## 1 Introduction
These algorithms are trained on large databases of otoscopic images and other related images to identify patterns that indicate the presence of pathology. A notable research study was carried out by [PERSON] et al. [18], who developed an AI system to automatically detect acute otitis media through otoscopic images, achieving diagnostic accuracy greater than that of medical professionals in some cases. Additionally, software such as DeepHear developed by [PERSON] et al. [19] uses AI to analyze audiometry signals and help with the early detection of hearing loss. The system is based on recurrent neural networks (RNNs) and has been used to predict the evolution of hearing loss in patients, contributing to more personalized treatments. These systems are particularly useful for diagnosis in remote areas or places with a shortage of specialized professionals. Other examples include OtoAI software, which applies AI algorithms to detect and classify inner ear pathology from MRI scans. The software is designed to be used in hospital settings, helping medical professionals make faster and more accurate diagnoses. The use of AI in diagnosing ear pathologies offers great advantages, such as reduced human errors, greater accuracy, and the ability to analyze large volumes of data in real time. However, the widespread implementation of these systems still faces challenges, such as the need for broad databases and rigorous clinical validation to ensure the reliability and safety of results. The video otoscope with integrated scanner presented here allows the acquisition of EEC images in real time, avoiding submitting patients to radiation, e.g., in MRI or CT scans. Furthermore, it represents an economical and quick way to obtain the 3D EEC geometry CAD model for customized hearing aid 3D printing through the use of Open-Source 3D reconstruction software, such as COLMAP, developed by [PERSON] and other researchers at the Technical University of Munich [20]. The versatility and efficiency that characterize COLMAP have made it a popular tool in 3D reconstruction projects in areas such as archaeology, architecture, and engineering. Moreover, it has also been used in academic and research environments [21]. In a clinical context, [PERSON] et al. [22] demonstrate the value of using advanced computational tools, augmented visualization, and 3D printing in pre-procedural planning for high-risk cardiac interventions, concluding the feasibility and safety of integrative augmented reality, simulations, and 3D printing in procedural planning. These technologies provide enhanced visualization, accurate prediction of device interference, and increased procedural confidence prior to cardiac intervention. Additive manufacturing (AM) offers significant cost and delivery time reductions due to the inherent characteristics of the manufacturing process. Compared to traditional machining, AM techniques are simpler to execute as they do not require detailed drawings, using nominal geometry based on computer models [23]. Nevertheless, open-source photogrammetry software, such as Meshroom [24] developed by AliceVision [25], or commercial solutions, such as Agisoft Metashape [26], can also be used.