Artificial intelligence for instance segmentation of MRI: advancing efficiency and safety in laparoscopic myomectomy of broad ligament fibroids
BackgroundUterine broad ligament fibroids present unique surgical challenges due to their proximity to vital pelvic structures. This study aimed to evaluate artificial intelligence (AI)-guided MRI instance segmentation for optimizing laparoscopic myomectomy outcomes.MethodsIn this trial, 120 patient...
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Frontiers Media S.A.
2025-04-01
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| Series: | Frontiers in Oncology |
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| Online Access: | https://www.frontiersin.org/articles/10.3389/fonc.2025.1549803/full |
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| author | Feiran Liu Minghuang Chen Haixia Pan Bin Li Wenpei Bai |
| author_facet | Feiran Liu Minghuang Chen Haixia Pan Bin Li Wenpei Bai |
| author_sort | Feiran Liu |
| collection | DOAJ |
| description | BackgroundUterine broad ligament fibroids present unique surgical challenges due to their proximity to vital pelvic structures. This study aimed to evaluate artificial intelligence (AI)-guided MRI instance segmentation for optimizing laparoscopic myomectomy outcomes.MethodsIn this trial, 120 patients with MRI-confirmed broad ligament fibroids were allocated to either AI-assisted group (n=60) or conventional MRI group (n=60). A deep learning model was developed to segment fibroids, uterine walls, and uterine cavity from preoperative MRI.ResultCompared to conventional MRI guidance, AI assistance significantly reduced operative time (118 [112.25-125.00] vs. 140 [115.75-160.75] minutes; p<0.001). The AI group also demonstrated lower intraoperative blood loss (50 [50-100] vs. 85 [50-100] ml; p=0.01) and faster postoperative recovery (first flatus within 24 hours: (15[25.00%] vs. 29[48.33%], p=0.01).ConclusionThis multidisciplinary AI system enhances surgical precision through millimeter-level anatomical delineation, demonstrating transformative potential for complex gynecologic oncology procedures. Clinical adoption of this approach could reduce intraoperative blood loss and iatrogenic complications, thereby promoting postoperative recovery. |
| format | Article |
| id | doaj-art-55f4bca8656d4c41ae4bd86d7ddabce5 |
| institution | OA Journals |
| issn | 2234-943X |
| language | English |
| publishDate | 2025-04-01 |
| publisher | Frontiers Media S.A. |
| record_format | Article |
| series | Frontiers in Oncology |
| spelling | doaj-art-55f4bca8656d4c41ae4bd86d7ddabce52025-08-20T02:25:59ZengFrontiers Media S.A.Frontiers in Oncology2234-943X2025-04-011510.3389/fonc.2025.15498031549803Artificial intelligence for instance segmentation of MRI: advancing efficiency and safety in laparoscopic myomectomy of broad ligament fibroidsFeiran Liu0Minghuang Chen1Haixia Pan2Bin Li3Wenpei Bai4Department of Obstetrics and Gynecology, Beijing Shijitan Hospital, Capital Medical University, Beijing, ChinaDepartment of Obstetrics and Gynecology, Beijing Shijitan Hospital, Capital Medical University, Beijing, ChinaCollege of Software, Beihang University, Beijing, ChinaDepartment of MRI, Beijing Shijitan Hospital, Capital Medical University, Beijing, ChinaDepartment of Obstetrics and Gynecology, Beijing Shijitan Hospital, Capital Medical University, Beijing, ChinaBackgroundUterine broad ligament fibroids present unique surgical challenges due to their proximity to vital pelvic structures. This study aimed to evaluate artificial intelligence (AI)-guided MRI instance segmentation for optimizing laparoscopic myomectomy outcomes.MethodsIn this trial, 120 patients with MRI-confirmed broad ligament fibroids were allocated to either AI-assisted group (n=60) or conventional MRI group (n=60). A deep learning model was developed to segment fibroids, uterine walls, and uterine cavity from preoperative MRI.ResultCompared to conventional MRI guidance, AI assistance significantly reduced operative time (118 [112.25-125.00] vs. 140 [115.75-160.75] minutes; p<0.001). The AI group also demonstrated lower intraoperative blood loss (50 [50-100] vs. 85 [50-100] ml; p=0.01) and faster postoperative recovery (first flatus within 24 hours: (15[25.00%] vs. 29[48.33%], p=0.01).ConclusionThis multidisciplinary AI system enhances surgical precision through millimeter-level anatomical delineation, demonstrating transformative potential for complex gynecologic oncology procedures. Clinical adoption of this approach could reduce intraoperative blood loss and iatrogenic complications, thereby promoting postoperative recovery.https://www.frontiersin.org/articles/10.3389/fonc.2025.1549803/fullartificial intelligence - AIuterine myomaInstance segmentationlaproscopic myomectomyMRI |
| spellingShingle | Feiran Liu Minghuang Chen Haixia Pan Bin Li Wenpei Bai Artificial intelligence for instance segmentation of MRI: advancing efficiency and safety in laparoscopic myomectomy of broad ligament fibroids Frontiers in Oncology artificial intelligence - AI uterine myoma Instance segmentation laproscopic myomectomy MRI |
| title | Artificial intelligence for instance segmentation of MRI: advancing efficiency and safety in laparoscopic myomectomy of broad ligament fibroids |
| title_full | Artificial intelligence for instance segmentation of MRI: advancing efficiency and safety in laparoscopic myomectomy of broad ligament fibroids |
| title_fullStr | Artificial intelligence for instance segmentation of MRI: advancing efficiency and safety in laparoscopic myomectomy of broad ligament fibroids |
| title_full_unstemmed | Artificial intelligence for instance segmentation of MRI: advancing efficiency and safety in laparoscopic myomectomy of broad ligament fibroids |
| title_short | Artificial intelligence for instance segmentation of MRI: advancing efficiency and safety in laparoscopic myomectomy of broad ligament fibroids |
| title_sort | artificial intelligence for instance segmentation of mri advancing efficiency and safety in laparoscopic myomectomy of broad ligament fibroids |
| topic | artificial intelligence - AI uterine myoma Instance segmentation laproscopic myomectomy MRI |
| url | https://www.frontiersin.org/articles/10.3389/fonc.2025.1549803/full |
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