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Bu J, Lei Y, Wang Y, Zhao J, Huang S, Liang J, Wang Z, Xu L, He B, Dong M, Liu G, Niu R, Ma C, Liu G. A Multi-Element Identification System Based on Deep Learning for the Visual Field of Percutaneous Endoscopic Spine Surgery. Indian J Orthop 2024; 58:587-597. [PMID: 38694692 PMCID: PMC11058141 DOI: 10.1007/s43465-024-01134-2] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 10/29/2023] [Accepted: 03/10/2024] [Indexed: 05/04/2024]
Abstract
Background Lumbar disc herniation is a common degenerative lumbar disease with an increasing incidence. Percutaneous endoscopic lumbar discectomy can treat lumbar disc herniation safely and effectively with a minimally invasive procedure. However, the learning curve of this technology is steep, which means that initial learners are often not sufficiently proficient in endoscopic operations, which can easily lead to iatrogenic damage. At present, the application of computer deep learning technology to clinical diagnosis, treatment, and surgical navigation has achieved satisfactory results. Purpose The objective of our team is to develop a multi-element identification system for the visual field of endoscopic spine surgery using deep learning algorithms and to evaluate the feasibility of this system. Method We established an image database by collecting surgical videos of 48 patients diagnosed with lumbar disc herniation, which was labeled by two spinal surgeons. We selected 6000 images of the visual field of percutaneous endoscopic spine surgery (including various tissue structures and surgical instruments), divided into the training data, validation data, and test data according to 2:1:2. We developed convolutional neural network models based on instance segmentation-Solov2, CondInst, Mask R-CNN and Yolact, and set the four network model backbone as ResNet101 and ResNet50 respectively. Mean average precision (mAP) and frames per second (FPS) were used to measure the performance of each model for classification, localization and recognition in real time, and AP (average) is used to evaluate how easily an element is detected by neural networks based on computer deep learning. Result Comprehensively comparing mAP and FSP of each model for bounding box test and segmentation task for the test set of images, we found that Solov2 (ResNet101) (mAP = 73.5%, FPS = 28.9), Mask R-CNN (ResNet101) (mAP = 72.8%, FPS = 28.5) models are the most stable, with higher precision and faster image processing speed. Combining the average precision of the elements in the bounding box test and segmentation tasks in each network, the AP(average) was highest for tool 3 (bbox-0.85, segm-0.89) and lowest for tool 5 (bbox-0.63, segm-0.72) in the instrumentation, whereas in the anatomical tissue elements, the fibrosus annulus (bbox-0.68, segm-0.69) and ligamentum flavum (bbox-0.65, segm-0.62) had higher AP(average),while extra-dural fat (bbox-0.42, segm-0.44) was lowest. Conclusion Our team has developed a multi-element identification system for the visual field of percutaneous endoscopic spine surgery adapted to the interlaminar and foraminal approaches, which can identify and track anatomical tissue (nerve, ligamentum flavum, nucleus pulposus, etc.) and surgical instruments (endoscopic forceps, an high-speed diamond burr, etc.), which can be used in the future as a virtual educational tool or applied to the intraoperative real-time assistance system for spinal endoscopic operation.
