Exploring watercolor illusion spreading between dissected stimulus parts.

The watercolor illusion (WCI) occurs when an achromatic region is surrounded by an outer contour and inner chromatic fringe, resulting in an apparent pale tint of the same hue as the fringe. The WCI both fills in and spreads out, with the previous literature suggesting it always spreads out in the absence of an enclosing border. We examined how global stimulus configuration affects this illusion by dissecting various WCI-inducing stimuli into parts. Specifically, would color spread out of the unenclosed ends of the disconnected parts? Participants provided WCI illusion magnitude ratings and shading data indicating perceived locations of color spreading for a variety of stimulus configurations. Instead of the WCI spreading modally into the spaces between the disconnected parts, we found a global reorganization of the stimuli occurred. The dissected WCI stimuli were perceived as either amodally completed behind a white illusory surface perceptually different than the physically identical background or, as empty space between separate objects depending in part on the distance between dissected parts. This study demonstrates the WCI does not always spread outside of unenclosed borders when the global interpretation interferes with spreading. These findings highlight the importance of global configuration and perceptual organization in the WCI.

Examining Increment thresholds as a function of pedestal contrast under hypothetical parvo- and magnocellular-biased conditions.

Theoretically, the pulsed- and steady-pedestal paradigms are thought to track contrast-increment thresholds (ΔC) as a function of pedestal contrast (C) for the parvocellular (P) and magnocellular (M) systems, respectively, yielding linear ΔC versus C functions for the pulsed- and nonlinear functions for the steady-pedestal paradigm. A recent study utilizing these paradigms to isolate the P and M systems reported no evidence of the M system being suppressed by red light, contrary to previous physiological and psychophysical findings. Curious as to why this may have occurred, we examined how ΔC varies with C for the P and M systems using the pulsed- and steady-pedestal paradigms and stimuli biased towards the P or M systems based on their sensitivity to spatial frequency (SF) and color. We found no effect of color and little influence of SF. To explain this lack of color effects, we used a quantitative model of ΔC (as it changes with C) to obtain Csat and contrast-gain values. The contrast-gain values (i) contradicted the hypothesis that the steady-pedestal paradigm tracks the M-system response, and (ii) our obtained Csat values indicated strongly that both pulsed- and steady-pedestal paradigms track primarily the P-system response.

Dual-Stream Spatiotemporal Networks with Feature Sharing for Monitoring Animals in the Home Cage.

This paper presents a spatiotemporal deep learning approach for mouse behavioral classification in the home-cage. Using a series of dual-stream architectures with assorted modifications for optimal performance, we introduce a novel feature sharing approach that jointly processes the streams at regular intervals throughout the network. The dataset in focus is an annotated, publicly available dataset of a singly-housed mouse. We achieved even better classification accuracy by ensembling the best performing models; an Inception-based network and an attention-based network, both of which utilize this feature sharing attribute. Furthermore, we demonstrate through ablation studies that for all models, the feature sharing architectures consistently outperform the conventional dual-stream having standalone streams. In particular, the inception-based architectures showed higher feature sharing gains with their increase in accuracy anywhere between 6.59% and 15.19%. The best-performing models were also further evaluated on other mouse behavioral datasets.

Bavituximab Decreases Immunosuppressive Myeloid-Derived Suppressor Cells in Newly Diagnosed Glioblastoma Patients.

