Clinician-driven artificial intelligence in ophthalmology: resources enabling democratization

PURPOSE OF REVIEW: This article aims to discuss the current state of resources enabling the democratization of artificial intelligence (AI) in ophthalmology. RECENT FINDINGS: Open datasets, efficient labeling techniques, code-free automated machine learning (AutoML) and cloud-based platforms for deployment are resources that enable clinicians with scarce resources to drive their own AI projects. SUMMARY: Clinicians are the use-case experts who are best suited to drive AI projects tackling patient-relevant outcome measures. Taken together, open datasets, efficient labeling techniques, code-free AutoML and cloud platforms break the barriers for clinician-driven AI. As AI becomes increasingly democratized through such tools, clinicians and patients stand to benefit greatly

An In-Depth Statistical Review of Retinal Image Processing Models from a Clinical Perspective

The burgeoning field of retinal image processing is critical in facilitating early diagnosis and treatment of retinal diseases, which are amongst the leading causes of vision impairment globally. Despite rapid advancements, existing machine learning models for retinal image processing are characterized by significant limitations, including disparities in pre-processing, segmentation, and classification methodologies, as well as inconsistencies in post-processing operations. These limitations hinder the realization of accurate, reliable, and clinically relevant outcomes. This paper provides an in-depth statistical review of extant machine learning models used in retinal image processing, meticulously comparing them based on their internal operating characteristics and performance levels. By adopting a robust analytical approach, our review delineates the strengths and weaknesses of current models, offering comprehensive insights that are instrumental in guiding future research and development in this domain. Furthermore, this review underscores the potential clinical impacts of these models, highlighting their pivotal role in enhancing diagnostic accuracy, prognostic assessments, and therapeutic interventions for retinal disorders. In conclusion, our work not only bridges the existing knowledge gap in the literature but also paves the way for the evolution of more sophisticated and clinically-aligned retinal image processing models, ultimately contributing to improved patient outcomes and advancements in ophthalmic care

Towards Unsupervised Domain Adaptation for Diabetic Retinopathy Detection in the Tromsø Eye Study

Diabetic retinopathy (DR) is an eye disease which affects a third of the diabetic population. It is a preventable disease, but requires early detection for efficient treatment. While there has been increasing interest in applying deep learning techniques for DR detection in order to aid practitioners make more accurate diagnosis, these efforts are mainly focused on datasets that have been collected or created with ML in mind. In this thesis, however, we take a look at two particular datasets that have been collected at the University Hospital of North-Norway - UNN. These datasets have inherent problems that motivate the methodological choices in this work such as a variable number of input images and domain shift. We therefore contribute a multi-stream model for DR classification. The multi-stream model can model dependency across different images, can take in a variable of input of any size, is general in its detection such that the image processing is equal no matter which stream the image enters, and is compatible with the domain adaptation method ADDA, but we argue the model is compatible with many other methods. As a remedy for these problems, we propose a multi-stream deep learning architecture that is uniquely tailored to these datasets and illustrate how domain adaptation might be utilized within the framework to learn efficiently in the presence of domain shift. Our experiments demonstrates the models properties empirically, and shows it can deal with each of the presented problems. The model this paper contributes is a first step towards DR detection from these local datasets and, in the bigger picture, similar datasets worldwide

Open-Set Source-Free Domain Adaptation in Fundus Images Analysis

Unsupervised domain adaptation (UDA) is crucial in medical image analysis where only the source domain data is labeled. There is a lot of emphasis on the closed-set paradigm in UDA, where the label space is assumed to be the same in all domains. However, medical imaging often has an open-world scenario where the source domain has a limited number of disease categories and the target domain has unknown distinct classes. Also, maintaining the privacy of patients is a crucial aspect of medical research and practice. In this work, we shed light on the Open-Set Domain Adaptation (OSDA) setting on fundus image analysis while preserving the privacy concern. In particular, we step towards a source-free open-set domain adaptation where, without source data, the source model is utilized to facilitate adaptation to open-set unlabeled data by delving into channel-wise and local features for fundus disease recognition. In particular, considering the nature of the fundus images, we present a novel objective way in the adaptation phase to utilize spatial and channel-wise information to select the best source model for a target domain, even by considering the small inter-class variation between samples. Our approach has achieved state-of-the-art performance compared to other methods