Validation of the FASD-Tree as a screening tool for fetal alcohol spectrum disorders.

BACKGROUND: As many as 80% of individuals with fetal alcohol spectrum disorders (FASD) are misdiagnosed or not diagnosed. This study tests the accuracy and validity of a web-based screening tool (the FASD-Tree) for identifying children and adolescents with FASD. METHODS: Children with histories of prenatal alcohol exposure (PAE) and controls (N = 302, including 224 with PAE and 78 controls) were examined for physical signs of fetal alcohol syndrome (FAS), and parents completed behavioral questionnaires. Data were entered into the FASD-Tree, a web-based decision tree application. The FASD-Tree provided two outcomes: a dichotomous indicator (yes/no) and a numeric risk score (0 to 5), which have been shown separately to identify children with PAE and neurobehavioral impairment and to correlate with neurobehavioral outcomes. Overall accuracy (ACC), sensitivity, specificity, positive predictive value (PPV), and negative predictive value (NPV) were calculated for the decision tree, risk score, and their combination. Misclassified cases were examined for systematic bias. RESULTS: The FASD-Tree was successful in accurately identifying youth with histories of PAE and the subgroup of individuals with FASD, indicating its validity as an FASD screening tool. Overall accuracy rates for FASD-Tree components ranged from 75.0% to 84.1%, and both the decision tree outcome and risk score, and their combination, resulted in fair to good discrimination (area under the curve = 0.722 to 0.862) of youth with histories of PAE or FASD. While most participants were correctly classified, those who were misclassified differed in IQ and attention. Race, ethnicity, and sex did not affect the results. CONCLUSION: The FASD-Tree is not a biomarker of PAE and does not provide definitive evidence of prenatal alcohol exposure. Rather it is an accurate and valid screening tool for FASD and can be used by clinicians who suspect that a patient has a history of PAE, even if the exposure is unknown.

A Wearable, Extensible, Open-Source Platform for Hearing Healthcare Research.

Hearing loss is one of the most common conditions affecting older adults worldwide. Frequent complaints from the users of modern hearing aids include poor speech intelligibility in noisy environments and high cost, among other issues. However, the signal processing and audiological research needed to address these problems has long been hampered by proprietary development systems, underpowered embedded processors, and the difficulty of performing tests in real-world acoustical environments. To facilitate existing research in hearing healthcare and enable new investigations beyond what is currently possible, we have developed a modern, open-source hearing research platform, Open Speech Platform (OSP). This paper presents the system design of the complete OSP wearable platform, from hardware through firmware and software to user applications. The platform provides a complete suite of basic and advanced hearing aid features which can be adapted by researchers. It serves web apps directly from a hotspot on the wearable hardware, enabling users and researchers to control the system in real time. In addition, it can simultaneously acquire high-quality electroencephalography (EEG) or other electrophysiological signals closely synchronized to the audio. All of these features are provided in a wearable form factor with enough battery life for hours of operation in the field.

Pilot of a mobile money school fee payment system in rural Benin.

We present a rationale for, and results from, the pilot of a direct individual-to-institution remittance system in the context of school fee payment in rural Benin. Data confirm that school fees act as an impediment to educational attainment, and in very rural poor settings such as northern Benin, students often depend on extended family and kinship networks to pay fees. But existing remittance options are costly, in terms of fees, time, and risk. We pilot a new technology bundle in a single public high school in northeastern Benin, and evaluate its effectiveness. Here we describe the technical and institutional implementation of the project, as well as our findings from the first year of operation. We discuss takeaways and implications for scale-up.

A Wearable Platform for Research in Augmented Hearing.

We have previously reported a realtime, open-source speech-processing platform (OSP) for hearing aids (HAs) research. In this contribution, we describe a wearable version of this platform to facilitate audiological studies in the lab and in the field. The system is based on smartphone chipsets to leverage power efficiency in terms of FLOPS/watt and economies of scale. We present the system architecture and discuss salient design elements in support of HA research. The ear-level assemblies support up to 4 microphones on each ear, with 96 kHz, 24 bit codecs. The wearable unit runs OSP Release 2018c on top of 64-bit Debian Linux for binaural HA with an overall latency of 5.6 ms. The wearable unit also hosts an embedded web server (EWS) to monitor and control the HA state in realtime. We describe three example web apps in support of typical audiological studies they enable. Finally, we describe a baseline speech enhancement module included with Release 2018c, and describe extensions to the algorithms as future work.