PURPOSE: We evaluated the efficacy of bavituximab-a mAb with anti-angiogenic and immunomodulatory properties-in newly diagnosed patients with glioblastoma (GBM) who also received radiotherapy and temozolomide. Perfusion MRI and myeloid-related gene transcription and inflammatory infiltrates in pre-and post-treatment tumor specimens were studied to evaluate on-target effects (NCT03139916). PATIENTS AND METHODS: Thirty-three adults with IDH--wild-type GBM received 6 weeks of concurrent chemoradiotherapy, followed by 6 cycles of temozolomide (C1-C6). Bavituximab was given weekly, starting week 1 of chemoradiotherapy, for at least 18 weeks. The primary endpoint was proportion of patients alive at 12 months (OS-12). The null hypothesis would be rejected if OS-12 was ≥72%. Relative cerebral blood flow (rCBF) and vascular permeability (Ktrans) were calculated from perfusion MRIs. Peripheral blood mononuclear cells and tumor tissue were analyzed pre-treatment and at disease progression using RNA transcriptomics and multispectral immunofluorescence for myeloid-derived suppressor cells (MDSC) and macrophages. RESULTS: The study met its primary endpoint with an OS-12 of 73% (95% confidence interval, 59%-90%). Decreased pre-C1 rCBF (HR, 4.63; P = 0.029) and increased pre-C1 Ktrans were associated with improved overall survival (HR, 0.09; P = 0.005). Pre-treatment overexpression of myeloid-related genes in tumor tissue was associated with longer survival. Post-treatment tumor specimens contained fewer immunosuppressive MDSCs (P = 0.01). CONCLUSIONS: Bavituximab has activity in newly diagnosed GBM and resulted in on-target depletion of intratumoral immunosuppressive MDSCs. Elevated pre-treatment expression of myeloid-related transcripts in GBM may predict response to bavituximab.

Association of Biomarker-Based Artificial Intelligence With Risk of Racial Bias in Retinal Images.

Importance: Although race is a social construct, it is associated with variations in skin and retinal pigmentation. Image-based medical artificial intelligence (AI) algorithms that use images of these organs have the potential to learn features associated with self-reported race (SRR), which increases the risk of racially biased performance in diagnostic tasks; understanding whether this information can be removed, without affecting the performance of AI algorithms, is critical in reducing the risk of racial bias in medical AI. Objective: To evaluate whether converting color fundus photographs to retinal vessel maps (RVMs) of infants screened for retinopathy of prematurity (ROP) removes the risk for racial bias. Design, Setting, and Participants: The retinal fundus images (RFIs) of neonates with parent-reported Black or White race were collected for this study. A u-net, a convolutional neural network (CNN) that provides precise segmentation for biomedical images, was used to segment the major arteries and veins in RFIs into grayscale RVMs, which were subsequently thresholded, binarized, and/or skeletonized. CNNs were trained with patients' SRR labels on color RFIs, raw RVMs, and thresholded, binarized, or skeletonized RVMs. Study data were analyzed from July 1 to September 28, 2021. Main Outcomes and Measures: Area under the precision-recall curve (AUC-PR) and area under the receiver operating characteristic curve (AUROC) at both the image and eye level for classification of SRR. Results: A total of 4095 RFIs were collected from 245 neonates with parent-reported Black (94 [38.4%]; mean [SD] age, 27.2 [2.3] weeks; 55 majority sex [58.5%]) or White (151 [61.6%]; mean [SD] age, 27.6 [2.3] weeks, 80 majority sex [53.0%]) race. CNNs inferred SRR from RFIs nearly perfectly (image-level AUC-PR, 0.999; 95% CI, 0.999-1.000; infant-level AUC-PR, 1.000; 95% CI, 0.999-1.000). Raw RVMs were nearly as informative as color RFIs (image-level AUC-PR, 0.938; 95% CI, 0.926-0.950; infant-level AUC-PR, 0.995; 95% CI, 0.992-0.998). Ultimately, CNNs were able to learn whether RFIs or RVMs were from Black or White infants regardless of whether images contained color, vessel segmentation brightness differences were nullified, or vessel segmentation widths were uniform. Conclusions and Relevance: Results of this diagnostic study suggest that it can be very challenging to remove information relevant to SRR from fundus photographs. As a result, AI algorithms trained on fundus photographs have the potential for biased performance in practice, even if based on biomarkers rather than raw images. Regardless of the methodology used for training AI, evaluating performance in relevant subpopulations is critical.

Regularities in vertical saccadic metrics: new insights, and future perspectives.

