15 results on '"Carlos M. Dulanto-Reinoso"'
Search Results
2. Squamous neoplasia of the ocular surface in patients with pterygium in Peru
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Luis Furuya-Kanamori, Carlos M. Dulanto-Reinoso, Jennifer C Stone, Leila Marroquín, Victor Ch. Dulanto-Reinoso, José A. Roca, Francisco Contreras, and Graham A. Lee
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pterigion ,neoplasias oculares ,diagnóstico clínico ,Medicine ,Medicine (General) ,R5-920 - Abstract
Objectives. To estimate the frequency of unsuspected ocular surface squamous neoplasia (OSSN) in pterygium, the accuracy of clinical diagnosis, and associated demographic and clinical characteristics. Materials and methods. We reviewed histopathological reports of patients with a clinical diagnosis of pterygium and/or OSSN who were surgically treated between March 2009 and December 2012 at the National Eye Institute in Lima, Peru. The accuracy of the clinical diagnosis of OSSN was assessed by sensitivity, specificity, and likelihood ratios. Models of negative log-log regression were performed to identify demographic and clinical characteristics associated with increased odds of diagnosing OSSN. Results. 3,021 histopathological reports were reviewed. The frequency of unsuspected OSSN in pterygium was 0.65%. Clinical diagnosis had a sensitivity of 85%, a specificity of 99%, a positive likelihood ratio of 111.89, and a negative likelihood ratio of 0.15. Associated characteristics were male gender (OR =1.15; 95% CI: 1.01 to 1.30), age group of 61- 80 years (OR = 1.54, 95% CI: 1.28 to 1.85) ≥ 81 years (OR = 3.10; 95% CI: 2.09 to 4.58), presence of recurrent lesions (OR = 1.59; 95% CI: 1.03 to 2.46) and temporal location lesions (OR = 3.57; 95% CI: 2.63 to 4.85). These characteristics were associated with a greater likelihood of OSSN. Conclusions. A low frequency of unsuspected OSSN was found; however, it is recommended to routinely perform histopathology studies to avoid misdiagnosis of OSSN as pterygium.
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- 2014
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3. Neoplasia escamosa de la superficie ocular en pacientes con pterigión en Perú
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Luis Furuya-Kanamori, Carlos M. Dulanto-Reinoso, Jennifer C. Stone, Lelia Marroquín, Victor Ch. Dulanto-Reinoso, José A. Roca, Francisco Contreras, and Graham A. Lee
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pterigion ,neoplasias oculares ,diagnóstico clínico ,Medicine ,Medicine (General) ,R5-920 - Abstract
Objetivos. Estimar la frecuencia de neoplasia escamosa de la superficie ocular (NESO) no sospechada en pterigión, la precisión del diagnóstico clínico y las características demográficas y clínicas asociadas. Materiales y métodos. Se examinaron los informes histopatológicos de los pacientes con diagnóstico clínico de pterigión y/o NESO que fueron quirúrgicamente tratados entre marzo de 2009 y diciembre de 2012 en el Instituto Nacional de Oftalmología en Lima, Perú. La precisión del diagnóstico clínico para identificar la NESO se evaluó mediante la sensibilidad, especificidad y los cocientes de probabilidad. Se realizaron modelos de regresión log-log negativos para identificar las características demográficas y clínicas asociadas con un aumento de las probabilidades de diagnosticar NESO. Resultados. Se examinaron 3021 informes de histopatología. La frecuencia de NESO no sospechada en pterigión fue de 0,65%. El diagnóstico clínico presentó una sensibilidad del 85%, una especificidad del 99%, un cociente de probabilidad positiva de 111,89 y un cociente probabilidad negativa de 0,15. Las características asociadas fueron el sexo masculino (OR 1,15; IC 95%:1,01-1,30), pacientes de 61 a 80 años (OR 1,54; IC 95%: 1,28-1,85), ≥ de 81 años (OR 3,10; IC 95%: 2,09-4,58), pacientes con lesiones recurrentes (OR 1,59; IC 95%: 1,03-2,46) y lesiones en el lado temporal (OR 3,57; IC 95%: 2,63-4,85) presentaron mayor probabilidad de NESO. Conclusiones. Se encontró una baja frecuencia de NESO no sospechada, sin embargo, es recomendable realizar el estudio histopatológico de forma rutinaria para evitar diagnósticos erróneos de NESO como pterigión.
