Real-time estimation of the remaining surgery duration for cataract surgery using deep convolutional neural networks and long short-term memory
Abstract Purpose Estimating the surgery length has the potential to be utilized as skill assessment, surgical training, or efficient surgical facility utilization especially if it is done in real-time as a remaining surgery duration (RSD). Surgical length reflects a certain level of efficiency and m...
| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English |
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BMC
2023-05-01
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| Series: | BMC Medical Informatics and Decision Making |
| Subjects: | |
| Online Access: | https://doi.org/10.1186/s12911-023-02160-0 |
| _version_ | 1797832029745709056 |
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| author | Bowen Wang Liangzhi Li Yuta Nakashima Ryo Kawasaki Hajime Nagahara |
| author_facet | Bowen Wang Liangzhi Li Yuta Nakashima Ryo Kawasaki Hajime Nagahara |
| author_sort | Bowen Wang |
| collection | DOAJ |
| description | Abstract Purpose Estimating the surgery length has the potential to be utilized as skill assessment, surgical training, or efficient surgical facility utilization especially if it is done in real-time as a remaining surgery duration (RSD). Surgical length reflects a certain level of efficiency and mastery of the surgeon in a well-standardized surgery such as cataract surgery. In this paper, we design and develop a real-time RSD estimation method for cataract surgery that does not require manual labeling and is transferable with minimum fine-tuning. Methods A regression method consisting of convolutional neural networks (CNNs) and long short-term memory (LSTM) is designed for RSD estimation. The model is firstly trained and evaluated for the single main surgeon with a large number of surgeries. Then, the fine-tuning strategy is used to transfer the model to the data of the other two surgeons. Mean Absolute Error (MAE in seconds) was used to evaluate the performance of the RSD estimation. The proposed method is compared with the naïve method which is based on the statistic of the historical data. A transferability experiment is also set to demonstrate the generalizability of the method. Result The mean surgical time for the sample videos was 318.7 s (s) (standard deviation 83.4 s) for the main surgeon for the initial training. In our experiments, the lowest MAE of 19.4 s (equal to about 6.4% of the mean surgical time) is achieved by our best-trained model for the independent test data of the main target surgeon. It reduces the MAE by 35.5 s (-10.2%) compared to the naïve method. The fine-tuning strategy transfers the model trained for the main target to the data of other surgeons with only a small number of training data (20% of the pre-training). The MAEs for the other two surgeons are 28.3 s and 30.6 s with the fine-tuning model, which decreased by -8.1 s and -7.5 s than the Per-surgeon model (average declining of -7.8 s and 1.3% of video duration). External validation study with Cataract-101 outperformed 3 reported methods of TimeLSTM, RSDNet, and CataNet. Conclusion An approach to build a pre-trained model for estimating RSD estimation based on a single surgeon and then transfer to other surgeons demonstrated both low prediction error and good transferability with minimum fine-tuning videos. |
| first_indexed | 2024-04-09T14:02:18Z |
| format | Article |
| id | doaj.art-7e22c4c87bbc496ba7ed306ec94139c6 |
| institution | Directory Open Access Journal |
| issn | 1472-6947 |
| language | English |
| last_indexed | 2024-04-09T14:02:18Z |
| publishDate | 2023-05-01 |
| publisher | BMC |
| record_format | Article |
| series | BMC Medical Informatics and Decision Making |
| spelling | doaj.art-7e22c4c87bbc496ba7ed306ec94139c62023-05-07T11:15:08ZengBMCBMC Medical Informatics and Decision Making1472-69472023-05-0123111110.1186/s12911-023-02160-0Real-time estimation of the remaining surgery duration for cataract surgery using deep convolutional neural networks and long short-term memoryBowen Wang0Liangzhi Li1Yuta Nakashima2Ryo Kawasaki3Hajime Nagahara4Institute for Datability Science (IDS), Osaka UniversityInstitute for Datability Science (IDS), Osaka UniversityInstitute for Datability Science (IDS), Osaka UniversityArtificial Intelligence Center for Medical Research and Application, Osaka University HospitalInstitute for Datability Science (IDS), Osaka UniversityAbstract Purpose Estimating the surgery length has the potential to be utilized as skill assessment, surgical training, or efficient surgical facility utilization especially if it is done in real-time as a remaining surgery duration (RSD). Surgical length reflects a certain level of efficiency and mastery of the surgeon in a well-standardized surgery such as cataract surgery. In this paper, we design and develop a real-time RSD estimation method for cataract surgery that does not require manual labeling and is transferable with minimum fine-tuning. Methods A regression method consisting of convolutional neural networks (CNNs) and long short-term memory (LSTM) is designed for RSD estimation. The model is firstly trained and evaluated for the single main surgeon with a large number of surgeries. Then, the fine-tuning strategy is used to transfer the model to the data of the other two surgeons. Mean Absolute Error (MAE in seconds) was used to evaluate the performance of the RSD estimation. The proposed method is compared with the naïve method which is based on the statistic of the historical data. A transferability experiment is also set to demonstrate the generalizability of the method. Result The mean surgical time for the sample videos was 318.7 s (s) (standard deviation 83.4 s) for the main surgeon for the initial training. In our experiments, the lowest MAE of 19.4 s (equal to about 6.4% of the mean surgical time) is achieved by our best-trained model for the independent test data of the main target surgeon. It reduces the MAE by 35.5 s (-10.2%) compared to the naïve method. The fine-tuning strategy transfers the model trained for the main target to the data of other surgeons with only a small number of training data (20% of the pre-training). The MAEs for the other two surgeons are 28.3 s and 30.6 s with the fine-tuning model, which decreased by -8.1 s and -7.5 s than the Per-surgeon model (average declining of -7.8 s and 1.3% of video duration). External validation study with Cataract-101 outperformed 3 reported methods of TimeLSTM, RSDNet, and CataNet. Conclusion An approach to build a pre-trained model for estimating RSD estimation based on a single surgeon and then transfer to other surgeons demonstrated both low prediction error and good transferability with minimum fine-tuning videos.https://doi.org/10.1186/s12911-023-02160-0Surgery timeCataract surgeryLong short-term memory |
| spellingShingle | Bowen Wang Liangzhi Li Yuta Nakashima Ryo Kawasaki Hajime Nagahara Real-time estimation of the remaining surgery duration for cataract surgery using deep convolutional neural networks and long short-term memory BMC Medical Informatics and Decision Making Surgery time Cataract surgery Long short-term memory |
| title | Real-time estimation of the remaining surgery duration for cataract surgery using deep convolutional neural networks and long short-term memory |
| title_full | Real-time estimation of the remaining surgery duration for cataract surgery using deep convolutional neural networks and long short-term memory |
| title_fullStr | Real-time estimation of the remaining surgery duration for cataract surgery using deep convolutional neural networks and long short-term memory |
| title_full_unstemmed | Real-time estimation of the remaining surgery duration for cataract surgery using deep convolutional neural networks and long short-term memory |
| title_short | Real-time estimation of the remaining surgery duration for cataract surgery using deep convolutional neural networks and long short-term memory |
| title_sort | real time estimation of the remaining surgery duration for cataract surgery using deep convolutional neural networks and long short term memory |
| topic | Surgery time Cataract surgery Long short-term memory |
| url | https://doi.org/10.1186/s12911-023-02160-0 |
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