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#392 Artificial intelligence in the ultrasound diagnosis of ovarian cancer: a systematic review and meta–analysis
  1. Sian Mitchell1,
  2. Manolis Nikolopoulos1,
  3. Alaa Zarka2,
  4. Dhurgham Al-Karawi3,
  5. Avi Ghai4,
  6. Jonathan Gaughran1,
  7. Med Mustafa Zelal Muallem5 and
  8. Ahmad Sayasneh6,7
  1. 1Guy’s and St Thomas’ NHS trust, London, UK
  2. 2University of Alexandria, Alexandria, Egypt
  3. 3Russell IPM Ltd and Medical Analytica Ltd, Flint, UK
  4. 4King’s College London Medical School, London, UK
  5. 5Deputy Director of Department of Gynecology with Center for Oncological Surgery Charité Medical University of Berlin, Berlin, Germany
  6. 6Faculty of Life Sciences and Medicine at Guy’s, London, UK
  7. 7Department of Gynaecological Oncology, London, UK


Introduction/Background Ovarian cancer is the 6th most common malignancy with a 35% survival rate across all stages at 10 years. Ultrasound is a widely used tool 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.

Methodology EMBASE and MEDLINE databases were searched. All type of clinical studies that used artificial intelligence in ultrasound for the diagnosis of ovarian malignancies were screened. Studies with the histopathological findings as standard were used. The diagnostic performance of each study was analysed, and the pooled diagnostic performance was assessed.

Results The initial search identified 3726 papers of which 166 were suitable for abstract screening. In the final analysis, 16 papers were included with different sample sizes and different methods used. There was a combined total of 18451 ultrasound images examined through the final included studies. The overall sensitivity was 85% (95% CI 0.84–0.85) and specificity was 93% (95% CI 0.93–0.94).

Conclusion Artificial intelligence plays an important role in aiding the ultrasound diagnosis of ovarian cancer. Further prospective work is required to further validate AI for use in clinical practice.

Disclosures nothing to declare

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