4. Variability in Plus Disease Diagnosis using Single and Serial Images
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Emily D. Cole, Shin Hae Park, Sang Jin Kim, Kai B. Kang, Nita G. Valikodath, Tala Al-Khaled, Samir N. Patel, Karyn E. Jonas, Susan Ostmo, Aaron Coyner, Audina Berrocal, Kimberly A. Drenser, Aaron Nagiel, Jason D. Horowitz, Thomas C. Lee, Jayashree Kalpathy-Cramer, Michael F. Chiang, J. Peter Campbell, R.V. Paul Chan, Kemal Sonmez, RV Paul Chan, Karyn Jonas, Jason Horowitz, Osode Coki, Cheryl-Ann Eccles, Leora Sarna, Anton Orlin, Catherin Negron, Kimberly Denser, Kristi Cumming, Tammy Osentoski, Tammy Check, Mary Zajechowski, Thomas Lee, Evan Kruger, Kathryn McGovern, Charles Simmons, Raghu Murthy, Sharon Galvis, Jerome Rotter, Ida Chen, Xiaohui Li, Kent Taylor, Kaye Roll, Deniz Erdogmus, Stratis Ioannidis, Maria Ana Martinez-Castellanos, Samantha Salinas-Longoria, Rafael Romero, Andrea Arriola, Francisco Olguin-Manriquez, Miroslava Meraz-Gutierrez, Carlos M. Dulanto-Reinoso, and Cristina Montero-Mendoza
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Cohort Studies ,Diagnostic Imaging ,Ophthalmology ,Infant, Newborn ,Humans ,Reproducibility of Results ,Retinopathy of Prematurity ,Telemedicine ,Article - Abstract
PURPOSE: To assess changes in retinopathy of prematurity (ROP) diagnosis in single and serial retinal images. DESIGN: Cohort study. PARTICIPANTS: Cases of ROP recruited from the Imaging and Informatics in Retinopathy of Prematurity (i-ROP) consortium evaluated by 7 graders. METHODS: Seven ophthalmologists reviewed both single and 3 consecutive serial retinal images from 15 cases with ROP, and severity was assigned as plus, preplus, or none. Imaging data were acquired during routine ROP screening from 2011 to 2015, and a reference standard diagnosis was established for each image. A secondary analysis was performed using the i-ROP deep learning system to assign a vascular severity score (VSS) to each image, ranging from 1 to 9, with 9 being the most severe disease. This score has been previously demonstrated to correlate with the International Classification of ROP. Mean plus disease severity was calculated by averaging 14 labels per image in serial and single images to decrease noise. MAIN OUTCOME MEASURES: Grading severity of ROP as defined by plus, preplus, or no ROP. RESULTS: Assessment of serial retinal images changed the grading severity for > 50% of the graders, although there was wide variability. Cohen’s kappa ranged from 0.29 to 1.0, which showed a wide range of agreement from slight to perfect by each grader. Changes in the grading of serial retinal images were noted more commonly in cases of preplus disease. The mean severity in cases with a diagnosis of plus disease and no disease did not change between single and serial images. The ROP VSS demonstrated good correlation with the range of expert classifications of plus disease and overall agreement with the mode class (P = 0.001). The VSS correlated with mean plus disease severity by expert diagnosis (correlation coefficient, 0.89). The more aggressive graders tended to be influenced by serial images to increase the severity of their grading. The VSS also demonstrated agreement with disease progression across serial images, which progressed to preplus and plus disease. CONCLUSIONS: Clinicians demonstrated variability in ROP diagnosis when presented with both single and serial images. The use of deep learning as a quantitative assessment of plus disease has the potential to standardize ROP diagnosis and treatment.
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- 2022
5. Federated Learning for Multicenter Collaboration in Ophthalmology
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Adam Hanif, Charles Lu, Ken Chang, Praveer Singh, Aaron S. Coyner, James M. Brown, Susan Ostmo, Robison V. Paul Chan, Daniel Rubin, Michael F. Chiang, Jayashree Kalpathy-Cramer, John Peter Campbell, Sang Jin Kim, Kemal Sonmez, Robert Schelonka, Aaron Coyner, R.V. Paul Chan, Karyn Jonas, Bhavana Kolli, Jason Horowitz, Osode Coki, Cheryl-Ann Eccles, Leora Sarna, Anton Orlin, Audina Berrocal, Catherin Negron, Kimberly Denser, Kristi Cumming, Tammy Osentoski, Tammy Check, Mary Zajechowski, Thomas Lee, Aaron Nagiel, Evan Kruger, Kathryn McGovern, Dilshad Contractor, Margaret Havunjian, Charles Simmons, Raghu Murthy, Sharon Galvis, Jerome Rotter, Ida Chen, Xiaohui Li, Kent Taylor, Kaye Roll, Mary Elizabeth Hartnett, Leah Owen, Darius Moshfeghi, Mariana Nunez, Zac Wennber-Smith, Deniz Erdogmus, Stratis Ioannidis, Maria Ana Martinez-Castellanos, Samantha Salinas-Longoria, Rafael Romero, Andrea Arriola, Francisco Olguin-Manriquez, Miroslava Meraz-Gutierrez, Carlos M. Dulanto-Reinoso, and Cristina Montero-Mendoza
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Ophthalmology - Published
- 2022
6. Evaluation of a Deep Learning-Derived Quantitative Retinopathy of Prematurity Severity Scale
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J. Peter Campbell, Sang Jin Kim, James M. Brown, Susan Ostmo, R. V. Paul Chan, Jayashree Kalpathy-Cramer, Michael F. Chiang, Kemal Sonmez, Robert Schelonka, R.V. Paul Chan, Karyn Jonas, Jason Horowitz, Osode Coki, Cheryl-Ann Eccles, Leora Sarna, Anton Orlin, Audina Berrocal, Catherin Negron, Kimberly Denser, Kristi Cumming, Tammy Osentoski, Tammy Check, Mary Zajechowski, Thomas Lee, Aaron Nagiel, Evan Kruger, Kathryn McGovern, Charles Simmons, Raghu Murthy, Sharon Galvis, null Jerome Rotter MD, Ida Chen, Xiaohui Li, Kent Taylor, Kaye Roll, Deniz Erdogmus, Stratis Ioannidis, Maria Ana Martinez-Castellanos, Samantha Salinas-Longoria, Rafael Romero, Andrea Arriola, Francisco Olguin-Manriquez, Miroslava Meraz-Gutierrez, Carlos M. Dulanto-Reinoso, and Cristina Montero-Mendoza
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G740 Computer Vision ,medicine.medical_specialty ,Design analysis ,Posterior pole ,Gestational Age ,G700 Artificial Intelligence ,Severity of Illness Index ,Correlation ,symbols.namesake ,Deep Learning ,Bayesian multivariate linear regression ,medicine ,Humans ,Retinopathy of Prematurity ,Retrospective Studies ,business.industry ,Deep learning ,Infant, Newborn ,Retinal Vessels ,Retinopathy of prematurity ,medicine.disease ,Pearson product-moment correlation coefficient ,Ophthalmoscopy ,Ophthalmology ,Informatics ,symbols ,B500 Ophthalmics ,Radiology ,Artificial intelligence ,G760 Machine Learning ,business ,Algorithms ,Follow-Up Studies - Abstract