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Affiliation(s)
- Jinhui Bu
- Affiliated Xuzhou Clinical College of Xuzhou Medical University, Xuzhou, 221009 China
| | - Yan Lei
- Affiliated Xuzhou Clinical College of Xuzhou Medical University, Xuzhou, 221009 China
| | - Yari Wang
- School of Computer Science, China University of Mining and Technology, Xuzhou, 221116 China
| | - Jiaqi Zhao
- School of Computer Science, China University of Mining and Technology, Xuzhou, 221116 China
| | - Sen Huang
- Affiliated Xuzhou Clinical College of Xuzhou Medical University, Xuzhou, 221009 China
| | - Jun Liang
- Department of Orthopedic Surgery, Xuzhou Central Hospital, Xuzhou Central Hospital Affiliated to Nanjing University of Chinese Medicine, The Xuzhou School of Clinical Medicine of Nanjing Medical University, Xuzhou Central Hospital Affiliated to Medical School of Southeast University, Xuzhou, 221009 China
| | - Zhenfei Wang
- Department of Orthopedic Surgery, Xuzhou Central Hospital, Xuzhou Central Hospital Affiliated to Nanjing University of Chinese Medicine, The Xuzhou School of Clinical Medicine of Nanjing Medical University, Xuzhou Central Hospital Affiliated to Medical School of Southeast University, Xuzhou, 221009 China
| | - Long Xu
- Bengbu Medical College, Bengbu, 233000 China
| | - Bo He
- Affiliated Xuzhou Clinical College of Xuzhou Medical University, Xuzhou, 221009 China
| | - Minghui Dong
- Affiliated Xuzhou Clinical College of Xuzhou Medical University, Xuzhou, 221009 China
| | - Guangpu Liu
- Department of Orthopedic Surgery, Xuzhou Central Hospital, Xuzhou Central Hospital Affiliated to Nanjing University of Chinese Medicine, The Xuzhou School of Clinical Medicine of Nanjing Medical University, Xuzhou Central Hospital Affiliated to Medical School of Southeast University, Xuzhou, 221009 China
| | - Ru Niu
- Department of Orthopedic Surgery, Xuzhou Central Hospital, Xuzhou Central Hospital Affiliated to Nanjing University of Chinese Medicine, The Xuzhou School of Clinical Medicine of Nanjing Medical University, Xuzhou Central Hospital Affiliated to Medical School of Southeast University, Xuzhou, 221009 China
| | - Chao Ma
- Affiliated Xuzhou Clinical College of Xuzhou Medical University, Xuzhou, 221009 China
- Department of Orthopedic Surgery, Xuzhou Central Hospital, Xuzhou Central Hospital Affiliated to Nanjing University of Chinese Medicine, The Xuzhou School of Clinical Medicine of Nanjing Medical University, Xuzhou Central Hospital Affiliated to Medical School of Southeast University, Xuzhou, 221009 China
| | - Guangwang Liu
- Affiliated Xuzhou Clinical College of Xuzhou Medical University, Xuzhou, 221009 China
- Department of Orthopedic Surgery, Xuzhou Central Hospital, Xuzhou Central Hospital Affiliated to Nanjing University of Chinese Medicine, The Xuzhou School of Clinical Medicine of Nanjing Medical University, Xuzhou Central Hospital Affiliated to Medical School of Southeast University, Xuzhou, 221009 China
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Knocton S, Hunter A, Connors W, Dithurbide L, Neyedli HF. The Effect of Informing Participants of the Response Bias of an Automated Target Recognition System on Trust and Reliance Behavior. HUMAN FACTORS 2023; 65:189-199. [PMID: 34078167 PMCID: PMC9969489 DOI: 10.1177/00187208211021711] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 10/06/2020] [Accepted: 03/29/2021] [Indexed: 06/12/2023]
Abstract
OBJECTIVE To determine how changing and informing a user of the false alarm (FA) rate of an automated target recognition (ATR) system affects the user's trust in and reliance on the system and their performance during an underwater mine detection task. BACKGROUND ATR systems are designed to operate using a high sensitivity and a liberal decision criterion to reduce the risk of the ATR system missing a target. A high number of FAs in general may lead to a decrease in operator trust and reliance. METHODS Participants viewed sonar images and were asked to identify mines in the images. They performed the task without ATR and with ATR at a lower and higher FA rate. The participants were split into two groups-one informed and one uninformed of the changed FA rate. Trust and/or confidence in detecting mines was measured after each block. RESULTS When not informed of the FA rate, the FA rate had a significant effect on the participants' response bias. Participants had greater trust in the system and a more consistent response bias when informed of the FA rate. Sensitivity and confidence were not influenced by disclosure of the FA rate but were significantly worse for the high FA rate condition compared with performance without the ATR. CONCLUSION AND APPLICATION Informing a user of the FA rate of automation may positively influence the level of trust in and reliance on the aid.
