AI-Human Hybrid Workflow Enhances Teleophthalmology for the Detection of Diabetic Retinopathy.
2023; 3 (4): 100330
Detection of diabetic retinopathy (DR) outside of specialized eye care settings is an important means of access to vision-preserving health maintenance. Remote interpretation of fundus photographs acquired in a primary care or other nonophthalmic setting in a store-and-forward manner is a predominant paradigm of teleophthalmology screening programs. Artificial intelligence (AI)-based image interpretation offers an alternative means of DR detection. IDx-DR (Digital Diagnostics Inc) is a Food and Drug Administration-authorized autonomous testing device for DR. We evaluated the diagnostic performance of IDx-DR compared with human-based teleophthalmology over 2 and a half years. Additionally, we evaluated an AI-human hybrid workflow that combines AI-system evaluation with human expert-based assessment for referable cases.Prospective cohort study and retrospective analysis.Diabetic patients ≥ 18 years old without a prior DR diagnosis or DR examination in the past year presenting for routine DR screening in a primary care clinic.Macula-centered and optic nerve-centered fundus photographs were evaluated by an AI algorithm followed by consensus-based overreading by retina specialists at the Stanford Ophthalmic Reading Center. Detection of more-than-mild diabetic retinopathy (MTMDR) was compared with in-person examination by a retina specialist.Sensitivity, specificity, accuracy, positive predictive value, and gradability achieved by the AI algorithm and retina specialists.The AI algorithm had higher sensitivity (95.5% sensitivity; 95% confidence interval [CI], 86.7%-100%) but lower specificity (60.3% specificity; 95% CI, 47.7%-72.9%) for detection of MTMDR compared with remote image interpretation by retina specialists (69.5% sensitivity; 95% CI, 50.7%-88.3%; 96.9% specificity; 95% CI, 93.5%-100%). Gradability of encounters was also lower for the AI algorithm (62.5%) compared with retina specialists (93.1%). A 2-step AI-human hybrid workflow in which the AI algorithm initially rendered an assessment followed by overread by a retina specialist of MTMDR-positive encounters resulted in a sensitivity of 95.5% (95% CI, 86.7%-100%) and a specificity of 98.2% (95% CI, 94.6%-100%). Similarly, a 2-step overread by retina specialists of AI-ungradable encounters improved gradability from 63.5% to 95.6% of encounters.Implementation of an AI-human hybrid teleophthalmology workflow may both decrease reliance on human specialist effort and improve diagnostic accuracy.Proprietary or commercial disclosure may be found after the references.
View details for DOI 10.1016/j.xops.2023.100330
View details for PubMedID 37449051
View details for PubMedCentralID PMC10336195
Real world outcomes from artificial intelligence to detect diabetic retinopathy in the primary care setting: 12 month experience
ASSOC RESEARCH VISION OPHTHALMOLOGY INC. 2023
View details for Web of Science ID 001053758300331
Safety and Utility of Transnasal Humidified Rapid-Insufflation Ventilatory Exchange (THRIVE) for Laser Laryngeal Surgery.
Otolaryngology--head and neck surgery : official journal of American Academy of Otolaryngology-Head and Neck Surgery
OBJECTIVE: Transnasal humidified rapid-insufflation ventilatory exchange (THRIVE) is gaining acceptance as a safe method for apneic ventilation and oxygenation during laryngeal procedures, but remains controversial during laser laryngeal surgery (LLS) due to the theoretical risk of airway fire. This study describes our experience with THRIVE during LLS.STUDY DESIGN: Retrospective cohort study.SETTING: Stanford University Hospital, October 15, 2015 to June 1, 2021.METHODS: Retrospective chart review of patients ≥18 years who underwent LLS involving the CO2 or KTP laser with THRIVE as the primary mode of oxygenation.RESULTS: A total of172 cases were identified. 20.9% were obese (BMI≥30). Most common operative indication was subglottic stenosis. The CO2 laser was used in 79.1% of cases. Median lowest intraoperative SpO2 was 96%. 44.7% cases were solely under THRIVE while 16.3% required a single intubation and 19.2% required multiple intubations. Mean apnea time for THRIVE only cases was 32.1minutes and in cases requiring at least one intubation 24.0minutes (p<.001). Mean apnea time was significantly lower for patients who were obese (p<.001) or had a diagnosis of hypertension (p=.016). Obese patients and patients with hypertension were 2.03 and 1.43 times more likely to require intraoperative intubation, respectively. There were no intraoperative complications or fires since the institution of our LLS safety protocol.CONCLUSION: By eliminating the fuel component of the fire triangle, THRIVE can be safely used for continuous delivery of high FiO2 during LLS, provided adherence to institutional THRIVE-LLS protocols.
View details for DOI 10.1002/ohn.324
View details for PubMedID 37021493
Descemet stripping only for Descemet's membrane detachment and sectoral corneal edema.
American journal of ophthalmology case reports
2023; 29: 101784
To describe a case of corneal decompensation in the setting of a Descemet's membrane detachment that developed following aborted Hydrus minimally invasive glaucoma surgery (MIGS) that was successfully treated with a Descemet's stripping only procedure.A 75 year-old female patient presented with symptomatic corneal decompensation following complicated Hydrus MIGS surgery. Ocular coherence tomography demonstrated an inferonasal Descemet's detachment with overlying edema. Specular microscopy revealed undetectable cells centrally but a peripheral cell density of 1446 cells/mm2. The Descemet's detachment did not respond to an intracameral air injection and a subsequent Descemet's stripping only procedure was performed. The corneal edema resolved by postoperative week 6. At postoperative month 2, best corrected visual acuity was 20/40 and specular microscopy demonstrated central cell density of 975 cells/mm2.Descemet's stripping only can be an effective treatment for some cases of corneal decompensation that occur in the setting of a Descemet's membrane detachment from complicated intraocular surgery, such as in this case with MIGS surgery.
