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7 results on '"Kanai, Kuniyoshi"'

1. Case Series: Unbiased Deep Sequencing Analysis of Acute Infectious Conjunctivitis in an Ambulatory Eye Center in Berkeley, California.

Author

Yeung, Sze, Coulter, Adrienne, Roufail, Mareena, Ruder, Kevin, Chen, Cindi, Liu, David, Abraham, Thomas, Hinterwirth, Armin, Whiteside, Meredith, Wong, Michael, La, Tammy, Nassiri, Maryam, Doan, Thuy, Seitzman, Gerami, Lietman, Thomas, Kanai, Kuniyoshi, and Lee, Sam

Subjects
Humans, Acute Disease, California, Conjunctivitis, COVID-19, High-Throughput Nucleotide Sequencing, Pandemics
Abstract

SIGNIFICANCE: Acute infectious conjunctivitis poses significant challenges to eye care providers. It can be highly transmissible, and because etiology is often presumed, correct treatment and management can be difficult. This study uses unbiased deep sequencing to identify causative pathogens of infectious conjunctivitis, potentially allowing for improved approaches to diagnosis and management. PURPOSES: This study aimed to identify associated pathogens of acute infectious conjunctivitis in a single ambulatory eye care center. CASE REPORTS: This study included patients who presented to the University of California Berkeley eye center with signs and symptoms suggestive of infectious conjunctivitis. From December 2021 to July 2021, samples were collected from seven subjects (ages ranging from 18 to 38). Deep sequencing identified associated pathogens in five of seven samples, including human adenovirus D, Haemophilus influenzae , Chlamydia trachomatis , and human coronavirus 229E. CONCLUSIONS: Unbiased deep sequencing identified some unexpected pathogens in subjects with acute infectious conjunctivitis. Human adenovirus D was recovered from only one patient in this series. Although all samples were obtained during the COVID-19 pandemic, only one case of human coronavirus 229E and no SARS-CoV-2 were identified.

Published
2023

2. Predicting optical coherence tomography-derived diabetic macular edema grades from fundus photographs using deep learning

Author

Varadarajan, Avinash V, Bavishi, Pinal, Ruamviboonsuk, Paisan, Chotcomwongse, Peranut, Venugopalan, Subhashini, Narayanaswamy, Arunachalam, Cuadros, Jorge, Kanai, Kuniyoshi, Bresnick, George, Tadarati, Mongkol, Silpa-archa, Sukhum, Limwattanayingyong, Jirawut, Nganthavee, Variya, Ledsam, Joseph R, Keane, Pearse A, Corrado, Greg S, Peng, Lily, and Webster, Dale R

Subjects
Biomedical and Clinical Sciences, Ophthalmology and Optometry, Biomedical Imaging, Eye Disease and Disorders of Vision, Diabetes, Bioengineering, Clinical Research, 4.2 Evaluation of markers and technologies, 4.1 Discovery and preclinical testing of markers and technologies, Detection, screening and diagnosis, Eye, Aged, Deep Learning, Diabetic Retinopathy, Female, Humans, Imaging, Three-Dimensional, Macular Edema, Male, Middle Aged, Mutation, Photography, Retina, Tomography, Optical Coherence
Abstract

Center-involved diabetic macular edema (ci-DME) is a major cause of vision loss. Although the gold standard for diagnosis involves 3D imaging, 2D imaging by fundus photography is usually used in screening settings, resulting in high false-positive and false-negative calls. To address this, we train a deep learning model to predict ci-DME from fundus photographs, with an ROC-AUC of 0.89 (95% CI: 0.87-0.91), corresponding to 85% sensitivity at 80% specificity. In comparison, retinal specialists have similar sensitivities (82-85%), but only half the specificity (45-50%, p

Published
2020

3. Predicting optical coherence tomography-derived diabetic macular edema grades from fundus photographs using deep learning