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Affiliation(s)
| | - Aren Hunter
- Defence Research and Development Canada, Dartmouth, Nova Scotia,
Canada
| | - Warren Connors
- Defence Research and Development Canada, Dartmouth, Nova Scotia,
Canada
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Su B, Lin Y, Wang J, Quan X, Chang Z, Rui C. Deep Learning Target Detection System for Sewage Treatment. COMPUTATIONAL INTELLIGENCE AND NEUROSCIENCE 2022; 2022:2743781. [PMID: 35837224 PMCID: PMC9276506 DOI: 10.1155/2022/2743781] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 04/08/2022] [Revised: 05/10/2022] [Accepted: 05/25/2022] [Indexed: 11/28/2022]
Abstract
Object detection is to identify objects and then find some objects of interest. With the development of computers, target detection has evolved from traditional detection methods to artificial intelligence methods, and the latter are mainly based on some algorithms of deep learning. This paper mainly tests the treated sewage. First, the neural network and convolutional neural network algorithms in deep learning are studied, and then a target detection system is built based on these two algorithms. Finally, the treated sewage is detected and then compared with that of the traditional target detection system. The experimental results show that the target detection system of the convolutional neural network algorithm has a very stable recognition rate for the treated sewage, swinging around 70%, and the amplitude is not large. However, the target detection system of the neural network algorithm is not very stable in the recognition rate of the treated sewage, and the recognition rate is about 60%.
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Affiliation(s)
- Bingqin Su
- College of Environmental Science and Engineering, Taiyuan University of Technology, Jinzhong 030600, Shanxi, China
| | - Yuting Lin
- College of Environmental Science and Engineering, Taiyuan University of Science and Technology, Taiyuan 030024, Shanxi, China
| | - Jian Wang
- College of Environmental Science and Engineering, Taiyuan University of Technology, Jinzhong 030600, Shanxi, China
| | - Xiaohui Quan
- College of Environmental Science and Engineering, Taiyuan University of Technology, Jinzhong 030600, Shanxi, China
| | - Zhankun Chang
- College of Environmental Science and Engineering, Taiyuan University of Technology, Jinzhong 030600, Shanxi, China
| | - Chuangxue Rui
- Shanxi Jiabaoyuan Technology Co. Ltd., Taiyuan 030006, Shanxi, China
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Hollands JG, Terhaar P, Pavlovic NJ. Effects of Resolution, Range, and Image Contrast on Target Acquisition Performance. HUMAN FACTORS 2018; 60:363-383. [PMID: 29505286 DOI: 10.1177/0018720818760331] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/08/2023]
Abstract
OBJECTIVE We sought to determine the joint influence of resolution, target range, and image contrast on the detection and identification of targets in simulated naturalistic scenes. BACKGROUND Resolution requirements for target acquisition have been developed based on threshold values obtained using imaging systems, when target range was fixed, and image characteristics were determined by the system. Subsequent work has examined the influence of factors like target range and image contrast on target acquisition. METHOD We varied the resolution and contrast of static images in two experiments. Participants (soldiers) decided whether a human target was located in the scene (detection task) or whether a target was friendly or hostile (identification task). Target range was also varied (50-400 m). In Experiment 1, 30 participants saw color images with a single target exemplar. In Experiment 2, another 30 participants saw monochrome images containing different target exemplars. RESULTS The effects of target range and image contrast were qualitatively different above and below 6 pixels per meter of target for both tasks in both experiments. CONCLUSION Target detection and identification performance were a joint function of image resolution, range, and contrast for both color and monochrome images. APPLICATION The beneficial effects of increasing resolution for target acquisition performance are greater for closer (larger) targets.
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Affiliation(s)
| | - Phil Terhaar
- Defence Research and Development Canada, Toronto
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