Purpose To evaluate the clinical usefulness of a quantitative deep learning-derived vascular severity score for retinopathy of prematurity (ROP) by assessing its correlation with clinical ROP diagnosis and by measuring clinician agreement in applying a novel scale. Design Analysis of existing database of posterior pole fundus images and corresponding ophthalmoscopic examinations using 2 methods of assigning a quantitative scale to vascular severity. Participants Images were from clinical examinations of patients in the Imaging and Informatics in ROP Consortium. Four ophthalmologists and 1 study coordinator evaluated vascular severity on a scale from 1 to 9. Methods A quantitative vascular severity score (1–9) was applied to each image using a deep learning algorithm. A database of 499 images was developed for assessment of interobserver agreement. Main Outcome Measures Distribution of deep learning-derived vascular severity scores with the clinical assessment of zone (I, II, or III), stage (0, 1, 2, or 3), and extent (6 clock hours) of stage 3 evaluated using multivariate linear regression and weighted κ values and Pearson correlation coefficients for interobserver agreement on a 1-to-9 vascular severity scale. Results For deep learning analysis, a total of 6344 clinical examinations were analyzed. A higher deep learning-derived vascular severity score was associated with more posterior disease, higher disease stage, and higher extent of stage 3 disease (P < 0.001 for all). For a given ROP stage, the vascular severity score was higher in zone I than zones II or III (P < 0.001). Multivariate regression found zone, stage, and extent all were associated independently with the severity score (P < 0.001 for all). For interobserver agreement, the mean ± standard deviation weighted κ value was 0.67 ± 0.06, and the Pearson correlation coefficient ± standard deviation was 0.88 ± 0.04 on the use of a 1-to-9 vascular severity scale. Conclusions A vascular severity scale for ROP seems feasible for clinical adoption; corresponds with zone, stage, extent of stage 3, and plus disease; and facilitates the use of objective technology such as deep learning to improve the consistency of ROP diagnosis.
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- 2020
7. Variability in Plus Disease Identified Using a Deep Learning-Based Retinopathy of Prematurity Severity Scale
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Rene Y. Choi, James M. Brown, Jayashree Kalpathy-Cramer, R. V. Paul Chan, Susan Ostmo, Michael F. Chiang, J. Peter Campbell, Sang Jin Kim, Kemal Sonmez, Karyn Jonas, Jason Horowitz, Osode Coki, Cheryl-Ann Eccles, Leora Sarna, Anton Orlin, Audina Berrocal, Catherin Negron, Kimberly Denser, Kristi Cumming, Tammy Osentoski, Tammy Check, Mary Zajechowski, Thomas Lee, Evan Kruger, Kathryn McGovern, Charles Simmons, Raghu Murthy, Sharon Galvis, Jerome Rotter, Ida Chen, Xiaohui Li, Kent Taylor, Kaye Roll, Deniz Erdogmus, Stratis Ioannidis, Maria Ana Martinez-Castellanos, Samantha Salinas-Longoria, Rafael Romero, Andrea Arriola, Francisco Olguin-Manriquez, Miroslava Meraz-Gutierrez, Carlos M. Dulanto-Reinoso, and Cristina Montero-Mendoza
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Male ,Pediatrics ,medicine.medical_specialty ,MEDLINE ,Gestational Age ,Disease ,Severity of Illness Index ,03 medical and health sciences ,0302 clinical medicine ,Deep Learning ,Interquartile range ,Ophthalmology ,medicine ,Humans ,Retinopathy of Prematurity ,030304 developmental biology ,Retrospective Studies ,0303 health sciences ,business.industry ,Childhood blindness ,Infant, Newborn ,Retrospective cohort study ,Retinopathy of prematurity ,medicine.disease ,Clinical trial ,Informatics ,030221 ophthalmology & optometry ,Female ,business ,Algorithms - Abstract
Purpose Retinopathy of prematurity is a leading cause of childhood blindness worldwide, but clinical diagnosis is subjective, which leads to treatment differences. Our goal was to determine objective differences in the diagnosis of plus disease between clinicians using an automated retinopathy of prematurity (ROP) vascular severity score. Design This retrospective cohort study used data from the Imaging and Informatics in ROP Consortium, which comprises 8 tertiary care centers in North America. Fundus photographs of all infants undergoing ROP screening examinations between July 1, 2011, and December 31, 2016, were obtained. Participants Infants meeting ROP screening criteria who were diagnosed with plus disease and treatment initiated by an examining physician based on ophthalmoscopic examination results. Methods An ROP severity score (1–9) was generated for each image using a deep learning (DL) algorithm. Main Outcome Measures The mean, median, and range of ROP vascular severity scores overall and for each examiner when the diagnosis of plus disease was made. Results A total of 5255 clinical examinations in 871 babies were analyzed. Of these, 168 eyes were diagnosed with plus disease by 11 different examiners and were included in the study. The mean ± standard deviation vascular severity score for patients diagnosed with plus disease was 7.4 ± 1.9, median was 8.5 (interquartile range, 5.8–8.9), and range was 1.1 to 9.0. Within some examiners, variability in the level of vascular severity diagnosed as plus disease was present, and 1 examiner routinely diagnosed plus disease in patients with less severe disease than the other examiners (P Conclusions We observed variability both between and within examiners in the diagnosis of plus disease using DL. Prospective evaluation of clinical trial data using an objective measurement of vascular severity may help to define better the minimum necessary level of vascular severity for the diagnosis of plus disease or how other clinical features such as zone, stage, and extent of peripheral disease ought to be incorporated in treatment decisions.
