# Automated MRI Video Analysis for Pediatric Neuro-Oncology: An Experimental Approach
## 1 Introduction  
Over the past year, there has been growing interest in using open-source artificial intelligence models (OSAIM) in medicine [1]. Increasingly, research is focusing on evaluating these models' capabilities in image analysis, including magnetic resonance imaging (MRI) [2]. Following the release of the fourth-generation GPT model by OpenAI, there has been a significant increase in interest within the scientific sector. Research mainly focuses on the potential of ChatGPT 4 in solving medical problems. This model demonstrates the ability to analyze medical tests [3] and support education for students [4] and patients [5], and its applications as a diagnostic and therapeutic tool are widely studied [6; 7]. Recently, AI models have begun to be developed for analyzing video content, not just images. An example is ChatGPT 4 and the competing Google model, Gemini Pro. Conversational agents using large language models (LLM) offer a new way of interacting with visual data, extending their capabilities to video analysis. One such model is Video-ChatGPT, which combines a video-adapted visual encoder with an LLM [8]. Integrating artificial intelligence (AI) into brain MRI analysis has significantly enhanced diagnostic accuracy and improved patient outcomes. Deep learning algorithms, trained on large MRI datasets, are now capable of precisely identifying and localizing brain tumors [9; 10]. AI tools, such as SubtleMRTM, further enhance MRI image resolution, enabling the detection of changes as small as 4 mm [11]. Additionally, self-supervised text and vision models facilitate the detection of abnormalities in previously unreported MRI scans, automating tasks such as selection, diagnosis, and assessment of treatment responses. This automation supports radiologists by reducing errors and improving reporting efficiency [12]. Importantly, not all MRI sequences require imaging in the frontal, sagittal, and transverse planes. For example, [PERSON] et al. demonstrated that automating the determination of MRI scanning planes, particularly the mid-sagittal plane (MSP), can expedite diagnosis. Their proposed algorithm determines the MSP by analyzing the lines separating the brain hemispheres in frontal and transverse images. Tested on 100 images, the algorithm's results aligned closely with those obtained manually by MRI operators [13]. In addition to specialized AI models for brain MRI analysis, commercial models like ChatGPT are also being studied. [PERSON] et al. [14] evaluated ChatGPT 3.5 and 4 for their ability to accurately diagnose, propose treatment options, and create treatment plans for brain tumors in neuro-oncology cases. Their findings showed that ChatGPT 4 outperformed ChatGPT 3.5 in both diagnosis and treatment planning, demonstrating the potential of commercial AI models in medical applications. Brain tumors in children represent a significant global health concern, with an estimated incidence of 3 per 100,000 children annually [15]. These tumors are the most common solid tumors in children, with certain types, such as medulloblastoma, germ cell tumors, and ependymoma, occurring slightly more frequently in boys. The majority of pediatric brain tumors are infratentorial, affecting the cerebellum, brainstem, and fourth ventricle across all pediatric age groups [16]. MRI is a highly effective imaging method for detecting and diagnosing brain tumors in children, characterized by high sensitivity and specificity. Conventional brain MRI includes T1-weighted sequences before and after contrast administration, T2-weighted, FLAIR (fluid-attenuated inversion recovery), and diffusion sequences. High-resolution 3D imaging has become standard in T1-weighted imaging, which can be reformatted into various planes. Advanced techniques such as compression compensation and parallel imaging allow for obtaining high-resolution images with thin slices within a reasonable scan time [17].