1
|
Mariotti C, Mangoni L, Iorio S, Lombardo V, Fruttini D, Rizzo C, Chhablani J, Midena E, Lupidi M. Novel Artificial Intelligence-Based Assessment of Imaging Biomarkers in Full-Thickness Macular Holes: Preliminary Data from a Pivotal Trial. J Clin Med 2024; 13:628. [PMID: 38276134 PMCID: PMC10816123 DOI: 10.3390/jcm13020628] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/07/2023] [Revised: 01/10/2024] [Accepted: 01/16/2024] [Indexed: 01/27/2024] Open
Abstract
Artificial intelligence (AI)- and deep learning (DL)-based systems have shown significant progress in the field of macular disorders, demonstrating high performance in detecting retinal fluid and assessing anatomical changes during disease progression. This study aimed to validate an AI algorithm for identifying and quantifying prognostic factors in visual recovery after macular hole (MH) surgery by analyzing major optical coherence tomography (OCT) biomarkers. This study included 20 patients who underwent vitrectomy for a full-thickness macular hole (FTMH). The mean diameter of the FTMH was measured at 285.36 ± 97.4 μm. The preoperative best-corrected visual acuity (BCVA) was 0.76 ± 0.06 logMAR, improving to 0.38 ± 0.16 postoperatively, with a statistically significant difference (p = 0.001). AI software was utilized to assess biomarkers, such as intraretinal fluid (IRF) and subretinal fluid (SRF) volume, external limiting membrane (ELM) and ellipsoid zone (EZ) integrity, and retinal hyperreflective foci (HRF). The AI analysis showed a significant decrease in IRF volume, from 0.08 ± 0.12 mm3 preoperatively to 0.01 ± 0.01 mm3 postoperatively. ELM interruption improved from 79% ± 18% to 34% ± 37% after surgery (p = 0.006), whereas EZ interruption improved from 80% ± 22% to 40% ± 36% (p = 0.007) postoperatively. Additionally, the study revealed a negative correlation between preoperative IRF volume and postoperative BCVA recovery, suggesting that greater preoperative fluid volumes may hinder visual improvement. The integrity of the ELM and EZ was found to be essential for postoperative visual acuity improvement, with their disruption negatively impacting visual recovery. The study highlights the potential of AI in quantifying OCT biomarkers for managing MHs and improving patient care.
Collapse
Affiliation(s)
- Cesare Mariotti
- Eye Clinic, Department of Experimental and Clinical Medicine, Polytechnic University of Marche, 60131 Ancona, Italy (S.I.)
| | - Lorenzo Mangoni
- Eye Clinic, Department of Experimental and Clinical Medicine, Polytechnic University of Marche, 60131 Ancona, Italy (S.I.)
| | - Silvia Iorio
- Eye Clinic, Department of Experimental and Clinical Medicine, Polytechnic University of Marche, 60131 Ancona, Italy (S.I.)
| | - Veronica Lombardo
- Eye Clinic, Department of Experimental and Clinical Medicine, Polytechnic University of Marche, 60131 Ancona, Italy (S.I.)
| | - Daniela Fruttini
- Department of Medicine and Surgery, University of Perugia, S. Maria della Misericordia Hospital, 06123 Perugia, Italy
| | - Clara Rizzo
- Ophthalmic Unit, Department of Neurosciences, Biomedicine, and Movement Sciences, University of Verona, 37129 Verona, Italy
| | - Jay Chhablani
- Department of Ophthalmology, UPMC Eye Center, University of Pittsburgh, Pittsburgh, PA 15213, USA;
| | - Edoardo Midena
- Department of Ophthalmology, University of Padova, 35128 Padova, Italy;
- IRCCS—Fondazione Bietti, 00198 Rome, Italy
| | - Marco Lupidi
- Eye Clinic, Department of Experimental and Clinical Medicine, Polytechnic University of Marche, 60131 Ancona, Italy (S.I.)