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- 2020
8. Bilateral central retinal artery occlusion: An exceptional complication after frontal parasagittal meningioma resection
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Nelida Aliaga, Jafeth Lizana, Walter Marani, Nicola Montemurro, and Carlos M Dulanto Reinoso
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Parasagittal Meningioma ,medicine.medical_specialty ,Retinal Artery Occlusion ,business.industry ,Postoperative complication ,Postoperative management ,medicine.disease ,Case report ,Ophthalmic artery ,Retinal artery occlusion ,Surgical complication ,Resection ,Surgery ,High morbidity ,medicine.artery ,medicine ,Central retinal artery occlusion ,Neurology (clinical) ,Complication ,business - Abstract
Background: Central retinal artery occlusion (CRAO) is a rare acute disease associated with great morbidity. It is reported as a complication of surgical procedures, but rarely associated with brain surgery and no reports before due to parasagittal meningioma resection. Case Description: We present the case of a 41-year-old female who underwent surgery for a parasagittal meningioma and developed a bilateral CRAO as an acute postoperative complication. Most common causes, such as cardiac embolism, carotid pathology and coagulation problems, were discussed and all clinical and neuroradiological exams performed were reported. Conclusion: Bilateral CRAO as results of brain surgery is extremely rare; however, if it occurs, it should be early recognized and treated to minimize its high morbidity.
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- 2021
9. Assessment of a Tele-education System to Enhance Retinopathy of Prematurity Training by International Ophthalmologists-in-Training in Mexico
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Samir N. Patel, Maria Ana Martinez-Castellanos, David Berrones-Medina, Ryan Swan, Michael C. Ryan, Karyn E. Jonas, Susan Ostmo, J. Peter Campbell, Michael F. Chiang, R.V. Paul Chan, Vivien Yap, Alexander D. Port, Leslie D. Mackeen, Samantha Salinas-Longoria, Rafael Romero, Andrea Arriola, Wei-Chi Wu, Rachelle Go Ang Sam Anzures, Camila V. Ventura, Kemal Sonmez, Sang Jin Kim, Karyn Jonas, Anton Orlin, Jason Horowitz, Osode Coki, Cheryl-Ann Eccles, Leora Sarna, Audina Berrocal, Catherin Negron, Kimberly Denser, Kristi Cumming, Tammy Osentoski, Tammy Check, Mary Zajechowski, Thomas Lee, Evan Kruger, Kathryn McGovern, Charles Simmons, Raghu Murthy, Sharon Galvis, Jerome Rotter, Ida Chen, Xiaohui Li, Kent Taylor, Kaye Roll, Jayashree Kalpathy-Cramer, Deniz Erdogmus, Stratis Ionnidis, Francisco Olguin-Manriquez, Miroslava Meraz-Gutierrez, Carlos M. Dulanto-Reinoso, and Cristina Montero-Mendoza
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congenital, hereditary, and neonatal diseases and abnormalities ,Pediatrics ,medicine.medical_specialty ,Telemedicine ,genetic structures ,education ,MEDLINE ,Article ,law.invention ,03 medical and health sciences ,0302 clinical medicine ,Cohen's kappa ,Randomized controlled trial ,law ,medicine ,Humans ,Retinopathy of Prematurity ,Prospective Studies ,030212 general & internal medicine ,Prospective cohort study ,Mexico ,Internet ,Ophthalmologists ,business.industry ,Reproducibility of Results ,Retinopathy of prematurity ,medicine.disease ,eye diseases ,Ophthalmology ,Education, Medical, Graduate ,030221 ophthalmology & optometry ,Physical therapy ,sense organs ,Clinical Competence ,business ,Tele education ,Follow-Up Studies ,Cohort study - Abstract
PURPOSE: To evaluate a tele-education system developed to improve diagnostic competency in retinopathy of prematurity (ROP) by ophthalmologists-in-training in Mexico. DESIGN: Prospective, randomized cohort study. PARTICIPANTS: Fifty-eight ophthalmology residents and fellows from a training program in Mexico consented to participate. Twenty-nine of 58 trainees (50%) were randomized to the educational intervention (pretest, ROP tutorial, ROP educational chapters, and posttest), and 29 of 58 trainees (50%) were randomized to a control group (pretest and posttest only). METHODS: A secure web-based educational system was created using clinical cases (20 pretest, 20 posttest, and 25 training chapter-based) developed from a repository of over 2500 unique image sets of ROP. For each image set used, a reference standard ROP diagnosis was established by combining the clinical diagnosis by indirect ophthalmoscope examination and image-based diagnosis by multiple experts. Trainees were presented with image-based clinical cases of ROP during a pretest, posttest, and training chapters. MAIN OUTCOME MEASURES: The accuracy of ROP diagnosis (e.g., plus disease, zone, stage, category) was determined using sensitivity and specificity calculations from the pretest and posttest results of the educational intervention group versus control group. The unweighted kappa statistic was used to analyze the intragrader agreement for ROP diagnosis by the ophthalmologists-in-training during the pretest and posttest for both groups. RESULTS: Trainees completing the tele-education system had statistically significant improvements (P < 0.01) in the accuracy of ROP diagnosis for plus disease, zone, stage, category, and aggressive posterior ROP (AP-ROP). Compared with the control group, trainees who completed the ROP tele-education system performed better on the posttest for accurately diagnosing plus disease (67% vs. 48%; P = 0.04) and the presence of ROP (96% vs. 91%; P < 0.01). The specificity for diagnosing AP-ROP (94% vs. 78%; P < 0.01), type 2 ROP or worse (92% vs. 84%; P = 0.04), and ROP requiring treatment (89% vs. 79%; P < 0.01) was better for the trainees completing the tele-education system compared with the control group. Intragrader agreement improved for identification of plus disease, zone, stage, and category of ROP after completion of the educational intervention. CONCLUSIONS: A tele-education system for ROP education was effective in improving the diagnostic accuracy of ROP by ophthalmologists-in-training in Mexico. This system has the potential to increase competency in ROP diagnosis and management for ophthalmologists-in-training from middle-income nations.
