Artificial Intelligence in Ultrasound Diagnoses of Ovarian Cancer: A Systematic Review and Meta-Analysis
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 withi...
| Main Authors: | , , , , , , , |
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| Format: | Article |
| Language: | English |
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MDPI AG
2024-01-01
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| Series: | Cancers |
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| Online Access: | https://www.mdpi.com/2072-6694/16/2/422 |
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| author | Sian Mitchell Manolis Nikolopoulos Alaa El-Zarka Dhurgham Al-Karawi Shakir Al-Zaidi Avi Ghai Jonathan E. Gaughran Ahmad Sayasneh |
| author_facet | Sian Mitchell Manolis Nikolopoulos Alaa El-Zarka Dhurgham Al-Karawi Shakir Al-Zaidi Avi Ghai Jonathan E. Gaughran Ahmad Sayasneh |
| author_sort | Sian Mitchell |
| collection | DOAJ |
| description | 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. |
| first_indexed | 2024-03-08T11:02:05Z |
| format | Article |
| id | doaj.art-55091d54ec644fe0a614f62d3f1d79b4 |
| institution | Directory Open Access Journal |
| issn | 2072-6694 |
| language | English |
| last_indexed | 2024-03-08T11:02:05Z |
| publishDate | 2024-01-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Cancers |
| spelling | doaj.art-55091d54ec644fe0a614f62d3f1d79b42024-01-26T15:38:14ZengMDPI AGCancers2072-66942024-01-0116242210.3390/cancers16020422Artificial Intelligence in Ultrasound Diagnoses of Ovarian Cancer: A Systematic Review and Meta-AnalysisSian Mitchell0Manolis Nikolopoulos1Alaa El-Zarka2Dhurgham Al-Karawi3Shakir Al-Zaidi4Avi Ghai5Jonathan E. Gaughran6Ahmad Sayasneh7Department of Women’s Health, Guy’s and St Thomas’ Hospital NHS Foundation Trust, London SE1 7EH, UKDepartment of Women’s Health, Guy’s and St Thomas’ Hospital NHS Foundation Trust, London SE1 7EH, UKDepartment of Gynaecology, Alexandria Faculty of Medicine, Alexandria 21433, EgyptMedical Analytica Ltd., Flint CH6 SXA, UKMedical Analytica Ltd., Flint CH6 SXA, UKSchool of Life Course Sciences, Faculty of Life Sciences and Medicine, King’s College London, Strand, London WC2R 2LS, UKDepartment of Women’s Health, Guy’s and St Thomas’ Hospital NHS Foundation Trust, London SE1 7EH, UKDepartment of Gynaecological Oncology, Surgical Oncology Directorate, Cancer Centre, Guy’s Hospital, Great Maze Pond, London SE1 9RT, UKOvarian 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.https://www.mdpi.com/2072-6694/16/2/422machine learningartificial intelligenceultrasoundovarian cancerovarian tumours |
| spellingShingle | Sian Mitchell Manolis Nikolopoulos Alaa El-Zarka Dhurgham Al-Karawi Shakir Al-Zaidi Avi Ghai Jonathan E. Gaughran Ahmad Sayasneh Artificial Intelligence in Ultrasound Diagnoses of Ovarian Cancer: A Systematic Review and Meta-Analysis Cancers machine learning artificial intelligence ultrasound ovarian cancer ovarian tumours |
| title | Artificial Intelligence in Ultrasound Diagnoses of Ovarian Cancer: A Systematic Review and Meta-Analysis |
| title_full | Artificial Intelligence in Ultrasound Diagnoses of Ovarian Cancer: A Systematic Review and Meta-Analysis |
| title_fullStr | Artificial Intelligence in Ultrasound Diagnoses of Ovarian Cancer: A Systematic Review and Meta-Analysis |
| title_full_unstemmed | Artificial Intelligence in Ultrasound Diagnoses of Ovarian Cancer: A Systematic Review and Meta-Analysis |
| title_short | Artificial Intelligence in Ultrasound Diagnoses of Ovarian Cancer: A Systematic Review and Meta-Analysis |
| title_sort | artificial intelligence in ultrasound diagnoses of ovarian cancer a systematic review and meta analysis |
| topic | machine learning artificial intelligence ultrasound ovarian cancer ovarian tumours |
| url | https://www.mdpi.com/2072-6694/16/2/422 |
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