# Classification of OCT Images of the Human Eye Using Mobile Devices
## 1 Introduction  
Artificial intelligence solutions are gradually becoming permissible in medical diagnostics. For example, the Polish Code of Medical Ethics [1], in force since 2025, allows the use of artificial intelligence algorithms when certain conditions are met. The patient must be informed and must express informed consent to the use of artificial intelligence during the diagnostic and therapeutic process. Solutions approved and certified for medical use may be used. It is also important that the final diagnostic and therapeutic decision always be made by a doctor. The use of artificial intelligence (AI) techniques allows for the analysis of the influence of various factors on the prediction of disease changes. An example of the possibilities of deep learning (DL) techniques is inference for the integration of multimodal data (radiology, pathology, epigenetics, and clinical data) to predict the overall survival of patients with glioma [2]. Machine learning, and especially deep neural networks (DNNs), can be widely used in ophthalmology OCT image analysis [3]. Several application areas can be distinguished:segmentation of retinal layers (using U-Net-type networks [4]); analysis of changes in time using hybrid models (e.g., combining solutions of CNN with sequential recursive NN-type models) [5]; detection and classification of eye diseases such as age-related macular degeneration (AMD), glaucoma, diabetic retinopathy, or macular holes [6]. OCT devices for ophthalmological diagnostics often include extensive software for the segmentation of retinal layers and parametric visualization to assess abnormal changes. The correctness of deep learning solutions is comparable to manual image analysis by an ophthalmology specialist. The use of innovative NN models such as the BioImagingLab/I-NESC TEC [7] allows precise segmentation of the retinal layer for AMD patients and has demonstrated a moderate to strong correlation between computer and human metrics [8]. Based on B-scan OCT of the macula area, an experienced ophthalmologist can easily classify retinal pathological changes. However, for screening tests, computer classification using artificial intelligence techniques can be helpful. Datasets of B-scan OCTs prepared in recent years allow the preparation of classifiers that achieve an accuracy above 96% [3]. It should be noted that the software of OCT devices does not yet typically have such functionality, assuming that the diagnosis will be performed by a specialist. Modern smartphones have increasingly greater computing capabilities, and built-in cameras take high-resolution photos. We can observe the research progress of smartphone-based optical imaging biosensors in the fields of colorimetry, fluorescence, and microscopic imaging for genetic testing [9]. The widespread availability of smartphones and broadband transmission has enabled the development of telemedical consultations. This phenomenon can also be observed in the field of ophthalmology [10]. The ability to send self-taken photos of the anterior segment of the eye and eyelids, e.g., for conditions such as eyelid chalazion (called an eyelid cyst or a meibomian cyst), allows for a quick teleconsultation without traveling to an ophthalmological clinic or hospital. With the right smartphone camera attachments [11], it is also possible to take photos of the fundus of the eye, thus providing imaging used in diabetic retinopathy. There are a number of different applications available for smartphones dedicated to diagnosis, treatment, and symptom management in ophthalmology. The review of solutions presented in [12] shows that mobile applications can be successfully used in the following issues: visual acuity assessment, dry eye diagnostics, color recognition, strabismus assessment, detection of metamorphosis, pupillometry, or education for ophthalmologists and optometrists. More advanced diagnostics related to glaucoma and diabetes are also possible, in which case the parametric data are entered manually.