The lifestyle of modern society has changed significantly with the emergence of artificial intelligence (AI), machine learning (ML), and deep learning (DL) technologies in recent years. Artificial intelligence is a multidimensional technology with various components such as advanced algorithms, ML and DL. Together, AI, ML, and DL are expected to provide automated devices to ophthalmologists for early diagnosis and timely treatment of ocular disorders in the near future. In fact, AI, ML, and DL have been used in ophthalmic setting to validate the diagnosis of diseases, read images, perform corneal topographic mapping and intraocular lens calculations. Diabetic retinopathy (DR), age-related macular degeneration (AMD), and glaucoma are the 3 most common causes of irreversible blindness on a global scale. Ophthalmic imaging provides a way to diagnose and objectively detect the progression of a number of pathologies including DR, AMD, glaucoma, and other ophthalmic disorders. There are 2 methods of imaging used as diagnostic methods in ophthalmic practice: fundus digital photography and optical coherence tomography (OCT). Of note, OCT has become the most widely used imaging modality in ophthalmology settings in the developed world. Changes in population demographics and lifestyle, extension of average lifespan, and the changing pattern of chronic diseases such as obesity, diabetes, DR, AMD, and glaucoma create a rising demand for such images. Furthermore, the limitation of availability of retina specialists and trained human graders is a major problem in many countries. Consequently, given the current population growth trends, it is inevitable that analyzing such images is time-consuming, costly, and prone to human error. Therefore, the detection and treatment of DR, AMD, glaucoma, and other ophthalmic disorders through unmanned automated applications system in the near future will be inevitable. We provide an overview of the potential impact of the current AI, ML, and DL methods and their applications on the early detection and treatment of DR, AMD, glaucoma, and other ophthalmic diseases.

Promising Artificial Intelligence-Machine Learning-Deep Learning Algorithms in Ophthalmology.

Host-tumor relationships involve several factors that can enhance or suppress neoplastic growth. This second part of a review of basal cell carcinoma biology examines the role that hormones, cytokines, local and systemic immunity, congenital and genetic syndromes, and environmental factors play in the development of this neoplasm. Theories of etiology and pathogenesis are discussed, and transplantation and cell culture techniques used to study this cancer are explored. Valuable second-line therapies for treatment of multiple tumors are reviewed, and important areas of present and future research are emphasized.

Biology of Basal Cell Carcinoma (Part I)

Purpose of reviewTo describe the emerging applications of deep learning in ophthalmology.Recent findingsRecent studies have shown that various deep learning models are capable of detecting and diagnosing various diseases afflicting the posterior segment of the eye with high accuracy. Most of the ini

Deep learning applications in ophthalmology.

Introduction Basal cell carcinomas (BCCs) are the most frequently diagnosed type of skin cancer, with eyelid (including canthus) BCCs accounting for a notable proportion of these. Using population-based data from the English Cancer Registries, we report here the incidence of eyelid BCCs in England, for the period 2000–2010. Methods ICD-10 and histology codes for eyelid BCCs (including canthus) from the English National Cancer Data Repository were used to identify incident events. Crude incidence rates by age and sex, together with directly standardised incidence rates for eyelid BCCs in England in 3-year cohorts, are presented, in keeping with the reporting practice of the English Cancer Registries. Results Over the 11-year study period, there were a total of 33 610 recorded eyelid BCCs; 18 146 in females and 15 464 in males. There were regional variations in registrations. Incidence of eyelid BCCs increased with age. No major change in the age-standardised incidence of BCC was observed during the period 2000–2010. Overall, the age-standardised incidence of BCCs during 2008–2010 was similar for males and females (4.51 per 100 000 (95% CI 4.37 to 4.65) and 4.53 per 100 000 (95% CI 4.40 to 4.67), respectively). However, females under 50 years of age had higher incidence rates, and males over 75 years of age had higher rates. Conclusions The findings provide the current frequency and distribution of eyelid BCCs in England, highlighting opportunities for health education and improving reporting and registration of events, and for informing service planning.

