Understanding Barriers to Diagnosis in a Rare, Genetic Disease: Delays and Errors Diagnosing Schwannomatosis

Diagnosis of rare, genetic diseases is challenging, but conceptual frameworks of the diagnostic process can be used to guide benchmarking and process improvement initiatives. Using the National Academy of Medicine diagnostic framework, we assessed the extent of, and reasons for, diagnostic delays and errors in schwannomatosis, a neurogenetic syndrome characterized by nerve sheath tumors and chronic pain. We reviewed the medical records of 97 patients with confirmed or probable schwannomatosis seen in two U.S. tertiary care clinics from 2005-2016. Survival analysis revealed a median time from first symptom to diagnosis of 16.7 years (95% CI, 7.5-26.0 years) and median time from first medical consultation to diagnosis of 9.8 years (95% CI, 3.5-16.2 years). Factors associated with longer times to diagnosis included initial signs/symptoms that were intermittent, non-specific, or occurred at younger ages (p<0.05). Thirty-six percent of patients experienced a misdiagnosis of underlying genetic condition (18.6%), pain etiology (16.5%) and/or tumor imaging/pathology (11.3%). One-fifth (19.6%) of patients had a clear missed opportunity for appropriate workup that could have led to an earlier schwannomatosis diagnosis. These results suggest that interventions in clinician education, genetic testing availability, expert review of pathology findings, and automatic triggers for genetics referrals may improve diagnosis in schwannomatosis.

Describing Vocalizations in Young Children: A Big Data Approach Through Citizen Science Annotation

Purpose Recording young children's vocalizations through wearables is a promising method to assess language development. However, accurately and rapidly annotating these files remains challenging. Online crowdsourcing with the collaboration of citizen scientists could be a feasible solution. In this article, we assess the extent to which citizen scientists' annotations align with those gathered in the lab for recordings collected from young children. Method Segments identified by Language ENvironment Analysis as produced by the key child were extracted from one daylong recording for each of 20 participants: 10 low-risk control children and 10 children diagnosed with Angelman syndrome, a neurogenetic syndrome characterized by severe language impairments. Speech samples were annotated by trained annotators in the laboratory as well as by citizen scientists on Zooniverse. All annotators assigned one of five labels to each sample: Canonical, Noncanonical, Crying, Laughing, and Junk. This allowed the derivation of two child-level vocalization metrics: the Linguistic Proportion and the Canonical Proportion. Results At the segment level, Zooniverse classifications had moderate precision and recall. More importantly, the Linguistic Proportion and the Canonical Proportion derived from Zooniverse annotations were highly correlated with those derived from laboratory annotations. Conclusions Annotations obtained through a citizen science platform can help us overcome challenges posed by the process of annotating daylong speech recordings. Particularly when used in composites or derived metrics, such annotations can be used to investigate early markers of language delays.

Describing vocalizations in young children: A big data approach through citizen science annotation

Recording young children's vocalizations through wearables is a promising method. However, accurately and rapidly annotating these files remains challenging. Online crowdsourcing with the collaboration of citizen scientists could be a feasible solution. In this paper, we assess the extent to which citizen scientists' annotations align with those gathered in the lab for recordings collected from young children. Segments identified by LENA^TM^ as produced by the key child were extracted from one daylong recording for each of 20 participants: 10 low-risk control children and 10 children diagnosed with Angelman syndrome, a neurogenetic syndrome characterized by severe language impairments. Speech samples were annotated by trained annotators in the laboratory as well as by citizen scientists on Zooniverse. All annotators assigned one of five labels to each sample: Canonical, Non-Canonical, Crying, Laughing, and Junk. This allowed the derivation of two child-level vocalization metrics: the Linguistic Proportion, and the Canonical Proportion. At the segment level, Zooniverse classifications had moderate precision and recall. More importantly, the Linguistic Proportion and the Canonical Proportion derived from Zooniverse annotations were highly correlated with those derived from laboratory annotations. Annotations obtained through a citizen science platform can help us overcome challenges posed by the process of annotating daylong speech recordings. Particularly when used in composites or derived metrics, such annotations can be used to investigate early markers of language delays in non-typically developing children.