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- 2017
10. Plus Disease in Retinopathy of Prematurity
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J. Peter Campbell, Jayashree Kalpathy-Cramer, Deniz Erdogmus, Peng Tian, Dharanish Kedarisetti, Chace Moleta, James D. Reynolds, Kelly Hutcheson, Michael J. Shapiro, Michael X. Repka, Philip Ferrone, Kimberly Drenser, Jason Horowitz, Kemal Sonmez, Ryan Swan, Susan Ostmo, Karyn E. Jonas, R.V. Paul Chan, Michael F. Chiang, Karyn Jonas, Osode Coki, Cheryl-Ann Eccles, Leora Sarna, Audina Berrocal, Catherin Negron, Kimberly Denser, Kristi Cumming, Tammy Osentoski, Tammy Check, Mary Zajechowski, Thomas Lee, Evan Kruger, Kathryn McGovern, Charles Simmons, Raghu Murthy, Sharon Galvis, Jerome Rotter, Ida Chen, Xiaohui Li, Kent Taylor, Kaye Roll, Maria Ana Martinez-Castellanos, Samantha Salinas-Longoria, Rafael Romero, Andrea Arriola, Francisco Olguin-Manriquez, Miroslava Meraz-Gutierrez, Carlos M. Dulanto-Reinoso, and Cristina Montero-Mendoza
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0301 basic medicine ,Pediatrics ,Pathology ,medicine.medical_specialty ,Correlation coefficient ,Physical examination ,Correlation ,03 medical and health sciences ,0302 clinical medicine ,Disease severity ,Intensive care ,Statistics ,Severity of illness ,medicine ,Statistic ,medicine.diagnostic_test ,business.industry ,Retinopathy of prematurity ,medicine.disease ,Plus disease ,Ophthalmology ,030104 developmental biology ,VASCULAR ABNORMALITY ,Ranking ,Informatics ,030221 ophthalmology & optometry ,Pairwise comparison ,business ,030217 neurology & neurosurgery - Abstract
Purpose To determine expert agreement on relative retinopathy of prematurity (ROP) disease severity and whether computer-based image analysis can model relative disease severity, and to propose consideration of a more continuous severity score for ROP. Design We developed 2 databases of clinical images of varying disease severity (100 images and 34 images) as part of the Imaging and Informatics in ROP (i-ROP) cohort study and recruited expert physician, nonexpert physician, and nonphysician graders to classify and perform pairwise comparisons on both databases. Participants Six participating expert ROP clinician-scientists, each with a minimum of 10 years of clinical ROP experience and 5 ROP publications, and 5 image graders (3 physicians and 2 nonphysician graders) who analyzed images that were obtained during routine ROP screening in neonatal intensive care units. Methods Images in both databases were ranked by average disease classification (classification ranking), by pairwise comparison using the Elo rating method (comparison ranking), and by correlation with the i-ROP computer-based image analysis system. Main Outcome Measures Interexpert agreement (weighted κ statistic) compared with the correlation coefficient (CC) between experts on pairwise comparisons and correlation between expert rankings and computer-based image analysis modeling. Results There was variable interexpert agreement on diagnostic classification of disease (plus, preplus, or normal) among the 6 experts (mean weighted κ, 0.27; range, 0.06–0.63), but good correlation between experts on comparison ranking of disease severity (mean CC, 0.84; range, 0.74–0.93) on the set of 34 images. Comparison ranking provided a severity ranking that was in good agreement with ranking obtained by classification ranking (CC, 0.92). Comparison ranking on the larger dataset by both expert and nonexpert graders demonstrated good correlation (mean CC, 0.97; range, 0.95–0.98). The i-ROP system was able to model this continuous severity with good correlation (CC, 0.86). Conclusions Experts diagnose plus disease on a continuum, with poor absolute agreement on classification but good relative agreement on disease severity. These results suggest that the use of pairwise rankings and a continuous severity score, such as that provided by the i-ROP system, may improve agreement on disease severity in the future.