Introduction: Asymmetries in processing by the healthy brain demonstrate regularities that facilitate the modeling of brain operations. The goal of the present study was to determine asymmetries in saccadic metrics during visual exploration, devoid of confounding clutter in the visual field. Methods: Twenty healthy adults searched for a small, low-contrast gaze-contingent target on a blank computer screen. The target was visible, only if eye fixation was within a 5 deg. by 5 deg. area of the target's location. Results: Replicating previously-reported asymmetries, repeated measures contrast analyses indicated that up-directed saccades were executed earlier, were smaller in amplitude, and had greater probability than down-directed saccades. Given that saccade velocities are confounded by saccade amplitudes, it was also useful to investigate saccade kinematics of visual exploration, as a function of vertical saccade direction. Saccade kinematics were modeled for each participant, as a square root relationship between average saccade velocity (i.e., average velocity between launching and landing of a saccade) and corresponding saccade amplitude (Velocity = S*[Saccade Amplitude]0.5). A comparison of the vertical scaling parameter (S) for up- and down-directed saccades showed that up-directed saccades tended to be slower than down-directed ones. Discussion: To motivate future research, an ecological theory of asymmetric pre-saccadic inhibition was presented to explain the collection of vertical saccadic regularities. For example, given that the theory proposes strong inhibition for the releasing of reflexive down-directed prosaccades (cued by an attracting peripheral target below eye fixation), and weak inhibition for the releasing of up-directed prosaccades (cued by an attracting peripheral target above eye fixation), a prediction for future studies is longer reaction times for vertical anti-saccade cues above eye fixation. Finally, the present study with healthy individuals demonstrates a rationale for further study of vertical saccades in psychiatric disorders, as bio-markers for brain pathology.

Improvement in Multidisciplinary Provider Rounding (Surgical Rounds) in the Pediatric Cardiac ICU: An Application of Lean Methodology.

OBJECTIVES: Provider-only, combined surgical, and medical multidisciplinary rounds ("surgical rounds") are essential to achieve optimal outcomes in large pediatric cardiac ICUs. Lean methodology was applied with the aims of identifying areas of waste and nonvalue-added work within the surgical rounds process. Thereby, the goals were to improve rounding efficiency and reduce rounding duration while not sacrificing critical patient care discussion nor delaying bedside rounds or surgical start times. DESIGN: Single-center improvement science study with observational and interventional phases from February 2, 2021, to July 31, 2021. SETTING: Tertiary pediatric cardiac ICU. PARTICIPANTS: Cardiothoracic surgery and cardiac intensive care team members participating in daily "surgical" rounds. INTERVENTIONS: Implementation of technology automation, creation of work instructions, standardization of patient presentation content and order, provider training, and novel role assignment. MEASUREMENTS AND MAIN RESULTS: Sixty-one multidisciplinary rounds were observed (30 pre, 31 postintervention). During the preintervention period, identified inefficiencies included prolonged preparation time, redundant work, presentation variability and extraneous information, and frequent provider transitions. Application of targeted interventions resulted in a 26% decrease in indexed rounds duration (2.42 vs 1.8 min; p = 0.0003), 50% decrease in indexed rounds preparation time (0.53 vs 0.27 min; p < 0.0001), and 66% decrease in transition time between patients (0.09 vs 0.03 min; p < 0.0001). The number of presenting provider changes also decreased (9 vs 4; p < 0.0001). Indexed discussion duration did not change (1 vs 0.98 min; p = 0.08) nor did balancing measures (bedside rounds and surgical start times) change (8.5 vs 9 min; p = 0.89 and 38 vs 22 min; p = 0.09). CONCLUSIONS: Lean methodology can be effectively applied to multidisciplinary rounds in a joint cardiothoracic surgery/cardiac intensive care setting to decrease waste and inefficiency. Interventions resulted in decreased preparation time, transition time, presenting provider changes, total rounds duration indexed to patient census, and anecdotal improvements in provider satisfaction.

Federated Learning for Multicenter Collaboration in Ophthalmology: Implications for Clinical Diagnosis and Disease Epidemiology.

