Conectar
Adnexal torsion is a gynaecological emergency in which prompt diagnosis and management are critical to preserving ovarian function. However, the clinical presentation is often non-specific, and diagnosis primarily relies on pelvic ultrasound, a modality with limited sensitivity that can lead to misdiagnosis and unnecessary surgery. Contrast-enhanced ultrasound (CEUS) has emerged as a promising imaging technique that may enhance diagnostic accuracy by better characterising adnexal vascularisation.
The aim of this study is to assess whether the addition of CEUS to standard diagnostic procedures can reduce the rate of unnecessary emergency surgeries. Specifically, we compare two diagnostic strategies in cases of high clinical suspicion of adnexal torsion: the current standard approach versus an experimental strategy incorporating CEUS. The primary outcome is the rate of inappropriate surgical interventions, defined as emergency surgery performed within 6 hours without intraoperative confirmation of torsion.
This is a prospective, open-label, multicentre, randomised (1:1), controlled, superiority trial. A total of 256 women presenting with a high clinical suspicion of adnexal torsion will be enrolled over a period of 36 months. Participants will be randomly assigned to either the standard diagnostic strategy or an experimental strategy that includes CEUS. The primary endpoint is the proportion of emergency surgical procedures (performed within 6 hours of hospital admission) in which adnexal torsion is not confirmed.
The study was approved by the French Ethics Committee, the CPP (Comité de Protection des Personnes) on 28 October 2024. The results of this study will be published in peer-reviewed journals and presented at relevant national and international conferences. The ethical approval number from the CPP is 6115.
NCT06677554; 2024-511720-13-00.
by Nguyen Hong Tan, Tran Manh Tuan, Pham Minh Chuan, Nguyen Duc Hoang, Le Quang Thanh, Le Hoang Son
Artificial Intelligence (AI) has been dramatically applied to healthcare in various tasks to support clinicians in disease diagnosis and prognosis. It has been known that accurate diagnosis must be drawn from multiple evidence, namely clinical records, X-Ray images, IoT data, etc called the multi-modal data. Despite the existence of various approaches for multi-modal medical data fusion, the development of comprehensive systems capable of integrating data from multiple sources and modalities remains a considerable challenge. Besides, many machine learning models face difficulties in representation and computation due to the uncertainty and diversity of medical data. This study proposes a novel multi-modal fuzzy knowledge graph framework, called FKG-MM, which integrates multi-modal medical data from multiple sources, offering enhanced computational performance compared to unimodal data. In addition, the FKG-MM framework is based on the fuzzy knowledge graph model, one of the models that represent and compute effectively with medical data in tabular form. Through some experiment scenarios utilizing the well-known BRSET dataset on multi-modal diabetic retinopathy, it has been experimentally validated that the feature selection method, when combining image features with tabular medical data features, gives the highest reliability results among 5 methods including Feature Selection Method, Tensor Product, Hadamard Product, Filter Selection, and Wrapper Selection. In addition, the experiment also confirms that the accuracy of FKG-MM increases by 12–14% when combining image data with tabular medical data than the related methods diagnosing only on tabular data.Prescribing patterns for hyperopia in children vary widely among eye care providers worldwide. This scoping review aims to identify and map the current literature on optical correction and catalogue outcomes reported, particularly in the domains of vision, vision-related functional outcomes and quality of life (QoL) in school-aged children with hyperopia.
This protocol was developed in accordance with the Joanna Briggs Institute’s Manual for Evidence Synthesis. We will include studies involving school-aged children with hyperopia without restrictions on sex, gender, race, ethnicity, type of optical correction, length of intervention, publication date or country of origin. We will include studies with internal or external comparison groups. We will exclude studies associated with myopia control treatments, ocular and visual pathway pathologies affecting vision or visual function. We will search Cochrane CENTRAL, Embase.com and PubMed. Examples of data to be extracted include population demographics, visual acuity, study-specific definitions for refractive error, treatment regimens for optical correction, vision and vision-related functional outcomes and QoL (general or vision-related) as quantified by validated instruments.
Informed consent and Institutional Review Board approval will not be required, as this scoping review will only use published data. The results from the scoping review will be disseminated by publication in a peer-reviewed scientific journal and at professional conferences.
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