Purpose: To develop and validate a deep learning (DL) algorithm that predicts referable glaucomatous optic neuropathy (GON) and optic nerve head (ONH) features from color fundus images, to determine the relative importance of these features in referral decisions by glaucoma specialists (GSs) and the algorithm, and to compare the performance of the algorithm with eye care providers.Design: Development and validation of an algorithm.Participants: Fundus images from screening programs, studies, and a glaucoma clinic. Methods: A DL algorithm was trained using a retrospective dataset of 86 618 images, assessed for glaucomatous ONH features and referable GON (defined as ONH appearance worrisome enough to justify referral for comprehensive examination) by 43 graders. The algorithm was validated using 3 datasets: dataset A (1205 images, 1 image/patient; 18.1% referable), images adjudicated by panels of GSs; dataset B (9642 images, 1 image/ patient; 9.2% referable), images from a diabetic teleretinal screening program; and dataset C (346 images, 1 image/patient; 81.7% referable), images from a glaucoma clinic.Main Outcome Measures: The algorithm was evaluated using the area under the receiver operating characteristic curve (AUC), sensitivity, and specificity for referable GON and glaucomatous ONH features.Results: The algorithm's AUC for referable GON was 0.945 (95% confidence interval [CI], 0.929e0.960) in dataset A, 0.855 (95% CI, 0.841e0.870) in dataset B, and 0.881 (95% CI, 0.838e0.918) in dataset C. Algorithm AUCs ranged between 0.661 and 0.973 for glaucomatous ONH features. The algorithm showed significantly higher sensitivity than 7 of 10 graders not involved in determining the reference standard, including 2 of 3 GSs, and showed higher specificity than 3 graders (including 1 GS), while remaining comparable to others. For both GSs and the algorithm, the most crucial features related to referable GON were: presence of vertical cup-to-disc ratio of 0.7 or more, neuroretinal rim notching, retinal nerve fiber layer defect, and bared circumlinear vessels.Conclusions: A DL algorithm trained on fundus images alone can detect referable GON with higher sensitivity than and comparable specificity to eye care providers. The algorithm maintained good performance on an independent dataset with diagnoses based on a full glaucoma workup.

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Children with epilepsy in low-income countries often go undiagnosed and untreated. We examine a portable, low-cost smartphone-based EEG technology in a heterogeneous pediatric epilepsy cohort in the West African Republic of Guinea. Methods: : Children with epilepsy were recruited at the Ignace Deen Hospital in Conakry, 2017. Participants underwent sequential EEG recordings with an app-based EEG, the Smartphone Brain Scanner-2 (SBS2) and a standard Xltek EEG. Raw EEG data were transmitted via Bluetooth ™ connection to an Android ™ tablet and uploaded for remote EEG specialist review and reporting via a new, secure web-based reading platform, crowdEEG. The results were compared to same-visit Xltek 10-20 EEG recordings for identification of epileptiform and nonepileptiform abnormalities. Results: : 97 children meeting the International League Against Epilepsy's definition of epilepsy (49 male; mean age 10.3 years, 29 untreated with an antiepileptic drug; 0 with a prior EEG) were enrolled. Epileptiform discharges were detected on 21 (25.3%) SBS2 and 31 (37.3%) standard EEG recordings. The SBS2 had a sensitivity of 51.6% (95%CI 32.4%, 70.8%) and a specificity of 90.4% (95%CI 81.4%, 94.4%) for all types of epileptiform discharges, with positive and negative predictive values of 76.2% and 75.8% respectively. For generalized discharges, the SBS2 had a sensitivity of 43.5% with a specificity of 96.2%. Conclusions: : The SBS2 has a moderate sensitivity and high specificity for the detection of epileptiform abnormalities in children with epilepsy in this low-income setting. Use of the SBS2+crowdEEG platform permits specialist input for patients with previously poor access to clinical neurophysiology expertise.

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Expert disagreement is pervasive in clinical decision making and collective adjudication is a useful approach for resolving divergent assessments. Prior work shows that expert disagreement can arise due to diverse factors including expert background, the quality and presentation of data, and guideline clarity. In this work, we study how these factors predict initial discrepancies in the context of medical time series analysis, examining why certain disagreements persist after adjudication, and how adjudication impacts clinical decisions. Results from a case study with 36 experts and 4,543 adjudicated cases in a sleep stage classification task show that these factors contribute to both initial disagreement and resolvability, each in their own unique way. We provide evidence suggesting that structured adjudication can lead to significant revisions in treatment-relevant clinical parameters. Our work demonstrates how structured adjudication can support consensus and facilitate a deep understanding of expert disagreement in medical data analysis.