OBJECTIVE: 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). DESIGN: Evaluation of a diagnostic test or technology. SUBJECTS AND CONTROLS: We included 5245 patients with wide-angle retinal imaging from the neonatal intensive care units of 7 institutions as part of the Imaging and Informatics in ROP study. Images were labeled with the clinical diagnoses of plus disease (plus, preplus, no plus), which were documented in the chart, and a reference standard diagnosis was determined by 3 image-based ROP graders and the clinical diagnosis. METHODS: Demographics (birth weight, gestational age) and clinical diagnoses for all eye examinations were recorded from each institution. Using an FL approach, a DL model for plus disease classification was trained using only the clinical labels. The 3 class probabilities were then converted into a vascular severity score (VSS) for each eye examination, as well as an "institutional VSS," in which the average of the VSS values assigned to patients' higher severity ("worse") eyes at each examination was calculated for each institution. MAIN OUTCOME MEASURES: We compared demographics, clinical diagnoses of plus disease, and institutional VSSs between institutions using the McNemar-Bowker test, 2-proportion Z test, and 1-way analysis of variance with post hoc analysis by the Tukey-Kramer test. Single regression analysis was performed to explore the relationship between demographics and VSSs. RESULTS: We found that the proportion of patients diagnosed with preplus disease varied significantly between institutions (P < 0.001). Using the DL-derived VSS trained on the data from all institutions using FL, we observed differences in the institutional VSS and the level of vascular severity diagnosed as no plus (P < 0.001) across institutions. A significant, inverse relationship between the institutional VSS and mean gestational age was found (P = 0.049, adjusted R2 = 0.49). CONCLUSIONS: A DL-derived ROP VSS developed without sharing data between institutions using FL identified differences in the clinical diagnoses of plus disease and overall levels of ROP severity between institutions. Federated learning may represent a method to standardize clinical diagnoses and provide objective measurements of disease for image-based diseases.

Federated Learning for Multicenter Collaboration in Ophthalmology: Improving Classification Performance in Retinopathy of Prematurity.

OBJECTIVE: To compare the performance of deep learning classifiers for the diagnosis of plus disease in retinopathy of prematurity (ROP) trained using 2 methods for developing models on multi-institutional data sets: centralizing data versus federated learning (FL) in which no data leave each institution. DESIGN: Evaluation of a diagnostic test or technology. SUBJECTS: Deep learning models were trained, validated, and tested on 5255 wide-angle retinal images in the neonatal intensive care units of 7 institutions as part of the Imaging and Informatics in ROP study. All images were labeled for the presence of plus, preplus, or no plus disease with a clinical label and a reference standard diagnosis (RSD) determined by 3 image-based ROP graders and the clinical diagnosis. METHODS: We compared the area under the receiver operating characteristic curve (AUROC) for models developed on multi-institutional data, using a central approach initially, followed by FL, and compared locally trained models with both approaches. We compared the model performance (κ) with the label agreement (between clinical and RSD), data set size, and number of plus disease cases in each training cohort using the Spearman correlation coefficient (CC). MAIN OUTCOME MEASURES: Model performance using AUROC and linearly weighted κ. RESULTS: Four settings of experiment were used: FL trained on RSD against central trained on RSD, FL trained on clinical labels against central trained on clinical labels, FL trained on RSD against central trained on clinical labels, and FL trained on clinical labels against central trained on RSD (P = 0.046, P = 0.126, P = 0.224, and P = 0.0173, respectively). Four of the 7 (57%) models trained on local institutional data performed inferiorly to the FL models. The model performance for local models was positively correlated with the label agreement (between clinical and RSD labels, CC = 0.389, P = 0.387), total number of plus cases (CC = 0.759, P = 0.047), and overall training set size (CC = 0.924, P = 0.002). CONCLUSIONS: We found that a trained FL model performs comparably to a centralized model, confirming that FL may provide an effective, more feasible solution for interinstitutional learning. Smaller institutions benefit more from collaboration than larger institutions, showing the potential of FL for addressing disparities in resource access.

Influence of context on spatial expanse of color spreading in the watercolor illusion.

The watercolor illusion (WCI) occurs when a physically non-colored region surrounded by contrasting contour and fringe appears filled in with a hue similar to the fringe. The present experiments explored how local and global stimulus factors influence the spatial expanse of WCI color spreading. Experiment 1 utilized two- and three-dimensional-appearing stimuli with the WCI in only one part of each stimulus. Some conditions fully enclosed the color-spreading region with fringe on all sides. Others removed fringe from one side, opening up the color-spreading region to another part of the stimulus. Regardless of perceived dimensionality or enclosure, color did not spread beyond the fringed color-spreading region as confirmed by illusion magnitude ratings and handwritten shading. Experiment 2 consisted of transparent "wireframe" versions of the opaque-appearing stimuli used in Experiment 1. This altered the local context by adding physical contours inside the fringed color-spreading region. As in Experiment 1, color did not spread beyond physically open regions. Furthermore, illusory color filled a space bound by a combination of physical and illusory contours depending on the fringe end-cuts and other perceptual organization cues within the stimulus. Our main focus in these experiments was to determine where color spreads in a variety of contexts. Perceptual organization factors other than perceived depth seem more likely to impact the spatial expanse of WCI color spreading. These are some of the first experiments to explore the impact of changes to local and global context on the spatial expanse of the WCI.

