Artificial Intelligence to Identify Arthroplasty Implants From Radiographs of the Knee

Karnuta, Jaret M.; Luu, Bryan C.; Roth, Alexander; Haeberle, Heather S.; Chen, Antonia F.; Iorio, Richard; Schaffer, Jonathan; Mont, Michael A.; Patterson, Brendan M.; Krebs, Viktor E.; Ramkumar, Prem N.

doi:10.1016/j.arth.2020.10.021

Cited by 56 publications

(83 citation statements)

References 9 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Yabu et al 34 also demonstrated that convolutional neural network could detect new osteoporotic vertebral fractures utilising magnetic resonance images with performance comparable to that of spine surgeons. Moreover, Karnuta et al 18 used AI to differentiate between knee arthroplasty implants from different manufacturers with near-perfect accuracy. Lind et al 21 also showed that AI could identify fractures around the knee in adults according to the 2018 AO/OTA classification system with performance similar to that of senior orthopaedic surgeons.…”

Section: Discussionmentioning

confidence: 99%

See 1 more Smart Citation

Deep learning approach for guiding three‐dimensional computed tomography reconstruction of lower limbs for robotically‐assisted total knee arthroplasty

Zhang

Ding

et al. 2021

Robotics Computer Surgery

View full text Add to dashboard Cite

Background: Robotic-assisted total knee arthroplasty (TKA) was performed to promote the accuracy of bone resection and mechanical alignment. Among these TKA system procedures, 3D reconstruction of CT data of lower limbs consumes significant manpower. Artificial intelligence (AI) algorithms applying deep learning has been proved efficient in automated identification and visual processing.Methods: CT data of a total of 200 lower limbs scanning were used for AI-based 3D model construction and CT data of 20 lower limbs scanning were utilised for verification. Results:We showed that the performance of an AI-guided 3D reconstruction of CT data of lower limbs for robotic-assisted TKA was similar to that of the operatorbased approach. The time of 3D lower limb model construction using AI was 4.7 min. AI-based 3D models can be used for surgical planning. Conclusion:AI was used for the first time to guide the 3D reconstruction of CT data of lower limbs for facilitating robotic-assisted TKA. Incorporation of AI in 3D model reconstruction before TKA might reduce the workload of radiologists. K E Y W O R D Sartificial intelligence, HURWA robotic-assisted TKA system, robotically TKA assisted system, 3D CT model | INTRODUCTIONTotal knee arthroplasty (TKA) is an effective procedure for cases with end-stage rheumatoid arthritis or osteoarthritis of the knee. 1 The surgery was carried out in over 370,000 and 700,000 patients annually in China and USA, respectively. 2 Despite the development in implant materials and design, thromboembolic prophylaxis, rehabilitation, computer navigation and patient-specific implants, and the use of prophylactic antibiotics, over 20% of patients reported unsatisfaction following TKA. [3][4][5][6] Robotically assisted TKA was thus developed to promote mechanical alignment, accuracy of bone resection, soft tissue protection, and implant survivorship and implant stability. [7][8][9][10] In this connection, our previous studies showed that a novel HURWA robotic-assisted TKA system (BEIJING HURWA-ROBOT Technology Co. Ltd) could enhance the accuracy of bone resection in terms of resection angles and levels in the sawbone and the sheep models. 11,12 The procedures of robotic-assisted TKA system consists of the following steps: preoperative computed tomography (CT) scanning of lower limbs, three-dimensional (3D) reconstruction of CT data of lower limbs, CT-based planning, surgical planning depending on the 3D CT model, and the act of performing robotic-assisted TKA. Among

show abstract

Section: Discussionmentioning

confidence: 99%

“…[13][14][15][16][17] Recently, an increasing number of studies reported that AI can be used in the TKA procedure. [18][19][20] For example, Karnuta et al 18 used AI to identify arthroplasty implants from radiographs of the knee. Lind et al 21 also utilised AI to identify and classify fractures of the knee.…”

mentioning

confidence: 99%

Deep learning approach for guiding three‐dimensional computed tomography reconstruction of lower limbs for robotically‐assisted total knee arthroplasty

Zhang

Ding

et al. 2021

Robotics Computer Surgery

View full text Add to dashboard Cite

show abstract

“…Their network is trained to recognize only two classes, which limits its generalizability. In [33], the authors achieved 99% accuracy by using an artificial intelligence-based DL model to classify knee implants from four manufacturers. In [34], the authors used the visual geometry group (VGG)-16 and VGG-19 models by applying transfer learning to classify dental implants in panoramic X-ray images.…”

