Duffy, J

Abstract Background To promote equitable access to neurologic diagnostics, our team developed MASTERMIND—the Mobile Assessment of Speech To Evaluate, Recognize, and Monitor Indicators of Neurologic health and Disease. One subtest, the MASTERMIND Assessment of Naming, includes ten pictures randomly selected from a list of 45 words varying in frequency. This study aimed to 1) evaluate the feasibility of a remotely self‐administered naming test for patients with cognitive, linguistic, and/or communication difficulties and 2) assess the sensitivity and specificity of performance measures (accuracy, error type, and response time) for detecting aphasia and cognitive‐communication difficulties. Method The task was remotely administered to 138 controls, 26 cognitively impaired individuals, 15 with motor speech disorders, 8 with cognitive concerns, and 6 with aphasia. Participants named pictures displayed on‐screen, advancing through the task by clicking “Next.” Responses were processed using natural language processing (NLP). Audio was transcribed with Whisper Large‐v3, and responses were matched to stimuli within a 0.4‐second threshold. Errors were categorized by type (semantic, phonemic, or unclear source) through consensus between two raters. Performance metrics (accuracy, error type, response time) were analyzed using Kruskal‐Wallis and Pearson's Chi‐Square tests, with AUROC analysis evaluating diagnostic performance. Results Differences in accuracy, response time, and error type were observed across groups (Table 1). Controls and cognitively impaired participants performed similarly, with longer response times observed in patients with aphasia and motor speech disorders. Accuracy alone was a poor predictor of all patients versus controls (AUROC = 0.468) but prediction was better in distinguishing aphasia patients from 1) controls (AUROC = 0.698), 2) cognitively impaired participants (AUROC = 0.679), and 3) those with MSDs (AUROC = 0.670). Conclusions The MASTERMIND Assessment of Naming is a feasible, scalable, self‐administered diagnostic tool enhanced by automated NLP. It provides critical insights through response time, accuracy, and error analysis. Limitations include potential bias in error classification and fixed processing thresholds. The study highlights MASTERMIND's potential for patient triaging and screening. Future research will evaluate the sensitivity of combined performance metrics to disease progression, expand sample sizes and diversity, and refine usability based on participant feedback.

Abstract Background Primary progressive apraxia of speech (PPAOS) is a neurodegenerative disorder affecting speech motor planning/programming. Current interventions for PPAOS are limited and often adapted from treatments for nondegenerative AOS, which rely on intensive practice to maintain speech accuracy for practiced items. Systematic interventions for PPAOS are scarce, and no studies have explored differential effects across AOS subtypes. PPAOS subtypes include those with predominant phonetic (articulatory distortions and substitutions) or prosodic (slow speech rate, segmentation) speech changes. These subtypes likely differ in their neural underpinnings and may respond variably to intervention. Delayed auditory feedback (DAF), which introduces a slight delay in hearing one's speech, has shown promise in altering speech rate and fluency for stuttering and hypokinetic dysarthria, though its effects on stroke‐related AOS have been mixed. Recent studies suggest DAF can increase speech rate in patients with AOS in the context of nonfluent/agrammatic primary progressive aphasia. We hypothesized DAF would improve speech clarity and fluency in PPAOS, with subtype‐specific effects: phonetic PPAOS may see slower, more accurate speech, while prosodic PPAOS may produce faster, less segmented speech. Method Four PPAOS patients (3 prosodic, 1 phonetic) completed a web‐based speech battery with and without DAF. Feedback delay was calibrated individually using the DAF Pro app. Speech metrics were analyzed acoustically, and blinded perceptual ratings of deviant speech features were conducted. Results Key data are summarized in Table 1. Three out of four patients found the DAF helpful and two re‐recorded with DAF to allow for quantitative analysis. Patient 1 produced words faster with DAF but had slower total sentence production times; he did not find it helpful. Patient 2 spoke slower with DAF. Both patients produced AMRs faster with DAF, while SMRs were faster for Patient 1 and slower for Patient 2. Blinded perceptual judgments of speech clarity and prosody revealed mixed effects of DAF across participants. Conclusions Preliminary results suggest DAF may differentially benefit phonetic and prosodic PPAOS, particularly in sentences compared to word production. Further research is needed to assess clinical significance, refine DAF calibration, and explore patient‐perceived improvements with guided practice in a larger, more diverse sample.