- Published
- 2016
11. Automated Fundus Image Quality Assessment in Retinopathy of Prematurity Using Deep Convolutional Neural Networks
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Aaron S. Coyner, Ryan Swan, J. Peter Campbell, Susan Ostmo, James M. Brown, Jayashree Kalpathy-Cramer, Sang Jin Kim, Karyn E. Jonas, R.V. Paul Chan, Michael F. Chiang, Kemal Sonmez, R. V. Paul Chan, Karyn Jonas, Jason Horowitz, Osode Coki, Cheryl-Ann Eccles, Leora Sarna, Anton Orlin, Audina Berrocal, Catherin Negron, Kimberly Denser, Kristi Cumming, Tammy Osentoski, Tammy Check, Mary Zajechowski, Thomas Lee, Evan Kruger, Kathryn McGovern, Charles Simmons, Raghu Murthy, Sharon Galvis, Jerome Rotter, Ida Chen, Xiaohui Li, Kent Taylor, Kaye Roll, Ken Chang, Andrew Beers, Deniz Erdogmus, Stratis Ioannidis, Maria Ana Martinez-Castellanos, Samantha Salinas-Longoria, Rafael Romero, Andrea Arriola, Francisco Olguin-Manriquez, Miroslava Meraz-Gutierrez, Carlos M. Dulanto-Reinoso, and Cristina Montero-Mendoza
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Male ,medicine.medical_specialty ,Convolutional neural network ,Article ,Ophthalmology ,medicine ,Image Processing, Computer-Assisted ,Humans ,Generalizability theory ,Retinopathy of Prematurity ,Medical diagnosis ,Rank correlation ,Receiver operating characteristic ,business.industry ,Infant, Newborn ,Pattern recognition ,Ophthalmoscopy ,Ranking ,ROC Curve ,Test set ,Pairwise comparison ,Female ,Artificial intelligence ,Neural Networks, Computer ,business ,Algorithms - Abstract
Purpose Accurate image-based ophthalmic diagnosis relies on fundus image clarity. This has important implications for the quality of ophthalmic diagnoses and for emerging methods such as telemedicine and computer-based image analysis. The purpose of this study was to implement a deep convolutional neural network (CNN) for automated assessment of fundus image quality in retinopathy of prematurity (ROP). Design Experimental study. Participants Retinal fundus images were collected from preterm infants during routine ROP screenings. Methods Six thousand one hundred thirty-nine retinal fundus images were collected from 9 academic institutions. Each image was graded for quality (acceptable quality [AQ], possibly acceptable quality [PAQ], or not acceptable quality [NAQ]) by 3 independent experts. Quality was defined as the ability to assess an image confidently for the presence of ROP. Of the 6139 images, NAQ, PAQ, and AQ images represented 5.6%, 43.6%, and 50.8% of the image set, respectively. Because of low representation of NAQ images in the data set, images labeled NAQ were grouped into the PAQ category, and a binary CNN classifier was trained using 5-fold cross-validation on 4000 images. A test set of 2109 images was held out for final model evaluation. Additionally, 30 images were ranked from worst to best quality by 6 experts via pairwise comparisons, and the CNN’s ability to rank quality, regardless of quality classification, was assessed. Main Outcome Measures The CNN performance was evaluated using area under the receiver operating characteristic curve (AUC). A Spearman’s rank correlation was calculated to evaluate the overall ability of the CNN to rank images from worst to best quality as compared with experts. Results The mean AUC for 5-fold cross-validation was 0.958 (standard deviation, 0.005) for the diagnosis of AQ versus PAQ images. The AUC was 0.965 for the test set. The Spearman’s rank correlation coefficient on the set of 30 images was 0.90 as compared with the overall expert consensus ranking. Conclusions This model accurately assessed retinal fundus image quality in a comparable manner with that of experts. This fully automated model has potential for application in clinical settings, telemedicine, and computer-based image analysis in ROP and for generalizability to other ophthalmic diseases.
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- 2019
12. Telemedical Diagnosis of Stage 4 and Stage 5 Retinopathy of Prematurity
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Samir N. Patel, Ranjodh Singh, Karyn E. Jonas, Susan Ostmo, Mrinali P. Gupta, J. Peter Campbell, Michael F. Chiang, R.V. Paul Chan, Kemal Sonmez, Karyn Jonas, Jason Horowitz, Osode Coki, Cheryl-Ann Eccles, Leora Sarna, Anton Orlin, Audina Berrocal, Catherin Negron, Kimberly Drenser, Kristi Cumming, Tammy Osentoski, Tammy Check, Mary Zajechowski, Thomas Lee, Evan Kruger, Kathryn McGovern, Charles Simmons, Raghu Murthy, Sharon Galvis, Jerome Rotter, Ida Chen, Xiaohui Li, Kent Taylor, Kaye Roll, Jayashree Kalpathy-Cramer, Deniz Erdogmus, Stratis Ioannidis, Maria Ana Martinez-Castellanos, Samantha Salinas-Longoria, Rafael Romero, Andrea Arriola, Francisco Olguin-Manriquez, Miroslava Meraz-Gutierrez, Carlos M. Dulanto-Reinoso, and Cristina Montero-Mendoza
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medicine.medical_specialty ,Gestational Age ,Severity of Illness Index ,Retina ,03 medical and health sciences ,0302 clinical medicine ,030225 pediatrics ,Ophthalmology ,Severity of illness ,Image Interpretation, Computer-Assisted ,Medicine ,Humans ,Retinopathy of Prematurity ,Prospective Studies ,Stage (cooking) ,Prospective cohort study ,Medical systems ,business.industry ,Infant, Newborn ,Gestational age ,Reproducibility of Results ,Retinopathy of prematurity ,medicine.disease ,eye diseases ,Indirect ophthalmoscopy ,Telemedicine ,Ophthalmoscopy ,ROC Curve ,Clinical diagnosis ,030221 ophthalmology & optometry ,business ,Infant, Premature ,Follow-Up Studies - Abstract
Purpose To determine the accuracy of image-based diagnosis for stage 4 or worse retinopathy of prematurity (ROP) disease. Design Prospective cohort study. Participants We prospectively obtained data, from 8 major ROP centers, for 1220 eye examinations from 230 infants. Methods An ophthalmologist at each center provided a clinical diagnosis using indirect ophthalmoscopy. Wide-angle retinal images (RetCam; Clarity Medical Systems, Pleasanton, CA) were then obtained, and these were independently read by 2 ROP experts using a web-based system for an image-based diagnosis. Main Outcome Measures Sensitivity and specificity of image-based diagnosis from the ROP experts were calculated using the clinical diagnosis as the reference standard. Results Of 1220 examinations, 28 (2%) had a clinical diagnosis of stage 4 or worse. Sensitivity and specificity for stage 4 or worse disease were 75% and 99% for expert 1, and 86% and 99% for expert 2. Sensitivity and specificity for the detection of stage 5 disease were 69% and 99% for both experts. Conclusions There are inconsistencies in the accuracy of image-based diagnosis of stage 4 and stage 5 ROP when compared with the clinical diagnosis.
