Publications by authors named "Jimmy S Chen"

6 Publications

  • Page 1 of 1

Single-Examination Risk Prediction of Severe Retinopathy of Prematurity.

Pediatrics 2021 Nov 23. Epub 2021 Nov 23.

Departments of Ophthalmology.

Background And Objectives: Retinopathy of prematurity (ROP) is a leading cause of childhood blindness. Screening and treatment reduces this risk, but requires multiple examinations of infants, most of whom will not develop severe disease. Previous work has suggested that artificial intelligence may be able to detect incident severe disease (treatment-requiring retinopathy of prematurity [TR-ROP]) before clinical diagnosis. We aimed to build a risk model that combined artificial intelligence with clinical demographics to reduce the number of examinations without missing cases of TR-ROP.

Methods: Infants undergoing routine ROP screening examinations (1579 total eyes, 190 with TR-ROP) were recruited from 8 North American study centers. A vascular severity score (VSS) was derived from retinal fundus images obtained at 32 to 33 weeks' postmenstrual age. Seven ElasticNet logistic regression models were trained on all combinations of birth weight, gestational age, and VSS. The area under the precision-recall curve was used to identify the highest-performing model.

Results: The gestational age + VSS model had the highest performance (mean ± SD area under the precision-recall curve: 0.35 ± 0.11). On 2 different test data sets (n = 444 and n = 132), sensitivity was 100% (positive predictive value: 28.1% and 22.6%) and specificity was 48.9% and 80.8% (negative predictive value: 100.0%).

Conclusions: Using a single examination, this model identified all infants who developed TR-ROP, on average, >1 month before diagnosis with moderate to high specificity. This approach could lead to earlier identification of incident severe ROP, reducing late diagnosis and treatment while simultaneously reducing the number of ROP examinations and unnecessary physiologic stress for low-risk infants.
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http://dx.doi.org/10.1542/peds.2021-051772DOI Listing
November 2021

Electronic health record note review in an outpatient specialty clinic: who is looking?

JAMIA Open 2021 Jul 31;4(3):ooab044. Epub 2021 Jul 31.

National Eye Institute, National Institutes of Health, Bethesda, Maryland, USA.

Note entry and review in electronic health records (EHRs) are time-consuming. While some clinics have adopted team-based models of note entry, how these models have impacted note review is unknown in outpatient specialty clinics such as ophthalmology. We hypothesized that ophthalmologists and ancillary staff review very few notes. Using audit log data from 9775 follow-up office visits in an academic ophthalmology clinic, we found ophthalmologists reviewed a median of 1 note per visit (2.6 ± 5.3% of available notes), while ancillary staff reviewed a median of 2 notes per visit (4.1 ± 6.2% of available notes). While prior ophthalmic office visit notes were the most frequently reviewed note type, ophthalmologists and staff reviewed no such notes in 51% and 31% of visits, respectively. These results highlight the collaborative nature of note review and raise concerns about how cumbersome EHR designs affect efficient note review and the utility of prior notes in ophthalmic clinical care.
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http://dx.doi.org/10.1093/jamiaopen/ooab044DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC8325486PMC
July 2021

A comparative analysis of system features used in the TREC-COVID information retrieval challenge.

J Biomed Inform 2021 05 6;117:103745. Epub 2021 Apr 6.

Department of Medical Informatics and Clinical Epidemiology, Oregon Health & Science University, Portland, OR, USA.

The COVID-19 pandemic has resulted in a rapidly growing quantity of scientific publications from journal articles, preprints, and other sources. The TREC-COVID Challenge was created to evaluate information retrieval (IR) methods and systems for this quickly expanding corpus. Using the COVID-19 Open Research Dataset (CORD-19), several dozen research teams participated in over 5 rounds of the TREC-COVID Challenge. While previous work has compared IR techniques used on other test collections, there are no studies that have analyzed the methods used by participants in the TREC-COVID Challenge. We manually reviewed team run reports from Rounds 2 and 5, extracted features from the documented methodologies, and used a univariate and multivariate regression-based analysis to identify features associated with higher retrieval performance. We observed that fine-tuning datasets with relevance judgments, MS-MARCO, and CORD-19 document vectors was associated with improved performance in Round 2 but not in Round 5. Though the relatively decreased heterogeneity of runs in Round 5 may explain the lack of significance in that round, fine-tuning has been found to improve search performance in previous challenge evaluations by improving a system's ability to map relevant queries and phrases to documents. Furthermore, term expansion was associated with improvement in system performance, and the use of the narrative field in the TREC-COVID topics was associated with decreased system performance in both rounds. These findings emphasize the need for clear queries in search. While our study has some limitations in its generalizability and scope of techniques analyzed, we identified some IR techniques that may be useful in building search systems for COVID-19 using the TREC-COVID test collections.
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http://dx.doi.org/10.1016/j.jbi.2021.103745DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC8021447PMC
May 2021

Deep Learning for the Diagnosis of Stage in Retinopathy of Prematurity: Accuracy and Generalizability across Populations and Cameras.

Ophthalmol Retina 2021 10 6;5(10):1027-1035. Epub 2021 Feb 6.

Department of Ophthalmology, Casey Eye Institute, Oregon Health & Science University, Portland, Oregon. Electronic address:

Purpose: Stage is an important feature to identify in retinal images of infants at risk of retinopathy of prematurity (ROP). The purpose of this study was to implement a convolutional neural network (CNN) for binary detection of stages 1, 2, and 3 in ROP and to evaluate its generalizability across different populations and camera systems.

