6 research outputs found
Chronic pulmonary aspergillosis – a guide for the general physician
This collaborative article presents a review of chronic pulmonary aspergillosis (CPA) from the perspective of a multidisciplinary team comprising of respiratory physicians, radiologists, mycologists, dietitians, pharmacists, physiotherapists and palliative care specialists. The review synthesises current knowledge on CPA, emphasising the intricate interplay between clinical, radiological, and microbiological aspects. We highlight the importance of assessing each patient as multidisciplinary team to ensure personalised treatment strategies and a holistic approach to patient care.</p
Development and international validation of custom-engineered and code-free deep-learning models for detection of plus disease in retinopathy of prematurity: a retrospective study
BACKGROUND: Retinopathy of prematurity (ROP), a leading cause of childhood blindness, is diagnosed through interval screening by paediatric ophthalmologists. However, improved survival of premature neonates coupled with a scarcity of available experts has raised concerns about the sustainability of this approach. We aimed to develop bespoke and code-free deep learning-based classifiers for plus disease, a hallmark of ROP, in an ethnically diverse population in London, UK, and externally validate them in ethnically, geographically, and socioeconomically diverse populations in four countries and three continents. Code-free deep learning is not reliant on the availability of expertly trained data scientists, thus being of particular potential benefit for low resource health-care settings. METHODS: This retrospective cohort study used retinal images from 1370 neonates admitted to a neonatal unit at Homerton University Hospital NHS Foundation Trust, London, UK, between 2008 and 2018. Images were acquired using a Retcam Version 2 device (Natus Medical, Pleasanton, CA, USA) on all babies who were either born at less than 32 weeks gestational age or had a birthweight of less than 1501 g. Each images was graded by two junior ophthalmologists with disagreements adjudicated by a senior paediatric ophthalmologist. Bespoke and code-free deep learning models (CFDL) were developed for the discrimination of healthy, pre-plus disease, and plus disease. Performance was assessed internally on 200 images with the majority vote of three senior paediatric ophthalmologists as the reference standard. External validation was on 338 retinal images from four separate datasets from the USA, Brazil, and Egypt with images derived from Retcam and the 3nethra neo device (Forus Health, Bengaluru, India). FINDINGS: Of the 7414 retinal images in the original dataset, 6141 images were used in the final development dataset. For the discrimination of healthy versus pre-plus or plus disease, the bespoke model had an area under the curve (AUC) of 0·986 (95% CI 0·973-0·996) and the CFDL model had an AUC of 0·989 (0·979-0·997) on the internal test set. Both models generalised well to external validation test sets acquired using the Retcam for discriminating healthy from pre-plus or plus disease (bespoke range was 0·975-1·000 and CFDL range was 0·969-0·995). The CFDL model was inferior to the bespoke model on discriminating pre-plus disease from healthy or plus disease in the USA dataset (CFDL 0·808 [95% CI 0·671-0·909, bespoke 0·942 [0·892-0·982]], p=0·0070). Performance also reduced when tested on the 3nethra neo imaging device (CFDL 0·865 [0·742-0·965] and bespoke 0·891 [0·783-0·977]). INTERPRETATION: Both bespoke and CFDL models conferred similar performance to senior paediatric ophthalmologists for discriminating healthy retinal images from ones with features of pre-plus or plus disease; however, CFDL models might generalise less well when considering minority classes. Care should be taken when testing on data acquired using alternative imaging devices from that used for the development dataset. Our study justifies further validation of plus disease classifiers in ROP screening and supports a potential role for code-free approaches to help prevent blindness in vulnerable neonates
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‘It would help people to help me’: Acceptability of digital phenotyping among young people with visual impairment and their families
Objectives To explore the acceptability of an eHealth App for vision-related monitoring and symptom reporting among young people with a visual impairment and their parents. Methods Qualitative investigation using virtual semi-structured focus groups (via Zoom software) of seven young participants with a genetic eye disorder including inherited retinal disease and structural eye abnormalities (e.g. microphthalmia), and 7 parents; all recruited from ocular genetic clinics at Moorfields Eye Hospital. Audio transcripts were analysed using thematic analysis. Results Data were coded into six key themes: (1) increased involvement in care, (2) opportunity for less hospital-centric care, (3) better representation of visual impairment in a real-world setting, (4) trust in a reputable service provider, (5) harnessing data for health purposes and (6) intended purpose of the app. Both young people and their families were accepting of an eHealth app and felt they would be empowered by greater involvement in their care plan, if privacy of the data was retained, and information was managed correctly. While parents endorsed the opportunity for mental health tracking, young people were hesitant towards its inclusion. Conclusion In summary, there was overall acceptability of an eHealth app among young people with a visual impairment and their parents. These findings will help to maximise the effective integration of digital phenotyping when monitoring and supporting young people experiencing sight loss