Radiomics Repeatability Pitfalls in a Scan-Rescan MRI Study of Glioblastoma.

PURPOSE: To determine the influence of preprocessing on the repeatability and redundancy of radiomics features extracted using a popular open-source radiomics software package in a scan-rescan glioblastoma MRI study. MATERIALS AND METHODS: In this study, a secondary analysis of T2-weighted fluid-attenuated inversion recovery (FLAIR) and T1-weighted postcontrast images from 48 patients (mean age, 56 years [range, 22-77 years]) diagnosed with glioblastoma were included from two prospective studies (ClinicalTrials.gov NCT00662506 [2009-2011] and NCT00756106 [2008-2011]). All patients underwent two baseline scans 2-6 days apart using identical imaging protocols on 3-T MRI systems. No treatment occurred between scan and rescan, and tumors were essentially unchanged visually. Radiomic features were extracted by using PyRadiomics (https://pyradiomics.readthedocs.io/) under varying conditions, including normalization strategies and intensity quantization. Subsequently, intraclass correlation coefficients were determined between feature values of the scan and rescan. RESULTS: Shape features showed a higher repeatability than intensity (adjusted P < .001) and texture features (adjusted P < .001) for both T2-weighted FLAIR and T1-weighted postcontrast images. Normalization improved the overlap between the region of interest intensity histograms of scan and rescan (adjusted P < .001 for both T2-weighted FLAIR and T1-weighted postcontrast images), except in scans where brain extraction fails. As such, normalization significantly improves the repeatability of intensity features from T2-weighted FLAIR scans (adjusted P = .003 [z score normalization] and adjusted P = .002 [histogram matching]). The use of a relative intensity binning strategy as opposed to default absolute intensity binning reduces correlation between gray-level co-occurrence matrix features after normalization. CONCLUSION: Both normalization and intensity quantization have an effect on the level of repeatability and redundancy of features, emphasizing the importance of both accurate reporting of methodology in radiomics articles and understanding the limitations of choices made in pipeline design. Supplemental material is available for this article. © RSNA, 2020See also the commentary by Tiwari and Verma in this issue.

Applications of Artificial Intelligence for Retinopathy of Prematurity Screening.

OBJECTIVES: Childhood blindness from retinopathy of prematurity (ROP) is increasing as a result of improvements in neonatal care worldwide. We evaluate the effectiveness of artificial intelligence (AI)-based screening in an Indian ROP telemedicine program and whether differences in ROP severity between neonatal care units (NCUs) identified by using AI are related to differences in oxygen-titrating capability. METHODS: External validation study of an existing AI-based quantitative severity scale for ROP on a data set of images from the Retinopathy of Prematurity Eradication Save Our Sight ROP telemedicine program in India. All images were assigned an ROP severity score (1-9) by using the Imaging and Informatics in Retinopathy of Prematurity Deep Learning system. We calculated the area under the receiver operating characteristic curve and sensitivity and specificity for treatment-requiring retinopathy of prematurity. Using multivariable linear regression, we evaluated the mean and median ROP severity in each NCU as a function of mean birth weight, gestational age, and the presence of oxygen blenders and pulse oxygenation monitors. RESULTS: The area under the receiver operating characteristic curve for detection of treatment-requiring retinopathy of prematurity was 0.98, with 100% sensitivity and 78% specificity. We found higher median (interquartile range) ROP severity in NCUs without oxygen blenders and pulse oxygenation monitors, most apparent in bigger infants (>1500 g and 31 weeks' gestation: 2.7 [2.5-3.0] vs 3.1 [2.4-3.8]; P = .007, with adjustment for birth weight and gestational age). CONCLUSIONS: Integration of AI into ROP screening programs may lead to improved access to care for secondary prevention of ROP and may facilitate assessment of disease epidemiology and NCU resources.