Design: Diagnostic validation study of CNN for stage detection.

Participants: Retinal fundus images obtained from preterm infants during routine ROP screenings.

Methods: Two datasets were used: 5943 fundus images obtained by RetCam camera (Natus Medical, Pleasanton, CA) from 9 North American institutions and 5049 images obtained by 3nethra camera (Forus Health Incorporated, Bengaluru, India) from 4 hospitals in Nepal. Images were labeled based on the presence of stage by 1 to 3 expert graders. Three CNN models were trained using 5-fold cross-validation on datasets from North America alone, Nepal alone, and a combined dataset and were evaluated on 2 held-out test sets consisting of 708 and 247 images from the Nepali and North American datasets, respectively.

Main Outcome Measures: Convolutional neural network performance was evaluated using area under the receiver operating characteristic curve (AUROC), area under the precision-recall curve (AUPRC), sensitivity, and specificity.

Results: Both the North American- and Nepali-trained models demonstrated high performance on a test set from the same population: AUROC, 0.99; AUPRC, 0.98; sensitivity, 94%; and AUROC, 0.97; AUPRC, 0.91; and sensitivity, 73%; respectively. However, the performance of each model decreased to AUROC of 0.96 and AUPRC of 0.88 (sensitivity, 52%) and AUROC of 0.62 and AUPRC of 0.36 (sensitivity, 44%) when evaluated on a test set from the other population. Compared with the models trained on individual datasets, the model trained on a combined dataset achieved improved performance on each respective test set: sensitivity improved from 94% to 98% on the North American test set and from 73% to 82% on the Nepali test set.

Conclusions: A CNN can identify accurately the presence of ROP stage in retinal images, but performance depends on the similarity between training and testing populations. We demonstrated that internal and external performance can be improved by increasing the heterogeneity of the training dataset features of the training dataset, in this case by combining images from different populations and cameras.
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http://dx.doi.org/10.1016/j.oret.2020.12.013DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC8364291PMC
October 2021

Early Outcomes in an Emerging Facial Nerve Center: The Oregon Health and Science University (OHSU) Experience.

Ann Otol Rhinol Laryngol 2021 May 11;130(5):459-466. Epub 2020 Sep 11.

Department of Otolaryngology-Head & Neck Surgery, Oregon Health & Science University, Portland, OR, USA.

Objectives: Nerve transfer (NT) and free gracilis muscle transfer (FGMT) are procedures for reanimation of the paralyzed face. Assessing the surgical outcomes of these procedures is imperative when evaluating the effectiveness of these interventions, especially when establishing a new center focused on the treatment of patients with facial paralysis. We desired to discuss the factors to consider when implementing a facial nerve center and the means by which the specialist can assess and analyze outcomes.

Methods: Patients with facial palsy secondary to multiple etiologies, including cerebellopontine angle tumors, head and neck carcinoma, and trauma, who underwent NT or FGMT between 2014 and 2019 were included. Primary outcomes were facial symmetry and smile excursion, calculated using FACE-gram and Emotrics software. Subjective quality of life outcomes, including the Facial Clinimetric Evaluation (FaCE) Scale and Synkinesis Assessment Questionnaire (SAQ), were also assessed.

Results: 14/22 NT and 6/6 FGMT patients met inclusion criteria having both pre-and postoperative photo documentation. NT increased oral commissure excursion from 0.4 mm (SD 5.3) to 2.9 mm (SD 6.8) ( = 0.05), and improved symmetry of excursion ( < 0.001) and angle ( < 0.001). FGMT increased oral commissure excursion from -1.4 mm (SD 3.9) to 2.1 mm (SD 3.7), ( = 0.02), and improved symmetry of excursion ( < 0.001). FaCE scores improved in NT patients postoperatively ( < 0.001).

Conclusions: Measuring outcomes, critical analyses, and a multidisciplinary approach are necessary components when building a facial nerve center. At our emerging facial nerve center, we found NT and FGMT procedures improved smile excursion and symmetry, and improved QOL following NT in patients with facial palsy secondary to multiple etiologies.
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http://dx.doi.org/10.1177/0003489420957371DOI Listing
May 2021

Applications of Artificial Intelligence to Electronic Health Record Data in Ophthalmology.

Transl Vis Sci Technol 2020 02 27;9(2):13. Epub 2020 Feb 27.

Department of Medical Informatics and Clinical Epidemiology, Oregon Health & Science University, Portland, OR, USA.

Widespread adoption of electronic health records (EHRs) has resulted in the collection of massive amounts of clinical data. In ophthalmology in particular, the volume range of data captured in EHR systems has been growing rapidly. Yet making effective secondary use of this EHR data for improving patient care and facilitating clinical decision-making has remained challenging due to the complexity and heterogeneity of these data. Artificial intelligence (AI) techniques present a promising way to analyze these multimodal data sets. While AI techniques have been extensively applied to imaging data, there are a limited number of studies employing AI techniques with clinical data from the EHR. The objective of this review is to provide an overview of different AI methods applied to EHR data in the field of ophthalmology. This literature review highlights that the secondary use of EHR data has focused on glaucoma, diabetic retinopathy, age-related macular degeneration, and cataracts with the use of AI techniques. These techniques have been used to improve ocular disease diagnosis, risk assessment, and progression prediction. Techniques such as supervised machine learning, deep learning, and natural language processing were most commonly used in the articles reviewed.
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http://dx.doi.org/10.1167/tvst.9.2.13DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7347028PMC
February 2020
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