https://bjo.bmj.com/content/bjophthalmol/101/2/209.full.pdf

Incidence of eyelid basal cell carcinoma in England: 2000–2010

Artificial intelligence (AI) and machine learning (ML) have entered several avenues of modern life, and health care is just one of them. Ophthalmology is a field with a lot of imaging and measurable data, thus ideal for application of AI and ML. Many of these are still in research stage, but show promising results. The ophthalmic diseases where AI is being used are diabetic retinopathy, glaucoma, age-related macular degeneration, retinopathy of prematurity, retinal vascular occlusions, keratoconus, cataract, refractive errors, retinal detachment, squint, and ocular cancers. It is also useful for intraocular lens power calculation, planning squint surgeries, and planning intravitreal antivascular endothelial growth factor injections. In addition, AI can detect cognitive impairment, dementia, Alzheimer's disease, stroke risk, and so on from fundus photographs and optical coherence tomography. We will surely see many more innovations in this rapidly growing field.

Role of artificial intelligence and machine learning in ophthalmology

Summary
Background

Basal cell carcinomas (BCCs) are generally regarded as slow-growing tumours. There is a paucity of data on the rate of BCC growth and the impact of delayed excision.



Objectives

To measure the growth rate and assess the impact of delayed excision on the growth of periocular BCC (pBCC).



Methods

Patients referred to an oculoplastic service for excision of pBCC were recruited. The tumour dimensions and patient demographic data were recorded at the first specialist appointment (FSA). Measurement of the pBCC was repeated when the patient attended for tumour excision by Mohs micrographic surgery (MMS). Correlation analyses were performed to determine whether the histological subtype and patient factors affected the pBCC growth rates.



Results

The study included 112 patients and 115 pBCCs. The primary ethnicity was European with Fitzpatrick type I and II skin. The mean size of the pBCC at FSA was 8 × 6 mm (range 6–12 × 4–8 mm) with a mean area of 68·5 mm2. The average waiting time for MMS was 157 ± 87 days. The pBCCs grew at a mean rate of 11·2 mm2 every 30 days. From the FSA to the MMS, a mean increase of 41·9 mm2 was observed. Recurrent tumours, larger tumours at presentation and male sex were associated with a faster growth rate.



Conclusions

Periocular basal cell carcinomas can grow rapidly, and many have aggressive histological subtypes. Rapid growth is more likely in recurrent tumours, larger tumours and in men.

Growth of periocular basal cell carcinomas.

Purpose
To derive a decision rule for predicting surgical complexity in periorbital basal cell carcinoma (pBCC).

Design
Prospective, cohort study.

Participants
Patients referred to an oculoplastic service for excision of pBCC from September 2010 to November 2015

Methods
This study was conducted at two centres in New Zealand from September 2010 to November 2015. Baseline demographic information, and an initial assessment of operative complexity (a four-point grading scale) were collected. Assessment of operative complexity was repeated at the time of reconstruction. Univariate analysis was applied to identify the associative factors and supervised machine learning was used to determine the best predictive models to construct a clinical decision rule.

Main outcome measures
Pre- and post-operative surgical complexity.

Results
156 patients and 156 periocular BCC were analysed. Univariate analysis revealed that older age, recurrent skin cancer, large, tumour size, being a public patient, and high complexity at preoperative assessment were associated with high actual operative complexity. Tumour histology was not associated with more complex surgery. Machine learning analyses revealed that Naive Bayesian classifier was able to distinguish surgical complexity with an average area under the receiver operating characteristic curve (AUC) of 0.854 (95% C.I. 0.762-0.946) whereas a simpler, alternating decision tree (ADT) that used only three clinical variables achieved an AUC of 0.853 (95% C.I. 0.739-0.931). The ADT model was 10.1 times more likely to correctly identify a high complexity case. The three predictive variables were pre-operative assessment of complexity (high versus low), surgical delays [early (<75 days) or delayed (≥75 days)], and tumour size [small (<14mm), or large (≥14 mm)]. For the subgroup with large tumours but low initial assessed complexity, late surgery was associated with a 6.7-fold increase in risk of high-risk surgery.

Conclusions
A simple, three-variable risk stratification system was able to predict the operative complexity of pBCC.

This article is protected by copyright. All rights reserved.

A practical decision-tree model to predict complexity of reconstructive surgery after periocular basal cell carcinoma excision

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Growth of periocular basal cell carcinomas.

A practical decision-tree model to predict complexity of reconstructive surgery after periocular basal cell carcinoma excision