Strengthening retinopathy of prematurity screening and treatment services in Nigeria: a case study of activities, challenges and outcomes 2017-2020.

OBJECTIVES: Retinopathy of prematurity (ROP) will become a major cause of blindness in Nigerian children unless screening and treatment services expand. This article aims to describe the collaborative activities undertaken to improve services for ROP between 2017 and 2020 as well as the outcome of these activities in Nigeria. DESIGN: Descriptive case study. SETTING: Neonatal intensive care units in Nigeria. PARTICIPANTS: Staff providing services for ROP, and 723 preterm infants screened for ROP who fulfilled screening criteria (gestational age <34 weeks or birth weight ≤2000 g, or sickness criteria). METHODS AND ANALYSIS: A WhatsApp group was initiated for Nigerian ophthalmologists and neonatologists in 2018. Members participated in a range of capacity-building, national and international collaborative activities between 2017 and 2018. A national protocol for ROP was developed for Nigeria and adopted in 2018; 1 year screening outcome data were collected and analysed. In 2019, an esurvey was used to collect service data from WhatsApp group members for 2017-2018 and to assess challenges in service provision. RESULTS: In 2017 only six of the 84 public neonatal units in Nigeria provided ROP services; this number had increased to 20 by 2018. Of the 723 babies screened in 10 units over a year, 127 (17.6%) developed any ROP; and 29 (22.8%) developed type 1 ROP. Only 13 (44.8%) babies were treated, most by intravitreal bevacizumab. The screening criteria were revised in 2020. Challenges included lack of equipment to regulate oxygen and to document and treat ROP, and lack of data systems. CONCLUSION: ROP screening coverage and quality improved after national and international collaborative efforts. To scale up and improve services, equipment for neonatal care and ROP treatment is urgently needed, as well as systems to monitor data. Ongoing advocacy is also essential

Electroencephalogram-based decoding cognitive states using convolutional neural network and likelihood ratio based score fusion

<div><p>Electroencephalogram (EEG)-based decoding human brain activity is challenging, owing to the low spatial resolution of EEG. However, EEG is an important technique, especially for brain–computer interface applications. In this study, a novel algorithm is proposed to decode brain activity associated with different types of images. In this hybrid algorithm, convolutional neural network is modified for the extraction of features, a t-test is used for the selection of significant features and likelihood ratio-based score fusion is used for the prediction of brain activity. The proposed algorithm takes input data from multichannel EEG time-series, which is also known as multivariate pattern analysis. Comprehensive analysis was conducted using data from 30 participants. The results from the proposed method are compared with current recognized feature extraction and classification/prediction techniques. The wavelet transform-support vector machine method is the most popular currently used feature extraction and prediction method. This method showed an accuracy of 65.7%. However, the proposed method predicts the novel data with improved accuracy of 79.9%. In conclusion, the proposed algorithm outperformed the current feature extraction and prediction method.</p></div

Performance for all categories using the proposed method (ConvNet & LRBSF).

<p>Best and worst performance of individual participant is also mentioned.</p

One-vs-one decoding accuracy among all participants for different conditions.

<p>Average chance level and permutation based chance level (upper boundary of a 95% confidence interval for chance based on a permutation test) are shown along with the accuracy found using proposed method.</p

Block diagram of proposed and compared methods for brain decoding.

<p>The proposed method is shown in the green boxes while compared methods are shown in blue boxes.</p

Multiclass accuracy distribution of the permutation based estimation of the chance level for decoding.

<p>Multiclass accuracy distribution of the permutation based estimation of the chance level for decoding.</p