Abstract Quantum computing (QC) has emerged as a transformative technology with the potential to surpass classical computational limits, especially in complex domains like medical imaging. This study investigates the integration of QC and quantum machine learning (QML) into medical imaging, with a focus on MRI, EEG, and CT. A structured literature review was conducted to identify recent developments, focusing on studies using accessible open-source data and English-language peer-reviewed publications. By leveraging quantum principles such as superposition and entanglement, hybrid quantum-classical models have demonstrated enhanced accuracy, speed, and efficiency in diagnostic tasks. In MRI, QML has improved early detection and classification of neurological diseases like Alzheimer’s and brain tumours. In EEG analysis, quantum algorithms such as Quantum EEGNet and Quantum Support Vector Machines have shown superior performance in detecting disorders, including schizophrenia and autism spectrum disorder. Additionally, quantum algorithms for CT image reconstruction and classification have achieved faster processing with fewer artifacts and greater fidelity. Despite current hardware constraints, results highlight the promise of QC in addressing existing limitations in medical diagnostics. The findings support continued development of QML models and quantum- enhanced workflows, with potential to revolutionise clinical practice by offering more accurate, efficient, and personalised care in neurological imaging and analysis.
Couvelaire syndrome is a rare complication of pregnancy involving the placenta and the uterine wall. It is caused by placental abruption, preeclampsia, or other disorders that compromise the blood supply to the placenta. Diagnosis is typically made through history, clinical evaluation, and ultrasound. This study aims to report a grave case of Couvelaire syndrome complemented by presenting a review of the relevant literature, to aid the meaning of a clinical path, as well as early and adequate diagnosis and treatment of Couvelaire syndrome, to prevent feto-maternal consequences. The treatment of Couvelaire syndrome depends on the severity of the condition and the well-being of both the mother and the fetus. If the condition is severe and poses a risk to the mother or fetus, immediate delivery may be recommended, mainly with urgent Cesarean section in general endotracheal anesthesia. If there is significant blood loss leading to a decrease in hemoglobin levels, blood transfusions may be necessary to restore normal blood volume and oxygen-carrying capacity. Part of the general treatment plan should also be the management of the underlying cause of placental abruption, for example, in cases of preeclampsia, medications are prescribed to control blood pressure and prevent seizures.
This paper focuses on the role of diffusion-weighted MRI of ovarian tumors as a potential biomarker to detect and characterize malignancy, and to improve the accuracy of preoperative diagnosis and postoperative follow-up to optimize therapeutic management. This is an original retrospective study of 76 patients with ovarian masses who underwent surgery between May 2014 and March 2016 at the "Dr Carol Davila" Central Military University Gynaecology Department. Patients underwent preoperative, 6-month, and 12-month postoperative evaluation of ovarian tumors using diffusion-weighted MRI. Due to the accuracy of this method using specific sequences in tissue characterization, magnetic resonance imaging can be used in the diagnostic work-up of complex adnexal masses.
