Artificial neural networks (ANN) provide a range of powerful new techniques for solving problems in sensor data analysis, fault detection, process identification, and control and have been used in a diverse range of chemical engineering applications. This paper aims to provide a comprehensive review of various ANN applications within the field of chemical engineering (CE). It deals with the significant aspects of ANN (architecture, methods of developing and training, and modeling strategies) in correlation with various types of applications. A systematic classification scheme is also presented, which uncovers, classifies, and interprets the existing works related to the ANN methodologies and applications within the CE domain. Based on this scheme, 717 scholarly papers from 169 journals are categorized into specific application areas and general (other) applications, including the following topics: petrochemicals, oil and gas industry, biotechnology, cellular industry, environment, health and safety, fuel and energy, mineral industry, nanotechnology, pharmaceutical industry, and polymer industry. It is hoped that this paper will serve as a comprehensive state-of-the-art reference for chemical engineers besides highlighting the potential applications of ANN in CE-related problems and consequently enhancing the future ANN research in CE field.

Background: Having several applications in medicine, and in ophthalmology in particular, artificial intelligence (AI) tools have been used to detect visual function deficits, thus playing a key role in diagnosing eye diseases and in predicting the evolution of these common and disabling diseases. AI tools, i.e., artificial neural networks (ANNs), are progressively involved in detecting and customized control of ophthalmic diseases. The studies that refer to the efficiency of AI in medicine and especially in ophthalmology were analyzed in this review. Materials and Methods: We conducted a comprehensive review in order to collect all accounts published between 2015 and 2022 that refer to these applications of AI in medicine and especially in ophthalmology. Neural networks have a major role in establishing the demand to initiate preliminary anti-glaucoma therapy to stop the advance of the disease. Results: Different surveys in the literature review show the remarkable benefit of these AI tools in ophthalmology in evaluating the visual field, optic nerve, and retinal nerve fiber layer, thus ensuring a higher precision in detecting advances in glaucoma and retinal shifts in diabetes. We thus identified 1762 applications of artificial intelligence in ophthalmology: review articles and research articles (301 pub med, 144 scopus, 445 web of science, 872 science direct). Of these, we analyzed 70 articles and review papers (diabetic retinopathy (N = 24), glaucoma (N = 24), DMLV (N = 15), other pathologies (N = 7)) after applying the inclusion and exclusion criteria. Conclusion: In medicine, AI tools are used in surgery, radiology, gynecology, oncology, etc., in making a diagnosis, predicting the evolution of a disease, and assessing the prognosis in patients with oncological pathologies. In ophthalmology, AI potentially increases the patient’s access to screening/clinical diagnosis and decreases healthcare costs, mainly when there is a high risk of disease or communities face financial shortages. AI/DL (deep learning) algorithms using both OCT and FO images will change image analysis techniques and methodologies. Optimizing these (combined) technologies will accelerate progress in this area.