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- 2016
13. Plus Disease in Retinopathy of Prematurity: Diagnostic Trends in 2016 Versus 2007
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Chace Moleta, J. Peter Campbell, Jayashree Kalpathy-Cramer, R.V. Paul Chan, Susan Ostmo, Karyn Jonas, Michael F. Chiang, Kemal Sonmez, Jason Horowitz, Osode Coki, Cheryl-Ann Eccles, Leora Sarna, Audina Berrocal, Catherin Negron, Kimberly Denser, Kristi Cumming, Tammy Osentoski, Tammy Check, Mary Zajechowski, Thomas Lee, Evan Kruger, Kathryn McGovern, Charles Simmons, Raghu Murthy, Sharon Galvis, Jerome Rotter, Ida Chen, Xiaohui Li, Kent Taylor, Kaye Roll, Deniz Erdogmus, Stratis Ioannidis, Maria Ana Martinez-Castellanos, Samantha Salinas-Longoria, Rafael Romero, Andrea Arriola, Francisco Olguin-Manriquez, Miroslava Meraz-Gutierrez, Carlos M. Dulanto-Reinoso, and Cristina Montero-Mendoza
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Male ,Pathology ,medicine.medical_specialty ,Pediatrics ,Video Recording ,Diagnostic Techniques, Ophthalmological ,Patient care ,Article ,Correlation ,03 medical and health sciences ,0302 clinical medicine ,Disease severity ,030225 pediatrics ,Image Processing, Computer-Assisted ,Photography ,Medicine ,Humans ,Retinopathy of Prematurity ,Prospective Studies ,Prospective cohort study ,business.industry ,Infant, Newborn ,Reproducibility of Results ,Retinopathy of prematurity ,medicine.disease ,Infant newborn ,Retinal Vein ,Plus disease ,Ophthalmology ,Cohort ,030221 ophthalmology & optometry ,Female ,business ,Dilatation, Pathologic ,Forecasting - Abstract
To identify any temporal trends in the diagnosis of plus disease in retinopathy of prematurity (ROP) by experts.Reliability analysis.ROP experts were recruited in 2007 and 2016 to classify 34 wide-field fundus images of ROP as plus, pre-plus, or normal, coded as "3," "2," and "1," respectively, in the database. The main outcome was the average calculated score for each image in each cohort. Secondary outcomes included correlation on the relative ordering of the images in 2016 vs 2007, interexpert agreement, and intraexpert agreement.The average score for each image was higher for 30 of 34 (88%) images in 2016 compared with 2007, influenced by fewer images classified as normal (P.01), a similar number of pre-plus (P = .52), and more classified as plus (P.01). The mean weighted kappa values in 2006 were 0.36 (range 0.21-0.60), compared with 0.22 (range 0-0.40) in 2016. There was good correlation between rankings of disease severity between the 2 cohorts (Spearman rank correlation ρ = 0.94), indicating near-perfect agreement on relative disease severity.Despite good agreement between cohorts on relative disease severity ranking, the higher average score and classifications for each image demonstrate that experts are diagnosing pre-plus and plus disease at earlier stages of disease severity in 2016, compared with 2007. This has implications for patient care, research, and teaching, and additional studies are needed to better understand this temporal trend in image-based plus disease diagnosis.
