ORCID Profile
0000-0002-7813-5023
Current Organisations
University of Erlangen-Nuremberg
,
Imperial College London
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Publisher: Wiley
Date: 30-09-2023
DOI: 10.1002/PD.6445
Publisher: Wiley
Date: 18-10-2022
DOI: 10.1002/PD.6059
Abstract: Advances in artificial intelligence (AI) have demonstrated potential to improve medical diagnosis. We piloted the end‐to‐end automation of the mid‐trimester screening ultrasound scan using AI‐enabled tools. A prospective method comparison study was conducted. Participants had both standard and AI‐assisted US scans performed. The AI tools automated image acquisition, biometric measurement, and report production. A feedback survey captured the sonographers' perceptions of scanning. Twenty‐three subjects were studied. The average time saving per scan was 7.62 min (34.7%) with the AI‐assisted method ( p 0.0001). There was no difference in reporting time. There were no clinically significant differences in biometric measurements between the two methods. The AI tools saved a satisfactory view in 93% of the cases (four core views only), and 73% for the full 13 views, compared to 98% for both using the manual scan. Survey responses suggest that the AI tools helped sonographers to concentrate on image interpretation by removing disruptive tasks. Separating freehand scanning from image capture and measurement resulted in a faster scan and altered workflow. Removing repetitive tasks may allow more attention to be directed identifying fetal malformation. Further work is required to improve the image plane detection algorithm for use in real time.
Publisher: Springer International Publishing
Date: 2020
Publisher: Springer International Publishing
Date: 2020
Publisher: Springer Science and Business Media LLC
Date: 29-03-2021
DOI: 10.1038/S41746-021-00431-6
Abstract: Data privacy mechanisms are essential for rapidly scaling medical training databases to capture the heterogeneity of patient data distributions toward robust and generalizable machine learning systems. In the current COVID-19 pandemic, a major focus of artificial intelligence (AI) is interpreting chest CT, which can be readily used in the assessment and management of the disease. This paper demonstrates the feasibility of a federated learning method for detecting COVID-19 related CT abnormalities with external validation on patients from a multinational study. We recruited 132 patients from seven multinational different centers, with three internal hospitals from Hong Kong for training and testing, and four external, independent datasets from Mainland China and Germany, for validating model generalizability. We also conducted case studies on longitudinal scans for automated estimation of lesion burden for hospitalized COVID-19 patients. We explore the federated learning algorithms to develop a privacy-preserving AI model for COVID-19 medical image diagnosis with good generalization capability on unseen multinational datasets. Federated learning could provide an effective mechanism during pandemics to rapidly develop clinically useful AI across institutions and countries overcoming the burden of central aggregation of large amounts of sensitive data.
Publisher: Springer Science and Business Media LLC
Date: 24-04-2023
Publisher: Springer Science and Business Media LLC
Date: 24-04-2022
Publisher: Wiley
Date: 02-02-2023
DOI: 10.1002/PD.6287
Publisher: Wiley
Date: 05-02-2021
DOI: 10.1002/PD.5892
Abstract: There has been a recent explosion in the use of artificial intelligence (AI), which is now part of our everyday lives. Uptake in medicine has been more limited, although in several fields there have been encouraging results showing excellent performance when AI is used to assist in a well‐defined medical task. Most of this work has been performed using retrospective data, and there have been few clinical trials published using prospective data. This review focuses on the potential uses of AI in the field of fetal cardiology. Ultrasound of the fetal heart is highly specific and sensitive in experienced hands, but despite this there is significant room for improvement in the rates of prenatal diagnosis of congenital heart disease in most countries. AI may be one way of improving this. Other potential applications in fetal cardiology include the provision of more accurate prognoses for in iduals, and automatic quantification of various metrics including cardiac function. However, there are also ethical and governance concerns. These will need to be overcome before AI can be widely accepted in mainstream use. It is likely that a familiarity of the uses, and pitfalls, of AI will soon be mandatory for many healthcare professionals working in fetal cardiology.
Publisher: Wiley
Date: 31-07-2023
DOI: 10.1002/UOG.26238
Location: United Kingdom of Great Britain and Northern Ireland
Location: United Kingdom of Great Britain and Northern Ireland
No related grants have been discovered for Bernhard Kainz.