ArticleBMJ mental health2026
Prediction models for highly scalable technology-assisted differential diagnostics of autism spectrum disorder.
Article in BMJ mental health, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
0 citing papers in PubMed.
No citing paper in PubMed yet.
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
7 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundDiagnosing autism spectrum disorder (ASD) in adulthood is time-consuming and markedly complicated by the requirement to distinguish between ASD and differential diagnoses also associated with social interaction difficulties, such as borderline personality disorder (BPD)-a distinction for which currently no valid screening or diagnostic tool exists. While technology-assisted diagnostics (TAD) has emerged, existing algorithms have focused on classifying between ASD and no diagnosis, not fully addressing clinical reality.
objectiveTherefore, we assessed the feasibility of TAD for differential diagnostics by classifying between ASD and BPD in this proof-of-concept study.
methodsWe collected a rich multimodal dataset of reciprocal interactions, specifically dyadic conversations (n=120 interaction partners). From this data, we extracted more than 800 features, allowing us to capture the core area of defining symptoms for both conditions: social interactions. These features include speech patterns, facial expressions, movement quantity and interpersonal synchrony. We used these features to train and stack linear support vector machines to classify between ASD-involved, BPD-involved and comparison interaction partners.
findingsBase models capturing facial expressions during speaking and listening, speech patterns, synchronisation of facial expressions and movement quantity all performed above chance when differentiating between ASD-involved and BPD-involved interaction partners. Stacking all base models containing conceptually related features further increased accuracy, with our algorithm achieving nearly 82% of balanced accuracy, solely based on 20 min of conversation.
conclusionsOur proof-of-concept study shows the immense potential of TAD for differential diagnostics: data collection only requires microphones and webcams while feature-extraction is automated, making this approach highly objective, scalable and user-friendly. CLINICAL IMPLICATIONS: Our TAD algorithm shows the potential of multimodal, behavioural data for differential diagnostics. On the basis of clinical validation such an algorithm has the potential to streamline differential diagnoses of ASD in the future, enabling faster and more accurate diagnostic assessment and ultimately reducing patient distress by shortening the wait for an appropriate treatment plan.
Indexed as
Identifiers
What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.