Ciara O'Byrne1,2, Abdallah Abbas1,3, Edward Korot1,4, Pearse A Keane1,5. 1. Medical Retina Department, Moorfields Eye Hospital NHS Foundation Trust, London, UK. 2. Trinity College School of Medicine, Dublin, Ireland. 3. University College London Medical School, London, UK. 4. Byers Eye Institute, Stanford University, Stanford, California, USA. 5. NIHR Biomedical Research Centre for Ophthalmology, Moorfields Eye Hospital NHS Foundation, London, UK.
Abstract
PURPOSE OF REVIEW: The purpose of this review is to describe the current status of automated deep learning in healthcare and to explore and detail the development of these models using commercially available platforms. We highlight key studies demonstrating the effectiveness of this technique and discuss current challenges and future directions of automated deep learning. RECENT FINDINGS: There are several commercially available automated deep learning platforms. Although specific features differ between platforms, they utilise the common approach of supervised learning. Ophthalmology is an exemplar speciality in the area, with a number of recent proof-of-concept studies exploring classification of retinal fundus photographs, optical coherence tomography images and indocyanine green angiography images. Automated deep learning has also demonstrated impressive results in other specialities such as dermatology, radiology and histopathology. SUMMARY: Automated deep learning allows users without coding expertise to develop deep learning algorithms. It is rapidly establishing itself as a valuable tool for those with limited technical experience. Despite residual challenges, it offers considerable potential in the future of patient management, clinical research and medical education. VIDEO ABSTRACT: http://links.lww.com/COOP/A44.
PURPOSE OF REVIEW: The purpose of this review is to describe the current status of automated deep learning in healthcare and to explore and detail the development of these models using commercially available platforms. We highlight key studies demonstrating the effectiveness of this technique and discuss current challenges and future directions of automated deep learning. RECENT FINDINGS: There are several commercially available automated deep learning platforms. Although specific features differ between platforms, they utilise the common approach of supervised learning. Ophthalmology is an exemplar speciality in the area, with a number of recent proof-of-concept studies exploring classification of retinal fundus photographs, optical coherence tomography images and indocyanine green angiography images. Automated deep learning has also demonstrated impressive results in other specialities such as dermatology, radiology and histopathology. SUMMARY: Automated deep learning allows users without coding expertise to develop deep learning algorithms. It is rapidly establishing itself as a valuable tool for those with limited technical experience. Despite residual challenges, it offers considerable potential in the future of patient management, clinical research and medical education. VIDEO ABSTRACT: http://links.lww.com/COOP/A44.
Authors: Josef Huemer; Martin Kronschläger; Manuel Ruiss; Dawn Sim; Pearse A Keane; Oliver Findl; Siegfried K Wagner Journal: BMJ Open Ophthalmol Date: 2022-05-23
Authors: Fabio Daniel Padilla-Pantoja; Yeison D Sanchez; Bernardo Alfonso Quijano-Nieto; Oscar J Perdomo; Fabio A Gonzalez Journal: Transl Vis Sci Technol Date: 2022-09-01 Impact factor: 3.048