Artificial Intelligence (AI) for Early Diagnosis of Retinal Diseases.

Journal: Medicina (Kaunas, Lithuania)
Published Date:

Abstract

Artificial intelligence (AI) has emerged as a transformative tool in the field of ophthalmology, revolutionizing disease diagnosis and management. This paper provides a comprehensive overview of AI applications in various retinal diseases, highlighting its potential to enhance screening efficiency, facilitate early diagnosis, and improve patient outcomes. Herein, we elucidate the fundamental concepts of AI, including machine learning (ML) and deep learning (DL), and their application in ophthalmology, underscoring the significance of AI-driven solutions in addressing the complexity and variability of retinal diseases. Furthermore, we delve into the specific applications of AI in retinal diseases such as diabetic retinopathy (DR), age-related macular degeneration (AMD), Macular Neovascularization, retinopathy of prematurity (ROP), retinal vein occlusion (RVO), hypertensive retinopathy (HR), Retinitis Pigmentosa, Stargardt disease, best vitelliform macular dystrophy, and sickle cell retinopathy. We focus on the current landscape of AI technologies, including various AI models, their performance metrics, and clinical implications. Furthermore, we aim to address challenges and pitfalls associated with the integration of AI in clinical practice, including the "black box phenomenon", biases in data representation, and limitations in comprehensive patient assessment. In conclusion, this review emphasizes the collaborative role of AI alongside healthcare professionals, advocating for a synergistic approach to healthcare delivery. It highlights the importance of leveraging AI to augment, rather than replace, human expertise, thereby maximizing its potential to revolutionize healthcare delivery, mitigate healthcare disparities, and improve patient outcomes in the evolving landscape of medicine.

Authors

  • Uday Pratap Singh Parmar
    Department of Ophthalmology, Government Medical College and Hospital, Chandigarh 160030, India.
  • Pier Luigi Surico
    Massachusetts Eye and Ear, Department of Ophthalmology, Harvard Medical School, Boston, MA 02114, USA.
  • Rohan Bir Singh
    Massachusetts Eye and Ear, Department of Ophthalmology, Harvard Medical School, Boston, MA 02114, USA.
  • Francesco Romano
    Université de Paris, Equipe d'accueil VIgilance FAtigue SOMmeil (VIFASOM) EA 7330, Paris, France; Assistance Publique-Hôpitaux de Paris (APHP) Hôtel Dieu, Centre du Sommeil et de la Vigilance, Paris, France.
  • Carlo Salati
    Department of Ophthalmology, University Hospital of Udine, p.le S. Maria della Misericordia 15, 33100 Udine, Italy.
  • Leopoldo Spadea
    Eye Clinic, Policlinico Umberto I, "Sapienza" University of Rome, 00142 Rome, Italy.
  • Mutali Musa
    Department of Optometry, University of Benin, Benin City 300238, Edo State, Nigeria.
  • Caterina Gagliano
    Department of Medicine and Surgery, University of Enna "Kore", Enna, 94100, Italy.
  • Tommaso Mori
    Department of Ophthalmology, Campus Bio-Medico University, 00128 Rome, Italy.
  • Marco Zeppieri
    Department of Ophthalmology, University Hospital of Udine, p.le S. Maria della Misericordia 15, 33100 Udine, Italy.