The need for time and attention, given by the doctor to the patient, due to the increased volume of medical data to be interpreted and filtered for diagnostic and therapeutic purposes has encouraged the development of the option to support, constructively and effectively, deep learning models. Deep learning (DL) has experienced an exponential development in recent years, with a major impact on interpretations of the medical image. This has influenced the development, diversification and increase of the quality of scientific data, the development of knowledge construction methods and the improvement of DL models used in medical applications. All research papers focus on description, highlighting, classification of one of the constituent elements of deep learning models (DL), used in the interpretation of medical images and do not provide a unified picture of the importance and impact of each constituent in the performance of DL models. The novelty in our paper consists primarily in the unitary approach, of the constituent elements of DL models, namely, data, tools used by DL architectures or specifically constructed DL architecture combinations and highlighting their “key” features, for completion of tasks in current applications in the interpretation of medical images. The use of “key” characteristics specific to each constituent of DL models and the correct determination of their correlations, may be the subject of future research, with the aim of increasing the performance of DL models in the interpretation of medical images.

Aim of the study The correct establishment of the therapeutic conduct in papillary thyroid cancer through the use of artificial intelligence (AI) models is essential in medical practice in diagnosing and establishing the prognosis of the disease, this aspect implies the most accurate and effective therapeutic approach. I aim to use AI models to obtain correct and favorable results for the diagnosis and prognosis of papillary thyroid cancer by conducting a clinical trial, in which we will use the diagnostic method optimized by artificial intelligence (AI) algorithms.The use of AI will avoid errors and increase performance in the interpretation of the doctor's computed tomography (CT) scan and consequently, improve treatment planning. Materials and methods The optimization of the method will consist in the development and training of artificial intelligence models, using AI algorithms in the diagnosis of papillary thyroid cancererol by accurately identifying pathological lesions and adenopathy and generating 3D images from 2D CT images. use the transfer function for opacity and color, grayscale from DICOM images projected in a three-dimensional space. [1]; [2] We also use artificial intelligence (AI), through the Generative Adversarial Networks (GAN) technique, which has proven to be effective in representing complex data distributions [2], as we do in this study. I will validate this method of diagnosis and prognosis of papillary thyroid cancer, optimized by the artificial intelligence algorithm, by conducting a randomized, controlled clinical trial over a period of 12 months. Results I will validate the diagnostic method by using AI. By using this method in medical practice, I aim to be able to avoid errors, to provide precision in diagnosing, staging and establishing the therapeutic plan in papillary thyroid cancer using AI models. Conclusions The use of the AI method can increase the quality of life by establishing the correct therapeutic plan and improving the prognosis.

Aim of the study For the accurate diagnosis and staging of precancers and cervical and thyroid cancer, we aim to create a diagnostic method optimized by artificial intelligence (AI) algorithms and validated by the notable positive results of a randomized, controlled, 17-month trial.. Materials and methods The optimization of the method will involve the development and training of artificial intelligence models using convolutive neural networks (CNN) to identify precancers and cancers in colposcopic images. We will use topologies that have demonstrated strong performance in similar image recognition projects, such as VGG16, Inception, MobilNet, ROI, U-Net, and KiU-Net. Additionally, the research includes a comparative study of various algorithms and tools employed in segmental volumetric constructions to generate 3D images from MRI/CT data. This study will also evaluate current advancements in DICOM image processing using techniques such as volume rendering, transfer functions for opacity and color, and shades of gray from DICOM images projected in a three-dimensional space. Validation of the proposed method will be achieved through a randomized, controlled clinical trial conducted over 17 months. Patients will be informed and recruited either via random presentation at the specialized medical centers participating in the trial or through a dedicated web platform. Selection criteria will adhere to inclusion and exclusion parameters defined in the clinical trial protocol, and all patient data will be handled ethically and in accordance with written and informed consent approved by the Ethics Committee. . Results The optimized method, supported by AI algorithms and validated through clinical trials, aims to demonstrate concrete and favorable outcomes in the diagnosis, staging, and treatment planning for cervical and thyroid precancers and cancers.. Conclusions By implementing this AI-optimized diagnostic method, we seek to raise the quality standard in diagnosing and staging precancers and cancers, ultimately enhancing therapeutic decision-making and patient outcomes.

Aim of the study. In December 2020, the first two mRNA vaccines, developed by Pfizer/BioNTech and Moderna, were approved by the FDA in the USA. Clarification about the safety, efficacy, and low rate of adverse reactions of COVID-19 vaccines was not sufficient to alleviate the level of hesitation against vaccination in the global community. Materials and methods. In medical practice in Romania, patients who presented to the gynecological consultation have blamed changes in the duration, appearance of menstruation and menstruation cycle, along with other new symptoms that they considered to be post-vaccination anti COVID-19. This aspect required a survey. The general principles of the strategy will continue to apply, namely, the continuation of screening by popularizing the survey with the aim of raising patient awareness and increasing timely addressability to avoid complications. Results Lowering the level of hesitation to the susceptibility to vaccination and increasing confidence in the vaccination campaigns against COVID-19 were identified as key outcomes. Conclusions The findings highlight the importance of addressing concerns regarding post-vaccination symptoms to improve public trust and participation in vaccination campaigns