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- 2016
14. Squamous neoplasia of the ocular surface in patients with pterygium in Peru
- Author
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José A. Roca, Luis Furuya-Kanamori, Carlos M. Dulanto-Reinoso, Victor Ch. Dulanto-Reinoso, Graham A Lee, Jennifer C. Stone, Francisco Ganga Contreras, and Lelia Marroquín
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medicine.medical_specialty ,Diagnóstico clínico ,lcsh:Medicine ,Eye neoplasms ,Pterygium ,Likelihood ratios in diagnostic testing ,Eye neoplasm ,Ophthalmology ,Carcinoma ,neoplasias oculares ,Medicine ,In patient ,lcsh:R5-920 ,business.industry ,Pterigion ,Neoplasias oculares ,lcsh:R ,Public Health, Environmental and Occupational Health ,General Medicine ,medicine.disease ,Dermatology ,Clinical diagnosis ,pterigion ,diagnóstico clínico ,Histopathology ,lcsh:Medicine (General) ,business ,Ocular surface - Abstract
Objetivos. Estimar la frecuencia de neoplasia escamosa de la superficie ocular (NESO) no sospechada en pterigión, la precisión del diagnóstico clínico y las características demográficas y clínicas asociadas. Materiales y métodos. Se examinaron los informes histopatológicos de los pacientes con diagnóstico clínico de pterigión y/o NESO que fueron quirúrgicamente tratados entre marzo de 2009 y diciembre de 2012 en el Instituto Nacional de Oftalmología en Lima, Perú. La precisión del diagnóstico clínico para identificar la NESO se evaluó mediante la sensibilidad, especificidad y los cocientes de probabilidad. Se realizaron modelos de regresión log-log negativos para identificar las características demográficas y clínicas asociadas con un aumento de las probabilidades de diagnosticar NESO. Resultados. Se examinaron 3021 informes de histopatología. La frecuencia de NESO no sospechada en pterigión fue de 0,65%. El diagnóstico clínico presentó una sensibilidad del 85%, una especificidad del 99%, un cociente de probabilidad positiva de 111,89 y un cociente probabilidad negativa de 0,15. Las características asociadas fueron el sexo masculino (OR 1,15; IC 95%:1,01-1,30), pacientes de 61 a 80 años (OR 1,54; IC 95%: 1,28-1,85), ≥ de 81 años (OR 3,10; IC 95%: 2,09-4,58), pacientes con lesiones recurrentes (OR 1,59; IC 95%: 1,03-2,46) y lesiones en el lado temporal (OR 3,57; IC 95%: 2,63-4,85) presentaron mayor probabilidad de NESO. Conclusiones. Se encontró una baja frecuencia de NESO no sospechada, sin embargo, es recomendable realizar el estudio histopatológico de forma rutinaria para evitar diagnósticos erróneos de NESO como pterigión. Objectives. To estimate the frequency of unsuspected ocular surface squamous neoplasia (OSSN) in pterygium, the accuracy of clinical diagnosis, and associated demographic and clinical characteristics. Materials and methods. We reviewed histopathological reports of patients with a clinical diagnosis of pterygium and/or OSSN who were surgically treated between March 2009 and December 2012 at the National Eye Institute in Lima, Peru. The accuracy of the clinical diagnosis of OSSN was assessed by sensitivity, specificity, and likelihood ratios. Models of negative log-log regression were performed to identify demographic and clinical characteristics associated with increased odds of diagnosing OSSN. Results. 3,021 histopathological reports were reviewed. The frequency of unsuspected OSSN in pterygium was 0.65%. Clinical diagnosis had a sensitivity of 85%, a specificity of 99%, a positive likelihood ratio of 111.89, and a negative likelihood ratio of 0.15. Associated characteristics were male gender (OR =1.15; 95% CI: 1.01 to 1.30), age group of 61-80 years (OR = 1.54, 95% CI: 1.28 to 1.85) ≥ 81 years (OR = 3.10; 95% CI: 2.09 to 4.58), presence of recurrent lesions (OR = 1.59; 95% CI: 1.03 to 2.46) and temporal location lesions (OR = 3.57; 95% CI: 2.63 to 4.85). These characteristics were associated with a greater likelihood of OSSN. Conclusions. A low frequency of unsuspected OSSN was found; however, it is recommended to routinely perform histopathology studies to avoid misdiagnosis of OSSN as pterygium.
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- 2014
15. Neoplasia escamosa de la superficie ocular en pacientes con pterigión en Perú
- Author
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Luis Furuya-Kanamori, Carlos M Dulanto-Reinoso, Jennifer C Stone, Lelia Marroquín, Victor Ch Dulanto-Reinoso, José A Roca, Francisco Contreras, and Graham A Lee
- Subjects
pterigion ,neoplasias oculares ,diagnóstico clínico ,Medicine ,Medicine (General) ,R5-920 - Abstract
Objetivos. Estimar la frecuencia de neoplasia escamosa de la superficie ocular (NESO) no sospechada en pterigión, la precisión del diagnóstico clínico y las características demográficas y clínicas asociadas. Materiales y métodos. Se examinaron los informes histopatológicos de los pacientes con diagnóstico clínico de pterigión y/o NESO que fueron quirúrgicamente tratados entre marzo de 2009 y diciembre de 2012 en el Instituto Nacional de Oftalmología en Lima, Perú. La precisión del diagnóstico clínico para identificar la NESO se evaluó mediante la sensibilidad, especificidad y los cocientes de probabilidad. Se realizaron modelos de regresión log-log negativos para identificar las características demográficas y clínicas asociadas con un aumento de las probabilidades de diagnosticar NESO. Resultados. Se examinaron 3021 informes de histopatología. La frecuencia de NESO no sospechada en pterigión fue de 0,65%. El diagnóstico clínico presentó una sensibilidad del 85%, una especificidad del 99%, un cociente de probabilidad positiva de 111,89 y un cociente probabilidad negativa de 0,15. Las características asociadas fueron el sexo masculino (OR 1,15; IC 95%:1,01-1,30), pacientes de 61 a 80 años (OR 1,54; IC 95%: 1,28-1,85), ≥ de 81 años (OR 3,10; IC 95%: 2,09-4,58), pacientes con lesiones recurrentes (OR 1,59; IC 95%: 1,03-2,46) y lesiones en el lado temporal (OR 3,57; IC 95%: 2,63-4,85) presentaron mayor probabilidad de NESO. Conclusiones. Se encontró una baja frecuencia de NESO no sospechada, sin embargo, es recomendable realizar el estudio histopatológico de forma rutinaria para evitar diagnósticos erróneos de NESO como pterigión.
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