LAMA: automated image analysis for the developmental phenotyping of mouse embryos.

Advanced 3D imaging modalities, such as micro-computed tomography (micro-CT), have been incorporated into the high-throughput embryo pipeline of the International Mouse Phenotyping Consortium (IMPC). This project generates large volumes of raw data that cannot be immediately exploited without significant resources of personnel and expertise. Thus, rapid automated annotation is crucial to ensure that 3D imaging data can be integrated with other multi-dimensional phenotyping data. We present an automated computational mouse embryo phenotyping pipeline that harnesses the large amount of wild-type control data available in the IMPC embryo pipeline in order to address issues of low mutant sample number as well as incomplete penetrance and variable expressivity. We also investigate the effect of developmental substage on automated phenotyping results. Designed primarily for developmental biologists, our software performs image pre-processing, registration, statistical analysis and segmentation of embryo images. We also present a novel anatomical E14.5 embryo atlas average and, using it with LAMA, show that we can uncover known and novel dysmorphology from two IMPC knockout lines.

Evaluation of a Deep Learning-Derived Quantitative Retinopathy of Prematurity Severity Scale.

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 (<3 clock hours, 3-6 clock hours, and >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.

A Failed Cardiac Surgery Program in an Underserved Minority Population County Reimagined: The Power of Partnership.

Background Prince George's County Maryland, historically a medically underserved region, has a population of 909 327 and a high incidence of cardiometabolic syndrome and hypertension. Application of level I evidence practices in such areas requires the availability of highly advanced cardiovascular interventions. Donabedian principles of quality of care were applied to a failing cardiac surgery program. We hypothesized that a multidisciplinary application of this model supported by partnership with a university hospital system could result in improved quality care outcomes. Methods and Results A 6-month assessment and planning process commenced in July 2014. Preoperative, intraoperative, and postoperative protocols were developed before program restart. Staff education and training was conducted via team simulation and rehearsal sessions. A total of 425 patients underwent cardiac surgical procedures. Quality tracking of key performance measures was conducted, and 323 isolated coronary artery bypass grafting procedures were performed from July 2014 to December 2019. Key risk factors in our patient demographic were higher than the Society of Thoracic Surgeons national mean. Risk-adjusted outcome data yielded a mortality rate of 0.3% versus 2.2% nationally. The overall major complication rate was lower than expected at 7.1% compared with 11.5% nationally. Readmission rate was less than the Society of Thoracic Surgeons mean for isolated coronary artery bypass grafting (4.0% versus 10.1%, P<0.0001). Significant differences in 6 key performance outcomes were noted, leading to a 3-star Society of Thoracic Surgeons designation in 7 of 8 tracking periods. Conclusions Excellent outcomes in cardiac surgery are attainable following program renovation in an underserved region in the setting of low volume. The principles and processes applied have potential broad application for any quality improvement effort.

When figure-ground segregation fails: Exploring antagonistic interactions in figure-ground perception.

Perceptual fading of an artificial scotoma can be viewed as a failure of figure-ground segregation, providing a useful tool for investigating possible mechanisms and processes involved in figure-ground perception. Weisstein's antagonistic magnocellular/parvocellular stream figure-ground model proposes P stream activity encodes figure, and M stream activity encodes background. Where a boundary separates two regions, the region that is perceived as figure or ground is determined by the outcome of antagonism between M and P activity within each region and across the boundary between them. The region with the relatively stronger P "figure signal" is perceived as figure, and the region with the relatively stronger M "ground signal" is perceived as ground. From this perspective, fading occurs when the figure signal is overwhelmed by the ground signal. Strengthening the figure signal or weakening the ground signal should make the figure more resistant to fading. Based on research showing that red light suppresses M activity and short wavelength sensitive S-cones provide minimal input to M cells, we used red and blue light to reduce M activity in both figure and ground. The time to fade from stimulus onset until the figure completely disappeared was measured. Every combination of gray, green, red, and blue as figure and/or ground was tested. Compared with gray and green light, fade times were greatest when red or blue light either strengthened the figure signal by reducing M activity in the figure, or weakened the ground signal by reducing M activity in ground. The results support a dynamic antagonistic relationship between M and P activity contributing to figure-ground perception as envisioned in Weisstein's model.