View details for DOI 10.1016/j.ajoc.2022.101784
View details for PubMedID 36619161
View details for PubMedCentralID PMC9811206
Predicting Systemic Health Features from Retinal Fundus Images Using Transfer-Learning-Based Artificial Intelligence Models.
Diagnostics (Basel, Switzerland)
2022; 12 (7)
While color fundus photos are used in routine clinical practice to diagnose ophthalmic conditions, evidence suggests that ocular imaging contains valuable information regarding the systemic health features of patients. These features can be identified through computer vision techniques including deep learning (DL) artificial intelligence (AI) models. We aim to construct a DL model that can predict systemic features from fundus images and to determine the optimal method of model construction for this task. Data were collected from a cohort of patients undergoing diabetic retinopathy screening between March 2020 and March 2021. Two models were created for each of 12 systemic health features based on the DenseNet201 architecture: one utilizing transfer learning with images from ImageNet and another from 35,126 fundus images. Here, 1277 fundus images were used to train the AI models. Area under the receiver operating characteristics curve (AUROC) scores were used to compare the model performance. Models utilizing the ImageNet transfer learning data were superior to those using retinal images for transfer learning (mean AUROC 0.78 vs. 0.65, p-value < 0.001). Models using ImageNet pretraining were able to predict systemic features including ethnicity (AUROC 0.93), age > 70 (AUROC 0.90), gender (AUROC 0.85), ACE inhibitor (AUROC 0.82), and ARB medication use (AUROC 0.78). We conclude that fundus images contain valuable information about the systemic characteristics of a patient. To optimize DL model performance, we recommend that even domain specific models consider using transfer learning from more generalized image sets to improve accuracy.
View details for DOI 10.3390/diagnostics12071714
View details for PubMedID 35885619
Integration of Artificial Intelligence into a Telemedicine-Based Diabetic Retinopathy Screening Program
ASSOC RESEARCH VISION OPHTHALMOLOGY INC. 2022
View details for Web of Science ID 000844401304101
Predicting systemic health features from retinal fundus images using transfer-learning based AI models
ASSOC RESEARCH VISION OPHTHALMOLOGY INC. 2022
View details for Web of Science ID 000844437002058
Real-life Wrist Movement Patterns Capture Motor Impairment in Individuals with Ataxia-Telangiectasia.
Cerebellum (London, England)
Sensitive motor outcome measures are needed to efficiently evaluate novel therapies for neurodegenerative diseases. Devices that can passively collect movement data in the home setting can provide continuous and ecologically valid measures of motor function. We tested the hypothesis that movement patterns extracted from continuous wrist accelerometer data capture motor impairment and disease progression in ataxia-telangiectasia. One week of continuous wrist accelerometer data were collected from 31 individuals with ataxia-telangiectasia and 27 controls aged 2-20 years old. Longitudinal wrist sensor data were collected in 14 ataxia-telangiectasia participants and 13 controls. A novel algorithm was developed to extract wrist submovements from the velocity time series. Wrist sensor features were compared with caregiver-reported motor function on the Caregiver Priorities and Child Health Index of Life with Disabilities survey and ataxia severity on the neurologist-performed Brief Ataxia Rating Scale. Submovements became smaller, slower, and less variable in ataxia-telangiectasia compared to controls. High-frequency oscillations in submovements were increased, and more variable and low-frequency oscillations were decreased and less variable in ataxia-telangiectasia. Wrist movement features correlated strongly with ataxia severity and caregiver-reported function, demonstrated high reliability, and showed significant progression over a 1-year interval. These results show that passive wrist sensor data produces interpretable and reliable measures that are sensitive to disease change, supporting their potential as ecologically valid motor biomarkers. The ability to obtain these measures from a low-cost sensor that is ubiquitous in smartwatches could help facilitate neurological care and participation in research regardless of geography and socioeconomic status.
View details for DOI 10.1007/s12311-022-01385-5
View details for PubMedID 35294727
Free-Living Motor Activity Monitoring in Ataxia-Telangiectasia.
Cerebellum (London, England)
With disease-modifying approaches under evaluation in ataxia-telangiectasia and other ataxias, there is a need for objective and reliable biomarkers of free-living motor function. In this study, we test the hypothesis that metrics derived from a single wrist sensor worn at home provide accurate, reliable, and interpretable information about neurological disease severity in children with A-T.A total of 15 children with A-T and 15 age- and sex-matched controls wore a sensor with a triaxial accelerometer on their dominant wrist for 1week at home. Activity intensity measures, derived from the sensor data, were compared with in-person neurological evaluation on the Brief Ataxia Rating Scale (BARS) and performance on a validated computer mouse task.Children with A-T were inactive the same proportion of each day as controls but produced more low intensity movements (p<0.01; Cohen's d=1.48) and fewer high intensity movements (p<0.001; Cohen's d=1.71). The range of activity intensities was markedly reduced in A-T compared to controls (p<0.0001; Cohen's d=2.72). The activity metrics correlated strongly with arm, gait, and total clinical severity (r: 0.71-0.87; p<0.0001), correlated with specific computer task motor features (r: 0.67-0.92; p<0.01), demonstrated high reliability (r: 0.86-0.93; p<0.00001), and were not significantly influenced by age in the healthy control group.Motor activity metrics from a single, inexpensive wrist sensor during free-living behavior provide accurate and reliable information about diagnosis, neurological disease severity, and motor performance. These low-burden measurements are applicable independent of ambulatory status and are potential digital behavioral biomarkers in A-T.
View details for DOI 10.1007/s12311-021-01306-y
View details for PubMedID 34302287