Section: Related Workmentioning

confidence: 99%

Artificial Intelligence-Based Recognition of Different Types of Shoulder Implants in X-ray Scans Based on Dense Residual Ensemble-Network for Personalized Medicine

Sultan

Owais

Park

et al. 2021

JPM

View full text Add to dashboard Cite

Re-operations and revisions are often performed in patients who have undergone total shoulder arthroplasty (TSA) and reverse total shoulder arthroplasty (RTSA). This necessitates an accurate recognition of the implant model and manufacturer to set the correct apparatus and procedure according to the patient’s anatomy as personalized medicine. Owing to unavailability and ambiguity in the medical data of a patient, expert surgeons identify the implants through a visual comparison of X-ray images. False steps cause heedlessness, morbidity, extra monetary weight, and a waste of time. Despite significant advancements in pattern recognition and deep learning in the medical field, extremely limited research has been conducted on classifying shoulder implants. To overcome these problems, we propose a robust deep learning-based framework comprised of an ensemble of convolutional neural networks (CNNs) to classify shoulder implants in X-ray images of different patients. Through our rotational invariant augmentation, the size of the training dataset is increased 36-fold. The modified ResNet and DenseNet are then combined deeply to form a dense residual ensemble-network (DRE-Net). To evaluate DRE-Net, experiments were executed on a 10-fold cross-validation on the openly available shoulder implant X-ray dataset. The experimental results showed that DRE-Net achieved an accuracy, F1-score, precision, and recall of 85.92%, 84.69%, 85.33%, and 84.11%, respectively, which were higher than those of the state-of-the-art methods. Moreover, we confirmed the generalization capability of our network by testing it in an open-world configuration, and the effectiveness of rotational invariant augmentation.

show abstract

“…While accurate identification of the manufacturer and type of implant is required for preoperative planning of revision arthroplasty, it is estimated that surgeons are unable to recognize the implant preoperatively and intraoperatively in about 10 and 2% of cases, respectively. Inability to identify implants can lead to unpreparedness which may contribute to increased surgical time, perioperative morbidity, and overall healthcare costs [45][46][47]. Deep learning has been shown its ability to recognize implant manufacturer and design in hip and knee arthroplasty [45][46][47][48][49].…”

Section: Preoperative Evaluationmentioning

confidence: 99%

“…Inability to identify implants can lead to unpreparedness which may contribute to increased surgical time, perioperative morbidity, and overall healthcare costs [45][46][47]. Deep learning has been shown its ability to recognize implant manufacturer and design in hip and knee arthroplasty [45][46][47][48][49].…”

Section: Preoperative Evaluationmentioning

confidence: 99%

Artificial intelligence in arthroplasty

2021

View full text Add to dashboard Cite

Artificial intelligence (AI) is altering the world of medicine. Given the rapid advances in technology, computers are now able to learn and improve, imitating humanoid cognitive function. AI applications currently exist in various medical specialties, some of which are already in clinical use. This review presents the potential uses and limitations of AI in arthroplasty to provide a better understanding of the existing technology and future direction of this field.Recent literature demonstrates that the utilization of AI in the field of arthroplasty has the potential to improve patient care through better diagnosis, screening, planning, monitoring, and prediction. The implementation of AI technology will enable arthroplasty surgeons to provide patient-specific management in clinical decision making, preoperative health optimization, resource allocation, decision support, and early intervention. While this technology presents a variety of exciting opportunities, it also has several limitations and challenges that need to be overcome to ensure its safety and effectiveness.

show abstract

Artificial Intelligence to Identify Arthroplasty Implants From Radiographs of the Knee

Cited by 56 publications

References 9 publications

Deep learning approach for guiding three‐dimensional computed tomography reconstruction of lower limbs for robotically‐assisted total knee arthroplasty

Deep learning approach for guiding three‐dimensional computed tomography reconstruction of lower limbs for robotically‐assisted total knee arthroplasty

Artificial Intelligence-Based Recognition of Different Types of Shoulder Implants in X-ray Scans Based on Dense Residual Ensemble-Network for Personalized Medicine

Artificial intelligence in arthroplasty

Contact Info

Product

Resources

About