Plus Disease in Retinopathy of Prematurity: Convolutional Neural Network Performance Using a Combined Neural Network and Feature Extraction Approach.

Purpose: Retinopathy of prematurity (ROP), a leading cause of childhood blindness, is diagnosed by clinical ophthalmoscopic examinations or reading retinal images. Plus disease, defined as abnormal tortuosity and dilation of the posterior retinal blood vessels, is the most important feature to determine treatment-requiring ROP. We aimed to create a complete, publicly available and feature-extraction-based pipeline, I-ROP ASSIST, that achieves convolutional neural network (CNN)-like performance when diagnosing plus disease from retinal images. Methods: We developed two datasets containing 100 and 5512 posterior retinal images, respectively. After segmenting retinal vessels, we detected the vessel centerlines. Then, we extracted features relevant to ROP, including tortuosity and dilation measures, and used these features in the classifiers including logistic regression, support vector machine and neural networks to assess a severity score for the input. We tested our system with fivefold cross-validation and calculated the area under the curve (AUC) metric for each classifier and dataset. Results: For predicting plus versus not-plus categories, we achieved 99% and 94% AUC on the first and second datasets, respectively. For predicting pre-plus or worse versus normal categories, we achieved 99% and 88% AUC on the first and second datasets, respectively. The CNN method achieved 98% and 94% for predicting two categories on the second dataset. Conclusions: Our system combining automatic retinal vessel segmentation, tracing, feature extraction and classification is able to diagnose plus disease in ROP with CNN-like performance. Translational Relevance: The high performance of I-ROP ASSIST suggests potential applications in automated and objective diagnosis of plus disease.

Variability in Plus Disease Identified Using a Deep Learning-Based Retinopathy of Prematurity Severity Scale.

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 < 0.01). 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.

Siamese neural networks for continuous disease severity evaluation and change detection in medical imaging.

Using medical images to evaluate disease severity and change over time is a routine and important task in clinical decision making. Grading systems are often used, but are unreliable as domain experts disagree on disease severity category thresholds. These discrete categories also do not reflect the underlying continuous spectrum of disease severity. To address these issues, we developed a convolutional Siamese neural network approach to evaluate disease severity at single time points and change between longitudinal patient visits on a continuous spectrum. We demonstrate this in two medical imaging domains: retinopathy of prematurity (ROP) in retinal photographs and osteoarthritis in knee radiographs. Our patient cohorts consist of 4861 images from 870 patients in the Imaging and Informatics in Retinopathy of Prematurity (i-ROP) cohort study and 10,012 images from 3021 patients in the Multicenter Osteoarthritis Study (MOST), both of which feature longitudinal imaging data. Multiple expert clinician raters ranked 100 retinal images and 100 knee radiographs from excluded test sets for severity of ROP and osteoarthritis, respectively. The Siamese neural network output for each image in comparison to a pool of normal reference images correlates with disease severity rank (ρ = 0.87 for ROP and ρ = 0.89 for osteoarthritis), both within and between the clinical grading categories. Thus, this output can represent the continuous spectrum of disease severity at any single time point. The difference in these outputs can be used to show change over time. Alternatively, paired images from the same patient at two time points can be directly compared using the Siamese neural network, resulting in an additional continuous measure of change between images. Importantly, our approach does not require manual localization of the pathology of interest and requires only a binary label for training (same versus different). The location of disease and site of change detected by the algorithm can be visualized using an occlusion sensitivity map-based approach. For a longitudinal binary change detection task, our Siamese neural networks achieve test set receiving operator characteristic area under the curves (AUCs) of up to 0.90 in evaluating ROP or knee osteoarthritis change, depending on the change detection strategy. The overall performance on this binary task is similar compared to a conventional convolutional deep-neural network trained for multi-class classification. Our results demonstrate that convolutional Siamese neural networks can be a powerful tool for evaluating the continuous spectrum of disease severity and change in medical imaging.