Background: The vitreomacular interface (VMI) encompasses a group of retinal disorders that significantly impact vision, requiring accurate classification for effective management. This study aims to compare the effectiveness of an expert-designed custom deep learning (DL) model and a code free Auto Machine Learning (ML) model in classifying optical coherence tomography (OCT) images of VMI disorders. Materials and Methods: A balanced dataset of OCT images across five classes—normal, epiretinal membrane (ERM), idiopathic full-thickness macular hole (FTMH), lamellar macular hole (LMH), and vitreomacular traction (VMT)—was used. The expert-designed model combined ResNet-50 and EfficientNet-B0 architectures with Monte Carlo cross-validation. The AutoML model was created on Google Vertex AI, which handled data processing, model selection, and hyperparameter tuning automatically. Performance was evaluated using average precision, precision, and recall metrics. Results: The expert-designed model achieved an overall balanced accuracy of 95.97% and a Matthews Correlation Coefficient (MCC) of 94.65%. Both models attained 100% precision and recall for normal cases. For FTMH, the expert model reached perfect precision and recall, while the AutoML model scored 97.8% average precision, and 97.4% recall. In VMT detection, the AutoML model showed 99.5% average precision with a slightly lower recall of 94.7% compared to the expert model’s 95%. For ERM, the expert model achieved 95% recall, while the AutoML model had higher precision at 93.9% but a lower recall of 79.5%. In LMH classification, the expert model exhibited 95% precision, compared to 72.3% for the AutoML model, with similar recall for both (88% and 87.2%, respectively). Conclusions: While the AutoML model demonstrated strong performance, the expert-designed model achieved superior accuracy across certain classes. AutoML platforms, although accessible to healthcare professionals, may require further advancements to match the performance of expert-designed models in clinical applications.

Background and Objectives: Tamoxifen, a cornerstone selective estrogen receptor modulator in breast cancer therapy, is increasingly recognized to be associated with retinal toxicity characterized by mitochondrial dysfunction, oxidative stress, lipid peroxidation, and oxidative DNA injury. By targeting mitochondrial bioenergetic dysfunction and redox disequilibrium, adenosine triphosphate (ATP) emerges as a biologically plausible candidate for retinal cytoprotection. This study aimed to evaluate the protective effect of ATP against tamoxifen-induced retinal toxicity in a rat model. Materials and Methods: Twenty-four male albino Wistar rats were randomly assigned to four groups: healthy control (HG), ATP-alone (ATPG, 4 mg/kg, intraperitoneally), tamoxifen-alone (TAMG, 5 mg/kg, orally), and tamoxifen plus ATP-treated (ATAG; ATP, 4 mg/kg, intraperitoneally; tamoxifen, 5 mg/kg, orally). Treatments were administered once daily for 30 days. Oxidative stress markers (malondialdehyde, total glutathione), antioxidant enzyme activities (superoxide dismutase, catalase), and oxidative DNA damage (8-hydroxy-2′-deoxyguanosine) were assessed in ocular tissues. Retinal histopathological evaluation included hematoxylin–eosin staining with semiquantitative assessment of edema, vascular congestion, polymorphonuclear leukocyte infiltration, and cytoplasmic vacuolization, together with quantitative measurements of retinal layer thicknesses and ganglion cell layer (GCL) cell counts. Results: Tamoxifen administration induced marked oxidative stress, antioxidant depletion, and increased oxidative DNA damage in ocular tissues, accompanied by significant thickening of retinal layers, reduced GCL cell counts, and pronounced disruption of retinal architecture. By comparison, ATP co-administration significantly suppressed lipid peroxidation and restored antioxidant defenses, thereby reducing oxidative DNA damage and preserving retinal structural integrity, as reflected by partial normalization of retinal layer thicknesses, preservation of GCL cell counts, and the presence of only mild residual edema. Conclusions: These findings indicate that ATP attenuates tamoxifen-induced retinal toxicity by supporting mitochondrial energy balance and redox homeostasis. Accordingly, ATP administration may represent a promising protective approach for reducing retinal injury associated with long-term tamoxifen therapy.