Author

Varadarajan, Avinash, Bavishi, Pinal, Raumviboonsuk, Paisan, Chotcomwongse, Peranut, Venugopalan, Subhashini, Narayanaswamy, Arunachalam, Cuadros, Jorge, Kanai, Kuniyoshi, Bresnick, George, Tadarati, Mongkol, Silpa-archa, Sukhum, Limwattanayingyong, Jirawut, Nganthavee, Variya, Ledsam, Joe, Keane, Pearse A, Corrado, Greg S, Peng, Lily, and Webster, Dale R

Subjects
Computer Science - Computer Vision and Pattern Recognition, Computer Science - Machine Learning, Statistics - Machine Learning
Abstract

Diabetic eye disease is one of the fastest growing causes of preventable blindness. With the advent of anti-VEGF (vascular endothelial growth factor) therapies, it has become increasingly important to detect center-involved diabetic macular edema (ci-DME). However, center-involved diabetic macular edema is diagnosed using optical coherence tomography (OCT), which is not generally available at screening sites because of cost and workflow constraints. Instead, screening programs rely on the detection of hard exudates in color fundus photographs as a proxy for DME, often resulting in high false positive or false negative calls. To improve the accuracy of DME screening, we trained a deep learning model to use color fundus photographs to predict ci-DME. Our model had an ROC-AUC of 0.89 (95% CI: 0.87-0.91), which corresponds to a sensitivity of 85% at a specificity of 80%. In comparison, three retinal specialists had similar sensitivities (82-85%), but only half the specificity (45-50%, p<0.001 for each comparison with model). The positive predictive value (PPV) of the model was 61% (95% CI: 56-66%), approximately double the 36-38% by the retinal specialists. In addition to predicting ci-DME, our model was able to detect the presence of intraretinal fluid with an AUC of 0.81 (95% CI: 0.81-0.86) and subretinal fluid with an AUC of 0.88 (95% CI: 0.85-0.91). The ability of deep learning algorithms to make clinically relevant predictions that generally require sophisticated 3D-imaging equipment from simple 2D images has broad relevance to many other applications in medical imaging.

Published
2018
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4. Coxsackievirus A24 causing acute conjunctivitis in a 2023 outbreak in Vietnam

Author

Prajna, Lalitha, Prajna, N. Venkatesh, Gunasekaran, Ramesh, Sharma, Sankalp Singh, Teja, Vishnu, Chaudhary, Meenu, Sitaula, Sanjeeta, Sié, Ali, Coulibaly, Boubacar, Bountogo, Mamadou, Somkijrungroj, Thanapong, Satitpitakul, Vannarut, Tran, Huy, Mai, Linh Hoàng, Xuân, Thảo Hạ, Tran, Yen, Urzua, Cristhian A., Vega, Fabian, Salgado, Felipe, Cuitino, Loreto, Pérez, Fernando Pérez, Martínez, Jaime Macías, Lansingh, Van Charles, Thanapaisal, Sukhumal, Laovirojjanakul, Wipada, McKie, George, Chavez, Kenia, Redd, Travis, Chamberlain, Winston, Cheng, Angel, Tham, Vivien, Sansanayudh, Wiwan, Yakoura, Abba Kaka Hajia, Amza, Abdou, Souley, Abdoul Salam Youssoufou, Diori, Adam Nouhou, Nassirou, Beido, Kadri, Boubacar, Mariama, Boubacar, Ibrahim, Cissé Mamadou, Roufaye, Lamyne Aboubacar, Boulhassane, Ramatou, Ali, Saley, Abdou, Zakou, Goren, Lee, Bahar, Irit, Sella, Ruti, Kelliher, Clare, Green, Laura, Ong, Hon Shing, Mehta, Jod, Liu, Yu-Chi, Pinsky, Benjamin A., Hwang, De-Kuang, Fan, Nai-Wen, Chiong, Hong Sheng, Lacorzana, Javier, Cabrera-Aguas, Maria, Watson, Stephanie, Tsui, Edmund, Cherian, Nina M., Feit-Leichman, Rachel, Hughes, Reginald E., Jr, Onclinx, Tania, Privratsky, Joseph K, Yu, Carol, McClean, Esmeralda, Molina, Iliana, Hinterwirth, Armin, Chen, Cindi, Yu, Danny, Liu, David, Lebas, Elodie, Colby, Emily, Seitzman, Gerami, Ruder, Kevin, Zhong, Lina, Deiner, Michael, Abraham, Thomas, Lietman, Thomas, Doan, Thuy, Porco, Travis, McLeod, Stephen, Kanai, Kuniyoshi, Whiteside, Meredith, Yeh, Steven, Fashina, Tolulope, Chodosh, James, Tarkap, Bridgit, Garap, Jambi N., Mangot, Magdalene, Amel, Edwin, Taleo, Fasihah, Kasso, Johnson, Willie, Kalbule, Nanu, Madopule, Rymill, Prudence, Solomon, Anthony W., Ha, Thao, Hoang, Linh, Liu, YuHeng, Porco, Travis C., Lietman, Thomas M., and Seitzman, Gerami D.

