-2.6 ± 0.5 vs. -0.3 ± 0.5D; P < 0.05), but there was no significant correlation between the number of SCPs with refraction. Short posterior ciliary arteries were observed to enter the eye through one Type 3 SCP.CONCLUSION: Scleral pits are, to the best of our knowledge, a novel optical coherence tomography finding in advanced choroideremia that likely represents the abnormal juxtaposition of penetrating short posterior ciliary arteries with the retina.

Published
2018

41. Automated Image Quality Assessment for Fundus Images in Retinopathy of Prematurity

Author

Coyner, Aaron S, Swan, Ryan, Kalpathy-Cramer, Jayashree, Kim, Sang Jin, Campbell, J Peter, Jonas, Karyn, Ostmo, Susan, Chan, RV Paul, and Chiang, Michael F

Subjects
congenital, hereditary, and neonatal diseases and abnormalities, genetic structures, sense organs, eye diseases
Abstract

The purpose of this study was to implement an algorithm for automatically assessing the quality of fundus images, and to evaluate its performance on a set of Retinopathy of Prematurity (ROP) images compared to expert assessment. The computer-based algorithm assessed image quality with strong correlation to the expert consensus rank, although not as high as individual expert correlation with the consensus rank.

Published
2017
Full Text
View/download PDF

48. Federated learning for multi-center collaboration in ophthalmology: implications for clinical diagnosis and disease epidemiology

Author

Hanif, Adam, Lu, Charles, Chang, Ken, Singh, Praveer, Coyner, Aaron S., Brown, James M., Ostmo, Susan, Chan, RV Paul, Rubin, Daniel, Chiang, Michael F., Kalpathy-Cramer, Jayashree, Campbell, J Peter, Chiang, Michael F., Ostmo, Susan, Kim, Sang Jin, Sonmez, Kemal, Campbell, J. Peter, Schelonka, Robert, Coyner, Aaron, Chan, RV Paul, Jonas, Karyn, Kolli, Bhavana, Horowitz, Jason, Coki, Osode, Eccles, Cheryl-Ann, Sarna, Leora, Orlin, Anton, Berrocal, Audina, Negron, Catherin, Denser, Kimberly, Cumming, Kristi, Osentoski, Tammy, Check, Tammy, Zajechowski, Mary, Lee, Thomas, Nagiel, Aaron, Kruger, Evan, McGovern, Kathryn, Contractor, Dilshad, Havunjian, Margaret, Simmons, Charles, Murthy, Raghu, Galvis, Sharon, NNP, Rotter, Jerome, Chen, Ida, Li, Xiaohui, Taylor, Kent, Roll, Kaye, Hartnett, Mary Elizabeth, Owen, Leah, Moshfeghi, Darius, Nunez, Mariana, Wennber-Smith, Zac, Kalpathy-Cramer, Jayashree, Erdogmus, Deniz, Ioannidis, Stratis, Martinez-Castellanos, Maria Ana, Salinas-Longoria, Samantha, Romero, Rafael, Arriola, Andrea, Olguin-Manriquez, Francisco, Meraz-Gutierrez, Miroslava, Dulanto-Reinoso, Carlos M., and Montero-Mendoza, Cristina

Abstract

To utilize a deep learning (DL) model trained via federated learning (FL), a method of collaborative training without sharing patient data, to delineate institutional differences in clinician diagnostic paradigms and disease epidemiology in retinopathy of prematurity (ROP).

Published
2022
Full Text
View/download PDF

49. Retinal Neuroprotective Effects of Flibanserin, an FDA-Approved Dual Serotonin Receptor Agonist-Antagonist

Author

Coyner, Aaron S., Ryals, Renee C., Ku, Cristy A., Fischer, Cody M., Patel, Rachel C., Datta, Shreya, Yang, Paul, Wen, Yuquan, Hen, Rene, and Pennesi, Mark E.

Subjects
Serotonin--Receptors, Psychiatry, FOS: Clinical medicine, Neurosciences, Medicine, Apoptosis, Retina, 3. Good health, Neuroprotective agents
Abstract

Purpose: To assess the neuroprotective effects of flibanserin (formerly BIMT-17), a dual 5-HT1A agonist and 5-HT2A antagonist, in a light-induced retinopathy model. Methods: Albino BALB/c mice were injected intraperitoneally with either vehicle or increasing doses of flibanserin ranging from 0.75 to 15 mg/kg flibanserin. To assess 5-HT1A-mediated effects, BALB/c mice were injected with 10 mg/kg WAY 100635, a 5-HT1A antagonist, prior to 6 mg/kg flibanserin and 5-HT1A knockout mice were injected with 6 mg/kg flibanserin. Injections were administered once immediately prior to light exposure or over the course of five days. Light exposure lasted for one hour at an intensity of 10,000 lux. Retinal structure was assessed using spectral domain optical coherence tomography and retinal function was assessed using electroretinography. To investigate the mechanisms of flibanserin-mediated neuroprotection, gene expression, measured by RT-qPCR, was assessed following five days of daily 15 mg/kg flibanserin injections. Results: A five-day treatment regimen of 3 to 15 mg/kg of flibanserin significantly preserved outer retinal structure and function in a dose-dependent manner. Additionally, a single-day treatment regimen of 6 to 15 mg/kg of flibanserin still provided significant protection. The action of flibanserin was hindered by the 5-HT1A antagonist, WAY 100635, and was not effective in 5-HT1A knockout mice. Creb, c-Jun, c-Fos, Bcl-2, Cast1, Nqo1, Sod1, and Cat were significantly increased in flibanserin-injected mice versus vehicle-injected mice. Conclusions: Intraperitoneal delivery of flibanserin in a light-induced retinopathy mouse model provides retinal neuroprotection. Mechanistic data suggests that this effect is mediated through 5-HT1A receptors and that flibanserin augments the expression of genes capable of reducing mitochondrial dysfunction and oxidative stress. Since flibanserin is already FDA-approved for other indications, the potential to repurpose this drug for treating retinal degenerations merits further investigation.

50. Software Development Best Practices of the AI-READI Project

Author

Patel, Bhavesh, Soundarajan, Sanjay, McWeeney, Shannon, Cordier, Benjamin A., Benton, Erik S., Baxter, Sally, Cohen, Aaron, Coyner, Aaron S., Ishikawa, Hiroshi, Lee, Aaron Y., Lee, Cecilia S., Lin, Wei-Chun, Liz Moyer, Lucero, Abigail, Mitchell, Julie A., Nebeker, Camille, and Singer, Sara

Subjects
open source, research software, guidelines, fair, collaboration
Abstract

We share herethe software development best practices of the AI-READI project, one of the data generation projects funded under the NIH Bridge2AI program. These best practices are maintained from a dedicated GitHub repositorywhere you can follow the latest developments.Use theGitHub issuesfor submitting feedback or making suggestions. The rationales for establishing these best practices are to: Help the standardization of the AI-READI software to facilitate future reuse, i.e. make the AI-READI software Findable, Accessible, Interoperable, and Reusable (FAIR) Ensure the efficient and effective development of software across the project, especially when contributions from several teams are required Facilitate on-boarding of new developers to the project Our focus here is on establishing actionable best practices that can be directly implemented by developers of the project. These best practices are intended to remain general enough such that they can apply to any software development project within AI-READI.&nbsp

Published
2022
Full Text
View/download PDF

Catalog

Books, media, physical & digital resources
See catalog results