Ultrasound assessment of acute pelvic pain: diagnostic challenges across gynecological, surgical and urological conditions
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Abstract
Background. Acute pelvic pain is a frequent clinical emergency, with an incidence of 15-24% among women, and may result from both gynecological and surgical causes. Differentiating between these conditions remains a diagnostic challenge, as rapid and accurate identification of the underlying pathology is essential for timely intervention. Objective. This study aims to analyze the role of ultrasound in the differential diagnosis of acute pelvic pain, emphasizing its accuracy, limitations and integration with complementary imaging methods. Materials and method. A systematic literature review was conducted, including 13 clinical studies, reviews and international guidelines published between 2000 and 2025, identified in PubMed, Scopus and Web of Science. The search used keywords such as “acute pelvic pain”, “gynecological causes”, “surgical causes”, “ultrasound” and “differential diagnosis”. Data extracted included diagnostic accuracy, clinical applicability and comparative imaging findings. Results. Ultrasound emerged as the first-line imaging method in acute pelvic pain evaluation due to its accessibility, lack of ionizing radiation and the ability to provide rapid diagnostic information. The reported sensitivity ranged between 80% and 92%, and specificity ranged between 85% and 95%, depending on pathology and operator expertise. Ultrasound demonstrated high accuracy in diagnosing ovarian torsion, ectopic pregnancy and ruptured ovarian cysts, while CT and MRI proved necessary in inconclusive cases or in suspected gastrointestinal and urological conditions. Standardized classifications and consensuses (IOTA, IDEA, #Enzian, Barnhart, Rettenbacher, Mizuki et al.) enhanced diagnostic consistency and clinical decision-making. Conclusions. Ultrasound remains the cornerstone in the evaluation of acute pelvic pain, effectively guiding the differential diagnosis between gynecological and surgical conditions. Its integration with clinical data and, when necessary, with CT or MRI ensures optimal patient management, minimizes diagnostic errors, and reduces the risk of complications.
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