Identifying Peripheral Neuropathy in Colour Fundus Photographs Based on Deep Learning

Ocular images-based artificial intelligence on systemic diseases

PURPOSE

To provide a summary of the research advances on ocular images-based artificial intelligence on systemic diseases.

METHODS

Narrative literature review.

RESULTS

Ocular images-based artificial intelligence has been used in a variety of systemic diseases, including endocrine, cardiovascular, neurological, renal, autoimmune, and hematological diseases, and many others. However, the studies are still at an early stage. The majority of studies have used AI only for diseases diagnosis, and the specific mechanisms linking systemic diseases to ocular images are still unclear. In addition, there are many limitations to the research, such as the number of images, the interpretability of artificial intelligence, rare diseases, and ethical and legal issues.

CONCLUSION

While ocular images-based artificial intelligence is widely used, the relationship between the eye and the whole body should be more clearly elucidated.

Diabetic retinopathy relates to the incidence of foot ulcers and amputations in type 2 diabetes

AIMS

We investigated whether Diabetic Retinopathy (DR) is related to Diabetic Foot Ulcer (DFU) development, adjusted for the stratification of the International Work Group on Diabetic Foot (IWGDF) guidance.

MATERIALS AND METHODS

DR and IWGDF stratification was registered retrospectively in patients hospitalised from 2009 to 2017 for uncontrolled and/or complicated type 2 diabetes. New DFUs were registered until 2020. Survival analyses categorised the subjects for DR, and multivariate Cox regression adjusted for confounders.

RESULTS

The 522 patients (57.9% male) were 62 ± 9 years old with a diabetes duration of 14 ± 10 years, HbA1c of 8.7 ± 1.8%, 33.9% macroangiopathies and 44.8% diabetic kidney diseases. Their grades of DFU risk were 0 for 43.3%, 1 for 23.9%, 2 for 7.1%, and 3 for 25.6%. During the 52 months follow-up (Inter Quartile Range: 32-71), 58 new DFUs and 18 lower-limb amputations occurred, mostly in patients with DR present in 140 (26.8%) patients. Adjusted for age, sex and conventional risk factors (duration and control of diabetes, arterial hypertension, and dyslipidemia), and other complications (macroangiopathy and diabetic kidney disease), DR was associated with a greater incidence of DFUs. Adjusted for the IWGDF classification, DR was related to new DFUs (HR: 2.51, 95%Confidence Interval [CI]: 1.48-4.26) and amputations (HR: 3.56, 95%CI: 1.26-10.07). This relationship persisted in ascending IWGDF grades with incidences of DFUs from 2/1000 (grade 0, no DR) to 121/1000 patient-years (grade 3 and DR) and amputations from 0 (grade 0, no DR) to 38/1000 patient-years (grade 3 and DR).

CONCLUSIONS

Diabetic retinopathy independently relates to the incidence of foot ulcers and amputations in patients hospitalised for type 2 diabetes.

Deep Learning Algorithms for Screening and Diagnosis of Systemic Diseases Based on Ophthalmic Manifestations: A Systematic Review

Deep learning (DL) is the new high-profile technology in medical artificial intelligence (AI) for building screening and diagnosing algorithms for various diseases. The eye provides a window for observing neurovascular pathophysiological changes. Previous studies have proposed that ocular manifestations indicate systemic conditions, revealing a new route in disease screening and management. There have been multiple DL models developed for identifying systemic diseases based on ocular data. However, the methods and results varied immensely across studies. This systematic review aims to summarize the existing studies and provide an overview of the present and future aspects of DL-based algorithms for screening systemic diseases based on ophthalmic examinations. We performed a thorough search in PubMed^®, Embase, and Web of Science for English-language articles published until August 2022. Among the 2873 articles collected, 62 were included for analysis and quality assessment. The selected studies mainly utilized eye appearance, retinal data, and eye movements as model input and covered a wide range of systemic diseases such as cardiovascular diseases, neurodegenerative diseases, and systemic health features. Despite the decent performance reported, most models lack disease specificity and public generalizability for real-world application. This review concludes the pros and cons and discusses the prospect of implementing AI based on ocular data in real-world clinical scenarios.

Recent trends and advances in fundus image analysis: A review

Automated retinal image analysis holds prime significance in the accurate diagnosis of various critical eye diseases that include diabetic retinopathy (DR), age-related macular degeneration (AMD), atherosclerosis, and glaucoma. Manual diagnosis of retinal diseases by ophthalmologists takes time, effort, and financial resources, and is prone to error, in comparison to computer-aided diagnosis systems. In this context, robust classification and segmentation of retinal images are primary operations that aid clinicians in the early screening of patients to ensure the prevention and/or treatment of these diseases. This paper conducts an extensive review of the state-of-the-art methods for the detection and segmentation of retinal image features. Existing notable techniques for the detection of retinal features are categorized into essential groups and compared in depth. Additionally, a summary of quantifiable performance measures for various important stages of retinal image analysis, such as image acquisition and preprocessing, is provided. Finally, the widely used in the literature datasets for analyzing retinal images are described and their significance is emphasized.

Artificial Intelligence in Predicting Systemic Parameters and Diseases From Ophthalmic Imaging

Artificial Intelligence (AI) analytics has been used to predict, classify, and aid clinical management of multiple eye diseases. Its robust performances have prompted researchers to expand the use of AI into predicting systemic, non-ocular diseases and parameters based on ocular images. Herein, we discuss the reasons why the eye is well-suited for systemic applications, and review the applications of deep learning on ophthalmic images in the prediction of demographic parameters, body composition factors, and diseases of the cardiovascular, hematological, neurodegenerative, metabolic, renal, and hepatobiliary systems. Three main imaging modalities are included—retinal fundus photographs, optical coherence tomographs and external ophthalmic images. We examine the range of systemic factors studied from ophthalmic imaging in current literature and discuss areas of future research, while acknowledging current limitations of AI systems based on ophthalmic images.