(1) Background: Coronavirus disease 2019 (COVID-19) is an infectious disease caused by SARS-CoV-2. Reverse transcription polymerase chain reaction (RT-PCR) remains the current gold standard for detecting SARS-CoV-2 infections in nasopharyngeal swabs. In Romania, the first reported patient to have contracted COVID-19 was officially declared on 26 February 2020. (2) Methods: This study proposes a federated learning approach with pre-trained deep learning models for COVID-19 detection. Three clients were locally deployed with their own dataset. The goal of the clients was to collaborate in order to obtain a global model without sharing samples from the dataset. The algorithm we developed was connected to our internal picture archiving and communication system and, after running backwards, it encountered chest CT changes suggestive for COVID-19 in a patient investigated in our medical imaging department on the 28 January 2020. (4) Conclusions: Based on our results, we recommend using an automated AI-assisted software in order to detect COVID-19 based on the lung imaging changes as an adjuvant diagnostic method to the current gold standard (RT-PCR) in order to greatly enhance the management of these patients and also limit the spread of the disease, not only to the general population but also to healthcare professionals.

The integration of deep learning into radiology has the potential to enhance diagnostic processes, yet its acceptance in clinical practice remains limited due to various challenges. This study aimed to develop and evaluate a fine-tuned large language model (LLM), based on Llama 3-8B, to automate the generation of accurate and concise conclusions in magnetic resonance imaging (MRI) and computed tomography (CT) radiology reports, thereby assisting radiologists and improving reporting efficiency. A dataset comprising 15,000 radiology reports was collected from the University of Medicine and Pharmacy of Craiova’s Imaging Center, covering a diverse range of MRI and CT examinations made by four experienced radiologists. The Llama 3-8B model was fine-tuned using transfer-learning techniques, incorporating parameter quantization to 4-bit precision and low-rank adaptation (LoRA) with a rank of 16 to optimize computational efficiency on consumer-grade GPUs. The model was trained over five epochs using an NVIDIA RTX 3090 GPU, with intermediary checkpoints saved for monitoring. Performance was evaluated quantitatively using Bidirectional Encoder Representations from Transformers Score (BERTScore), Recall-Oriented Understudy for Gisting Evaluation (ROUGE), Bilingual Evaluation Understudy (BLEU), and Metric for Evaluation of Translation with Explicit Ordering (METEOR) metrics on a held-out test set. Additionally, a qualitative assessment was conducted, involving 13 independent radiologists who participated in a Turing-like test and provided ratings for the AI-generated conclusions. The fine-tuned model demonstrated strong quantitative performance, achieving a BERTScore F1 of 0.8054, a ROUGE-1 F1 of 0.4998, a ROUGE-L F1 of 0.4628, and a METEOR score of 0.4282. In the human evaluation, the artificial intelligence (AI)-generated conclusions were preferred over human-written ones in approximately 21.8% of cases, indicating that the model’s outputs were competitive with those of experienced radiologists. The average rating of the AI-generated conclusions was 3.65 out of 5, reflecting a generally favorable assessment. Notably, the model maintained its consistency across various types of reports and demonstrated the ability to generalize to unseen data. The fine-tuned Llama 3-8B model effectively generates accurate and coherent conclusions for MRI and CT radiology reports. By automating the conclusion-writing process, this approach can assist radiologists in reducing their workload and enhancing report consistency, potentially addressing some barriers to the adoption of deep learning in clinical practice. The positive evaluations from independent radiologists underscore the model’s potential utility. While the model demonstrated strong performance, limitations such as dataset bias, limited sample diversity, a lack of clinical judgment, and the need for large computational resources require further refinement and real-world validation. Future work should explore the integration of such models into clinical workflows, address ethical and legal considerations, and extend this approach to generate complete radiology reports.

