review · Cancers
Ovarian cancer is the sixth most common malignancy, associated with a ten-year survival rate of 35 percent across all stages. While ultrasound is widely employed to detect ovarian tumours, precise pre-operative diagnosis remains essential for determining appropriate patient care. Emerging artificial intelligence tools offer potential assistance in interpreting these ultrasound examinations. A pooled analysis of 14 clinical studies evaluated the diagnostic performance of artificial intelligence systems compared against histopathological reference standards. Across a combined dataset of 15,358 ultrasound images, artificial intelligence models achieved an overall sensitivity of 81 percent and a specificity of 92 percent. These findings confirm good diagnostic capability for identifying ovarian malignancies from ultrasound imaging. Nevertheless, prospective studies remain necessary to validate these automated methods before routine deployment in clinical environments.
Ovarian cancer carries a low ten-year survival rate, making timely and accurate pre-operative diagnosis vital for patient outcomes. Standard ultrasound imaging plays a major role in detection, and combining it with artificial intelligence could help clinicians identify malignant tumours with high specificity. Establishing the accuracy of these automated approaches provides an evidence base for improving diagnostic confidence and planning appropriate clinical interventions.
This research supports the development of diagnostic decision-support software for ultrasound devices, aimed at gynaecologists and radiologists managing suspected ovarian tumours. With 81 percent sensitivity and 92 percent specificity established across diverse studies, the underlying algorithms represent applied and tested research. Commercial translation into certified medical devices remains at an intermediate stage, pending prospective clinical validation to ensure safety and generalisability across diverse clinical settings.
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Ovarian cancer is the sixth most common malignancy, with a 35% survival rate across all stages at 10 years. Ultrasound is widely used for ovarian tumour diagnosis, and accurate pre-operative diagnosis is essential for appropriate patient management. Artificial intelligence is an emerging field within gynaecology and has been shown to aid in the ultrasound diagnosis of ovarian cancers. For this study, Embase and MEDLINE databases were searched, and all original clinical studies that used artificial intelligence in ultrasound examinations for the diagnosis of ovarian malignancies were screened. Studies using histopathological findings as the standard were included. The diagnostic performance of each study was analysed, and all the diagnostic performances were pooled and assessed. The initial search identified 3726 papers, of which 63 were suitable for abstract screening. Fourteen studies that used artificial intelligence in ultrasound diagnoses of ovarian malignancies and had histopathological findings as a standard were included in the final analysis, each of which had different sample sizes and used different methods; these studies examined a combined total of 15,358 ultrasound images. The overall sensitivity was 81% (95% CI, 0.80-0.82), and specificity was 92% (95% CI, 0.92-0.93), indicating that artificial intelligence demonstrates good performance in ultrasound diagnoses of ovarian cancer. Further prospective work is required to further validate AI for its use in clinical practice.
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DOI: 10.3390/cancers16020422
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