Significant reduction in manual annotation costs in ultrasound medical image database construction through step by step artificial intelligence pre-annotation.
This study investigates the feasibility of reducing manual image annotation costs in medical image database construction by utilizing a step by step approach where the Artificial Intelligence model (AI model) trained on a previous batch of data automatically pre-annotates the next batch of image dat...
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| Main Authors: | , , , , , , , , , , , , , , , |
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| Format: | Article |
| Language: | English |
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Public Library of Science (PLoS)
2025-06-01
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| Series: | PLOS Digital Health |
| Online Access: | https://doi.org/10.1371/journal.pdig.0000738 |
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| _version_ | 1849427368843673600 |
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| author | Fu Zheng Liu XingMing Xu JuYing Tao MengYing Yang BaoJian Shan Yan Ye KeWei Lu ZhiKai Huang Cheng Qi KeLan Chen XiHao Du WenFei He Ping Wang RunYu Ying Ying Bu XiaoHui |
| author_facet | Fu Zheng Liu XingMing Xu JuYing Tao MengYing Yang BaoJian Shan Yan Ye KeWei Lu ZhiKai Huang Cheng Qi KeLan Chen XiHao Du WenFei He Ping Wang RunYu Ying Ying Bu XiaoHui |
| author_sort | Fu Zheng |
| collection | DOAJ |
| description | This study investigates the feasibility of reducing manual image annotation costs in medical image database construction by utilizing a step by step approach where the Artificial Intelligence model (AI model) trained on a previous batch of data automatically pre-annotates the next batch of image data, taking ultrasound image of thyroid nodule annotation as an example. The study used YOLOv8 as the AI model. During the AI model training, in addition to conventional image augmentation techniques, augmentation methods specifically tailored for ultrasound images were employed to balance the quantity differences between thyroid nodule classes and enhance model training effectiveness. The study found that training the model with augmented data significantly outperformed training with raw images data. When the number of original images number was only 1,360, with 7 thyroid nodule classifications, pre-annotation using the AI model trained on augmented data could save at least 30% of the manual annotation workload for junior physicians. When the scale of original images number reached 6,800, the classification accuracy of the AI model trained on augmented data was very close with that of junior physicians, eliminating the need for manual preliminary annotation. |
| format | Article |
| id | doaj-art-88fa595e1a0648f5810c24f3d5a0cb39 |
| institution | Kabale University |
| issn | 2767-3170 |
| language | English |
| publishDate | 2025-06-01 |
| publisher | Public Library of Science (PLoS) |
| record_format | Article |
| series | PLOS Digital Health |
| spelling | doaj-art-88fa595e1a0648f5810c24f3d5a0cb392025-08-20T03:29:03ZengPublic Library of Science (PLoS)PLOS Digital Health2767-31702025-06-0146e000073810.1371/journal.pdig.0000738Significant reduction in manual annotation costs in ultrasound medical image database construction through step by step artificial intelligence pre-annotation.Fu ZhengLiu XingMingXu JuYingTao MengYingYang BaoJianShan YanYe KeWeiLu ZhiKaiHuang ChengQi KeLanChen XiHaoDu WenFeiHe PingWang RunYuYing YingBu XiaoHuiThis study investigates the feasibility of reducing manual image annotation costs in medical image database construction by utilizing a step by step approach where the Artificial Intelligence model (AI model) trained on a previous batch of data automatically pre-annotates the next batch of image data, taking ultrasound image of thyroid nodule annotation as an example. The study used YOLOv8 as the AI model. During the AI model training, in addition to conventional image augmentation techniques, augmentation methods specifically tailored for ultrasound images were employed to balance the quantity differences between thyroid nodule classes and enhance model training effectiveness. The study found that training the model with augmented data significantly outperformed training with raw images data. When the number of original images number was only 1,360, with 7 thyroid nodule classifications, pre-annotation using the AI model trained on augmented data could save at least 30% of the manual annotation workload for junior physicians. When the scale of original images number reached 6,800, the classification accuracy of the AI model trained on augmented data was very close with that of junior physicians, eliminating the need for manual preliminary annotation.https://doi.org/10.1371/journal.pdig.0000738 |
| spellingShingle | Fu Zheng Liu XingMing Xu JuYing Tao MengYing Yang BaoJian Shan Yan Ye KeWei Lu ZhiKai Huang Cheng Qi KeLan Chen XiHao Du WenFei He Ping Wang RunYu Ying Ying Bu XiaoHui Significant reduction in manual annotation costs in ultrasound medical image database construction through step by step artificial intelligence pre-annotation. PLOS Digital Health |
| title | Significant reduction in manual annotation costs in ultrasound medical image database construction through step by step artificial intelligence pre-annotation. |
| title_full | Significant reduction in manual annotation costs in ultrasound medical image database construction through step by step artificial intelligence pre-annotation. |
| title_fullStr | Significant reduction in manual annotation costs in ultrasound medical image database construction through step by step artificial intelligence pre-annotation. |
| title_full_unstemmed | Significant reduction in manual annotation costs in ultrasound medical image database construction through step by step artificial intelligence pre-annotation. |
| title_short | Significant reduction in manual annotation costs in ultrasound medical image database construction through step by step artificial intelligence pre-annotation. |
| title_sort | significant reduction in manual annotation costs in ultrasound medical image database construction through step by step artificial intelligence pre annotation |
| url | https://doi.org/10.1371/journal.pdig.0000738 |
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