Automatic tip detection of surgical instruments in biportal endoscopic spine surgery

Cho, Sue Min; Kim, Young-Gon; Jeong, Ji-Young; Kim, In-Hwan; Lee, Ho-Jin; Kim, Namkug

doi:10.1016/j.compbiomed.2021.104384

Cited by 21 publications

(10 citation statements)

References 22 publications

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“… 24 25) As a result, it is crucial to gather high-quality data through the use of an auto-focusing function and technique that can consistently maintain the appropriate shooting distance, angle, and brightness. 27) Additionally, the factors of shooting distance, angle, lighting, training environment, and tool shape all influence the effectiveness of deep learning. As such, it is essential to collect high-quality data through the use of an auto-focusing function and method that can consistently maintain the proper shooting distance, angle, and brightness.…”

Section: Discussionmentioning

confidence: 99%

Assessment of Automated Identification of Phases in Videos of Total Hip Arthroplasty Using Deep Learning Techniques

Kang,

Kim,

Seo

et al. 2024

Clin Orthop Surg

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Background As the population ages, the rates of hip diseases and fragility fractures are increasing, making total hip arthroplasty (THA) one of the best methods for treating elderly patients. With the increasing number of THA surgeries and diverse surgical methods, there is a need for standard evaluation protocols. This study aimed to use deep learning algorithms to classify THA videos and evaluate the accuracy of the labelling of these videos. Methods In our study, we manually annotated 7 phases in THA, including skin incision, broaching, exposure of acetabulum, acetabular reaming, acetabular cup positioning, femoral stem insertion, and skin closure. Within each phase, a second trained annotator marked the beginning and end of instrument usages, such as the skin blade, forceps, Bovie, suction device, suture material, retractor, rasp, femoral stem, acetabular reamer, head trial, and real head. Results In our study, we utilized YOLOv3 to collect 540 operating images of THA procedures and create a scene annotation model. The results of our study showed relatively high accuracy in the clear classification of surgical techniques such as skin incision and closure, broaching, acetabular reaming, and femoral stem insertion, with a mean average precision (mAP) of 0.75 or higher. Most of the equipment showed good accuracy of mAP 0.7 or higher, except for the suction device, suture material, and retractor. Conclusions Scene annotation for the instrument and phases in THA using deep learning techniques may provide potentially useful tools for subsequent documentation, assessment of skills, and feedback.

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Section: Discussionmentioning

confidence: 99%

Assessment of Automated Identification of Phases in Videos of Total Hip Arthroplasty Using Deep Learning Techniques

Kang,

Kim,

Seo

et al. 2024

Clin Orthop Surg

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“…The CNN is the most applied DL method followed by Long Short Term Memory (LSTM), Recurrent Neural Network (RNN) and autoencoder architectures (Figure 5). The CNN model and the variants, with few modifications in the underlying architecture in some cases, yielded better performance in [29], [67]- [70], [72]- [75], [77], [79], [80], [82]- [85], [87]- [92], [94], [97], [98], [100], [101], whereas autoencoders, RNN, LSTM, and Generative Adversarial Network (GAN) formed another notable synergy [39], [71], [76], [78], [81], [86], [93], [95].…”

Section: ) Tool Detectionmentioning

confidence: 99%

Deep Learning Based Image Processing for Robot Assisted Surgery: A Systematic Literature Survey

et al. 2022

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The recent advancements in the surging field of Deep Learning (DL) have revolutionized every sphere of life, and the healthcare domain is no exception. The enormous success of DL models, particularly with image data, has led to the development of image-guided Robot Assisted Surgery (RAS) systems. By and large, the number of studies concerning image-driven computer assisted surgical systems using DL has increased exponentially. Additionally, the contemporary availability of surgical datasets has also boosted the DL applications in RAS. Inspired by the latest trends and contributions in surgery, this literature survey presents a summarized analysis of recent innovations of DL in image-guided RAS systems. After a thorough review, a sum of 184 articles are selected and grouped into four categories, based on the literature and the relevancy of the task in the articles, comprising 1) Surgical Tools, 2) Surgical Processes, 3) Surgical Surveillance, and 4) Surgical Performance. The survey also discusses publicly available surgical datasets and highlights the basics of the DL models. Furthermore, the legal, ethical, and technological challenges together with the intuitive predictions and recommendations related to the autonomous RAS systems are also presented. The study reveals that Convolutional Neural Network (CNN) is most widely adopted architecture, whereas, JIGSAWS is most employed dataset in RAS. The study suggests fusing kinematic data along with image data, which produces better accuracy and precision, particularly in gesture and trajectory segmentation tasks. Additionally, CNN and Long Short Term Memory (LSTM) networks have shown remarkable performance, however, authors recommend employing these gigantic architectures only when simpler models have failed to produce satisfactory results. The simpler models, despite their limitations, are time and cost effective and yield considerable outcomes even on the smaller datasets.

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“…Doerr et al (2020) used Faster R-CNN for detecting spinal pedicle screws from cone-beam CT (CBCT) projections. Cho et al (2021) used a custom CNN to detect surgical instruments' tips in biportal endoscopic spine surgery.…”

Section: Introductionmentioning

confidence: 99%

Surgical navigation for guidewire placement from intraoperative fluoroscopy in orthopaedic surgery

Mekki,

Sheth,

Vijayan

et al. 2023

Phys. Med. Biol.

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Objective. Surgical guidewires are commonly used in placing fixation implants to stabilize fractures. Accurate positioning of these instruments is challenged by difficulties in 3D reckoning from 2D fluoroscopy. This work aims to enhance the accuracy and reduce exposure times by providing 3D navigation for guidewire placement from as little as two fluoroscopic images.Approach. Our approach combines machine learning-based segmentation with the geometric model of the imager to determine the 3D poses of guidewires. Instrument tips are encoded as individual keypoints, and the segmentation masks are processed to estimate the trajectory. Correspondence between detections in multiple views is established using the pre-calibrated system geometry, and the corresponding features are backprojected to obtain the 3D pose. Guidewire 3D directions were computed using both an analytical and an optimization-based method. The complete approach was evaluated in cadaveric specimens with respect to potential confounding effects from the imaging geometry and radiographic scene clutter due to other instruments.Main results. The detection network identified the guidewire tips within 2.2 mm and guidewire directions within 1.1°, in 2D detector coordinates. Feature correspondence rejected false detections, particularly in images with other instruments, to achieve 83% precision and 90% recall. Estimating the 3D direction via numerical optimization showed added robustness to guidewires aligned with the gantry rotation plane. Guidewire tips and directions were localized in 3D world coordinates with a median accuracy of 1.8 mm and 2.7°, respectively.Significance. The paper reports a new method for automatic 2D detection and 3D localization of guidewires from pairs of fluoroscopic images. Localized guidewires can be virtually overlaid on the patient’s pre-operative 3D scan during the intervention. Accurate pose determination for multiple guidewires from two images offers to reduce radiation dose by minimizing the need for repeated imaging and provides quantitative feedback prior to implant placement.

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Automatic tip detection of surgical instruments in biportal endoscopic spine surgery

Cited by 21 publications

References 22 publications

Assessment of Automated Identification of Phases in Videos of Total Hip Arthroplasty Using Deep Learning Techniques

Assessment of Automated Identification of Phases in Videos of Total Hip Arthroplasty Using Deep Learning Techniques

Deep Learning Based Image Processing for Robot Assisted Surgery: A Systematic Literature Survey

Surgical navigation for guidewire placement from intraoperative fluoroscopy in orthopaedic surgery

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