Published
2024
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6. Case Series: Unbiased Deep Sequencing Analysis of Acute Infectious Conjunctivitis in an Ambulatory Eye Center in Berkeley, California

Author

Kanai, Kuniyoshi, Whiteside, Meredith, Wong, Michael, La, Tammy, Nassiri, Maryam, Lee, Sam, Yeung, Sze Kei, Coulter, Adrienne, Roufail, Mareena, Ruder, Kevin, Chen, Cindi, Liu, David, Abraham, Thomas, Hinterwirth, Armin, Lietman, Thomas M., Doan, Thuy, Seitzman, Gerami D., Prajna, Lalitha, Prajna, N. Venkatesh, Gunasekaran, Ramesh, Sharma, Sankalp Singh, Teja, Vishnu, Chaudhary, Meenu, Sitaula, Sanjeeta, Sié, Ali, Coulibaly, Boubacar, Bountogo, Mamadou, Somkijrungroj, Thanapong, Satitpitakul, Vannarut, Tran, Huy, Mai, Linh Hoàng, Xuân, Thảo Hạ, Tran, Yen, Urzua, Cristhian A., Vega, Fabian, Salgado, Felipe, Cuitino, Loreto, Pérez, Fernando Pérez, Martínez, Jaime Macías, Lansingh, Van Charles, Thanapaisal, Sukhumal, Laovirojjanakul, Wipada, McKie, George, Chavez, Kenia, Redd, Travis, Chamberlain, Winston, Cheng, Angel, Tham, Vivien, Sansanayudh, Wiwan, Yakoura, Abba Kaka Hajia, Amza, Abdou, Souley, Abdoul Salam Youssoufou, Diori, Adam Nouhou, Nassirou, Beido, Kadri, Boubacar, Mariama, Boubacar, Ibrahim, Cissé Mamadou, Roufaye, Lamyne Aboubacar, Boulhassane, Ramatou, Ali, Saley, Abdou, Zakou, Goren, Lee, Sella, Ruti, Kelliher, Clare, Green, Laura, Ong, Hon Shing, Mehta, Jod, Liu, Yu-Chi, Pinsky, Benjamin A., Hwang, De-Kuang, Fan, Nai-Wen, Chiong, Hong Sheng, Lacorzana, Javier, Cabrera-Aguas, Maria, Watson, Stephanie, Tsui, Edmund, Ramirez, Joana, Cherian, Nina M., Feit-Leichman, Rachel, Hughes, Reginald E., Onclinx, Tania, Yu, Carol, McClean, Esmeralda, Molina, Iliana, Hinterwirth, Armin, Chen, Cindi, Yu, Danny, Liu, David, Lebas, Elodie, Colby, Emily, Seitzman, Gerami, Ruder, Kevin, Zhong, Lina, Deiner, Michael, Abraham, Thomas, Lietman, Thomas, Doan, Thuy, Porco, Travis, McLeod, Stephen, Kanai, Kuniyoshi, Whiteside, Meredith, Yeh, Steven, Fashina, Tolulope, Chodosh, James, Tarkap, Bridgit, Garap, Jambi N., Mangot, Magdalene, Amel, Edwin, Taleo, Fasihah, Kasso, Johnson, Willie, Kalbule, Nanu, Madopule, Rymill, Prudence, and Solomon, Anthony W.

Published
2023
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