Background and purpose Epilepsy is most common in lower‐income settings where access to electroencephalography (EEG) is generally poor. A low‐cost tablet‐based EEG device may be valuable, but the quality and reproducibility of the EEG output are not established. Methods Tablet‐based EEG was deployed in a heterogeneous epilepsy cohort in the Republic of Guinea (2018–2019), consisting of a tablet wirelessly connected to a 14‐electrode cap. Participants underwent EEG twice (EEG1 and EEG2), separated by a variable time interval. Recordings were scored remotely by experts in clinical neurophysiology as to data quality and clinical utility. Results There were 149 participants (41% female; median age 17.9 years; 66.6% ≤21 years of age; mean seizures per month 5.7 ± SD 15.5). The mean duration of EEG1 was 53 ± 12.3 min and that of EEG2 was 29.6 ± 12.8 min. The mean quality scores of EEG1 and EEG2 were 6.4 [range, 1 (low) to 10 (high); both medians 7.0]. A total of 44 (29.5%) participants had epileptiform discharges (EDs) at EEG1 and 25 (16.8%) had EDs at EEG2. EDs were focal/multifocal (rather than generalized) in 70.1% of EEG1 and 72.5% of EEG2 interpretations. A total of 39 (26.2%) were recommended for neuroimaging after EEG1 and 22 (14.8%) after EEG2. Of participants without EDs at EEG1 (n = 53, 55.8%), seven (13.2%) had EDs at EEG2. Of participants with detectable EDs on EEG1 (n = 23, 24.2%), 12 (52.1%) did not have EDs at EEG2. Conclusions Tablet‐based EEG had a reproducible quality level on repeat testing and was useful for the detection of EDs. The incremental yield of a second EEG in this setting was ~13%. The need for neuroimaging access was evident.

Purpose: To present and evaluate a remote, tool-based system and structured grading rubric for adjudicating image-based diabetic retinopathy (DR) grades. Methods: We compared three different procedures for adjudicating DR severity assessments among retina specialist panels, including (1) in-person adjudication based on a previously described procedure (Baseline), (2) remote, tool-based adjudication for assessing DR severity alone (TA), and (3) remote, tool-based adjudication using a feature-based rubric (TA-F). We developed a system allowing graders to review images remotely and asynchronously. For both TA and TA-F approaches, images with disagreement were reviewed by all graders in a roundrobin fashion until disagreements were resolved. Five panels of three retina specialists each adjudicated a set of 499 retinal fundus images (1 panel using Baseline, 2 using TA, and 2 using TA-F adjudication). Reliability was measured as grade agreement among the panels using Cohen's quadratically weighted kappa. Efficiency was measured as the number of rounds needed to reach a consensus for tool-based adjudication. Results: The grades from remote, tool-based adjudication showed high agreement with the Baseline procedure, with Cohen's kappa scores of 0.948 and 0.943 for the two TA panels, and 0.921 and 0.963 for the two TA-F panels. Cases adjudicated using TA-F were resolved in fewer rounds compared with TA (P , 0.001; standard permutation test). Conclusions: Remote, tool-based adjudication presents a flexible and reliable alternative to in-person adjudication for DR diagnosis. Feature-based rubrics can help accelerate consensus for tool-based adjudication of DR without compromising label quality. Translational Relevance: This approach can generate reference standards to validate automated methods, and resolve ambiguous diagnoses by integrating into existing telemedical workflows.

Identifying player motivations such as curiosity could help game designers analyze player profiles and substantially improve game design. However, research on player profiling focuses on generalized personality traits, not specific aspects of motivation. This study examines how player behaviour indicates constructs of curiosity-related motivation. It contributes a more discriminating operationalization of game-related curiosity. We derive a curiosity measure from established self-report survey methodologies relating to social capital, behavioural activation, obsessive/harmonious passion, and BrainHex player types. We present the results of a cross-sectional study with data from 1,745 players of Destiny-a popular shared-world first-person shooter (FPS) game. Behaviour metrics were paired with four curiosity factors: 'social' curiosity, 'sensory/cognitive' curiosity, 'novelty-seeking' curiosity, and 'explorative' curiosity. Our findings provide key insights into the relationships between players curiosity and their in-game behaviour. We infer curiosity-related motivational profiles from behaviour metrics, and discuss how this may impact game design and player-computer interaction.