- Fondazione per la Macula Onlus, Dipartimento di Neuroscienze, Riabilitazione, Oftalmologia, Genetica e Scienze Materno-Infantili (DINOGMI), University Eye Clinic, 16132 Genova, Italy
| |
Collapse
|
2
|
Yang M, Han J, Park JI, Hwang JS, Han JM, Yoon J, Choi S, Hwang G, Hwang DDJ. Prediction of Visual Acuity in Pathologic Myopia with Myopic Choroidal Neovascularization Treated with Anti-Vascular Endothelial Growth Factor Using a Deep Neural Network Based on Optical Coherence Tomography Images. Biomedicines 2023; 11:2238. [PMID: 37626734 PMCID: PMC10452208 DOI: 10.3390/biomedicines11082238] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/16/2023] [Revised: 08/03/2023] [Accepted: 08/08/2023] [Indexed: 08/27/2023] Open
Abstract
Myopic choroidal neovascularization (mCNV) is a common cause of vision loss in patients with pathological myopia. However, predicting the visual prognosis of patients with mCNV remains challenging. This study aimed to develop an artificial intelligence (AI) model to predict visual acuity (VA) in patients with mCNV. This study included 279 patients with mCNV at baseline; patient data were collected, including optical coherence tomography (OCT) images, VA, and demographic information. Two models were developed: one comprising horizontal/vertical OCT images (H/V cuts) and the second comprising 25 volume scan images. The coefficient of determination (R2) and root mean square error (RMSE) were computed to evaluate the performance of the trained network. The models achieved high performance in predicting VA after 1 (R2 = 0.911, RMSE = 0.151), 2 (R2 = 0.894, RMSE = 0.254), and 3 (R2 = 0.891, RMSE = 0.227) years. Using multiple-volume scanning, OCT images enhanced the performance of the models relative to using only H/V cuts. This study proposes AI models to predict VA in patients with mCNV. The models achieved high performance by incorporating the baseline VA, OCT images, and post-injection data. This model could assist in predicting the visual prognosis and evaluating treatment outcomes in patients with mCNV undergoing intravitreal anti-vascular endothelial growth factor therapy.
Collapse
Affiliation(s)
- Migyeong Yang
- Department of Applied Artificial Intelligence, Sungkyunkwan University, Seoul 03603, Republic of Korea; (M.Y.); (J.H.); (J.Y.); (S.C.)
| | - Jinyoung Han
- Department of Applied Artificial Intelligence, Sungkyunkwan University, Seoul 03603, Republic of Korea; (M.Y.); (J.H.); (J.Y.); (S.C.)
- Department of Human-Artificial Intelligence Interaction, Sungkyunkwan University, Seoul 03603, Republic of Korea
| | - Ji In Park
- Department of Medicine, Kangwon National University Hospital, Kangwon National University School of Medicine, Chuncheon 24341, Gangwon-do, Republic of Korea;
| | | | - Jeong Mo Han
- Seoul Bombit Eye Clinic, Sejong 30127, Republic of Korea;
| | - Jeewoo Yoon
- Department of Applied Artificial Intelligence, Sungkyunkwan University, Seoul 03603, Republic of Korea; (M.Y.); (J.H.); (J.Y.); (S.C.)
- RAONDATA, Seoul 04615, Republic of Korea
| | - Seong Choi
- Department of Applied Artificial Intelligence, Sungkyunkwan University, Seoul 03603, Republic of Korea; (M.Y.); (J.H.); (J.Y.); (S.C.)
- RAONDATA, Seoul 04615, Republic of Korea
| | - Gyudeok Hwang
- Department of Ophthalmology, Hangil Eye Hospital, Incheon 21388, Republic of Korea;
| | - Daniel Duck-Jin Hwang
- Department of Ophthalmology, Hangil Eye Hospital, Incheon 21388, Republic of Korea;
- Department of Ophthalmology, Catholic Kwandong University College of Medicine, Incheon 22711, Republic of Korea
| |
Collapse
|