Currently, medical imaging has largely supplanted traditional methods in the realm of diagnosis and treatment planning. This shift is primarily attributable to the non-invasive nature, rapidity, and user-friendliness of medical-imaging techniques. The widespread adoption of medical imaging, however, has shifted the bottleneck to healthcare professionals who must analyze each case post-image acquisition. This process is characterized by its sluggishness and subjectivity, making it susceptible to errors. The anterior cruciate ligament (ACL), a frequently injured knee ligament, predominantly affects a youthful and sports-active demographic. ACL injuries often leave patients with substantial disabilities and alter knee mechanics. Since some of these cases necessitate surgery, it is crucial to accurately classify and detect ACL injury. This paper investigates the utilization of pre-trained convolutional neural networks featuring residual connections (ResNet) along with image-processing methods to identify ACL injury and differentiate between various tear levels. The ResNet employed in this study is not the standard ResNet but rather an adapted version capable of processing 3D volumes constructed from 2D image slices. Achieving a peak accuracy of 97.15% with a custom split, 96.32% through Monte-Carlo cross-validation, and 93.22% via five-fold cross-validation, our approach enhances the performance of three-class classifiers by over 7% in terms of raw accuracy. Moreover, we achieved an improvement of more than 1% across all types of evaluation. It is quite clear that the model’s output can effectively serve as an initial diagnostic baseline for radiologists with minimal effort and nearly instantaneous results. This advancement underscores the paper’s focus on harnessing deep learning for the nuanced detection and classification of ACL tears, demonstrating a significant leap toward automating and refining diagnostic accuracy in sports medicine and orthopedics.

Prostate cancer is the second most common cancer in men worldwide. The results obtained in magnetic resonance imaging examinations are used to decide the indication, type, and location of a prostate biopsy and contribute information about the characterization or aggressiveness of detected cancers, including tumor progression over time. This study proposes a method to highlight prostate lesions with a high and very high risk of being malignant by overlaying a T2-weighted image, apparent diffusion coefficient map, and diffusion-weighted image sequences using 204 pairs of slices from 80 examined patients. It was reviewed by two radiologists who segmented suspicious lesions and labeled them according to the prostate imaging-reporting and data system (PI-RADS) score. Both radiologists found the algorithm to be useful as a “first opinion”, and they gave an average score on the quality of the highlight of 9.2 and 9.3, with an agreement of 0.96.

Human dirofilariasis represents a zoonotic infectious disorder caused by parasites belonging to the Dirofilaria genus, which includes numerous species with a large variability regarding the host specificity, life cycle, and clinical manifestations. This disease appears to be a vector-borne parasitosis that is spread through insects - intermediate hosts (usually mosquitos) - and affects several carnivores as definitive hosts. Humans represent an unsuitable host for the parasite to complete its life cycle, being unable to release microfilariae in the blood as the inoculated larvae fail to reach sexual maturity. Therefore, humans are unable to transmit the infection to other humans, neither directly nor through an intermediate host. The current case report indicates a rare encounter of a Dirofilaria repens infection in a 42-year-old female patient living in an urban area (Craiova, Romania), who developed intermittent right periorbital edema after a previous trip to Greece. Over the course of one month, the right periorbital edema gradually remitted, and a firm, round lump developed in the external inferior right periorbital region. There were no similar clinical manifestations among other family members. Her medical history showed no other complaints or any serious general health problems. Also, the patient denied having any pets at home. The blood tests did not reveal any abnormalities. The exact source of infection could not be identified, but the chances for this infection to be related to the history of traveling to Greece are high, given that this region is one of the most important endemic areas in Europe.

Background: Artificial intelligence (AI) is increasingly used as a decision-support tool in mammography, but its influence on radiology residents’ interpretive behavior remains insufficiently characterized. This study evaluated the impact of AI assistance on BI-RADS classification and diagnostic confidence among radiology residents. Methods: This retrospective, single-center, multi-reader paired study included 112 diagnostic mammography examinations, corresponding to 223 assessable breasts and 2230 resident-breast readings. Ten radiology residents interpreted 2D mammography and digital breast tomosynthesis examinations first without AI assistance and subsequently with access to AI output. Changes in the BI-RADS category, diagnostic confidence, reasons for modification, and agreement with an expert-consensus BI-RADS reference standard were analyzed. Results: AI-assisted reassessment changed the BI-RADS classification in 9.7% of readings and diagnostic confidence in 19.2%, with any AI-associated modification observed in 24.3% of instances. Upgrades were more frequent than downgrades, particularly for medium- and high-suspicion AI outputs. Confidence increased more often than it decreased. Expert-reference agreement improved modestly, and BI-RADS 4+ sensitivity increased from 72.0% to 82.3%, with stable negative agreement, but these metrics reflect agreement with expert BI-RADS consensus rather than pathology-confirmed cancer detection. Conclusions: AI assistance influenced both BI-RADS reassessment and diagnostic confidence among radiology residents, producing modest but directionally favorable changes. These findings support cautious, supervised integration of AI into breast imaging training, with attention to confidence calibration and potential overreliance.