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Mohammed AJ, Mohammed AS, Mohammed AS. Prediction of Tribological Properties of UHMWPE/SiC Polymer Composites Using Machine Learning Techniques. Polymers (Basel) 2023; 15:4057. [PMID: 37896301 PMCID: PMC10610110 DOI: 10.3390/polym15204057] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/31/2023] [Revised: 10/03/2023] [Accepted: 10/08/2023] [Indexed: 10/29/2023] Open
Abstract
Polymer composites are a class of material that are gaining a lot of attention in demanding tribological applications due to the ability of manipulating their performance by changing various factors, such as processing parameters, types of fillers, and operational parameters. Hence, a number of samples under different conditions need to be repeatedly produced and tested in order to satisfy the requirements of an application. However, with the advent of a new field of triboinformatics, which is a scientific discipline involving computer technology to collect, store, analyze, and evaluate tribological properties, we presently have access to a variety of high-end tools, such as various machine learning (ML) techniques, which can significantly aid in efficiently gauging the polymer's characteristics without the need to invest time and money in a physical experimentation. The development of an accurate model specifically for predicting the properties of the composite would not only cheapen the process of product testing, but also bolster the production rates of a very strong polymer combination. Hence, in the current study, the performance of five different machine learning (ML) techniques is evaluated for accurately predicting the tribological properties of ultrahigh molecular-weight polyethylene (UHMWPE) polymer composites reinforced with silicon carbide (SiC) nanoparticles. Three input parameters, namely, the applied pressure, holding time, and the concentration of SiCs, are considered with the specific wear rate (SWR) and coefficient of friction (COF) as the two output parameters. The five techniques used are support vector machines (SVMs), decision trees (DTs), random forests (RFs), k-nearest neighbors (KNNs), and artificial neural networks (ANNs). Three evaluation statistical metrics, namely, the coefficient of determination (R2-value), mean absolute error (MAE), and root mean square error (RMSE), are used to evaluate and compare the performances of the different ML techniques. Based upon the experimental dataset, the SVM technique was observed to yield the lowest error rates-with the RMSE being 2.09 × 10-4 and MAE being 2 × 10-4 for COF and for SWR, an RMSE of 2 × 10-4 and MAE of 1.6 × 10-4 were obtained-and highest R2-values of 0.9999 for COF and 0.9998 for SWR. The observed performance metrics shows the SVM as the most reliable technique in predicting the tribological properties-with an accuracy of 99.99% for COF and 99.98% for SWR-of the polymer composites.
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Affiliation(s)
- Abdul Jawad Mohammed
- Department of Information and Computer Science, King Fahd University of Petroleum and Minerals, Dhahran 31261, Saudi Arabia;
| | | | - Abdul Samad Mohammed
- Department of Mechanical Engineering, King Fahd University of Petroleum and Minerals, Dhahran 31261, Saudi Arabia
- Interdisciplinary Research Center for Advanced Materials, King Fahd University of Petroleum and Minerals, Dhahran 31261, Saudi Arabia
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Artificial intelligence and machine learning as a viable solution for hip implant failure diagnosis-Review of literature and in vitro case study. Med Biol Eng Comput 2023; 61:1239-1255. [PMID: 36701013 DOI: 10.1007/s11517-023-02779-1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/24/2022] [Accepted: 01/09/2023] [Indexed: 01/27/2023]
Abstract
The digital health industry is experiencing fast-paced research which can provide digital care programs and technologies to enhance the competence of healthcare delivery. Orthopedic literature also confirms the applicability of artificial intelligence (AI) and machine learning (ML) models to medical diagnosis and clinical decision-making. However, implant monitoring after primary surgery often happens with a wellness visit or when a patient complains about it. Neglecting implant design and other technical errors in this scenario, unmonitored circumstances, and lack of post-surgery monitoring may ultimately lead to the implant system's failure and leave us with the only option of high-risk revision surgery. Preventive maintenance seems to be a good choice to identify the onset of an irreversible prosthesis failure. Considering all these aspects for hip implant monitoring, this paper explores existing studies linking ML models and intelligent systems for hip implant diagnosis. This paper explores the feasibility of an alternative continuous monitoring technique for post-surgery implant monitoring backed by an in vitro ML case study. Tribocorrosion and acoustic emission (AE) data are considered based on their efficacy in determining irreversible alteration of implant material to prevent total failures. This study also facilitates the relevance of developing an artificially intelligent implant monitoring methodology that can function with daily patient activities and how it can influence the digital orthopedic diagnosis. AI-based non-invasive hip implant monitoring system enabling point-of-care testing.
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Kumar S, Bhowmik S. Potential use of natural fiber-reinforced polymer biocomposites in knee prostheses: a review on fair inclusion in amputees. IRANIAN POLYMER JOURNAL 2022. [DOI: 10.1007/s13726-022-01077-1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 10/17/2022]
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The Ability of Deep Learning Models to Identify Total Hip and Knee Arthroplasty Implant Design From Plain Radiographs. J Am Acad Orthop Surg 2022; 30:409-415. [PMID: 35139038 DOI: 10.5435/jaaos-d-21-00771] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 07/20/2021] [Accepted: 01/05/2022] [Indexed: 02/01/2023] Open
Abstract
INTRODUCTION The surgical management of patients with failed total hip or knee arthroplasty (THA and TKA) necessitates the identification of the implant manufacturer and model. Failure to accurately identify implant design leads to delays in care, increased morbidity, and healthcare costs. The automated identification of implant designs has the potential to assist in the surgical management of patients with failed arthroplasty. This study aimed to develop and validate a convolutional neural network deep learning model for the identification of primary and revision hip and knee total joint arthroplasty designs from plain radiographs. METHODS This study trained a convolutional neural network deep learning model to automatically identify 24 THA designs and 14 TKA designs from 11,204 anterior-posterior radiographs obtained from 8,763 patients. From these radiographs, 8,963 radiographs (80%) were used for model training and 2,241 radiographs (20%) were used for model validation. Model performance was assessed through receiver operating curve characteristics. RESULTS After 1,000 training epochs by the convolutional neural network deep learning model, the computational model discriminated 17 primary THA designs with an area under the receiver operating curve (AUC) of 0.98, sensitivity of 95.8%, and specificity of 98.6%. The deep learning model discriminated eight primary TKA designs with an AUC of 0.97, sensitivity of 94.9%, and specificity of 97.8%. The deep learning model demonstrated an AUC of 0.98 and 0.96 for the identification of seven revision THA and six revision TKA designs, respectively. DISCUSSION This study developed and validated a convolutional neural network deep learning model for the identification of hip and knee total joint arthroplasty designs from plain radiographs. The study findings demonstrate excellent accuracy of the deep learning model for the identification of 24 THA and 14 TKA designs, illustrating the great potential of the deep learning model to assist in preoperative surgical planning of failed arthroplasty patients.
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Influence of Cross-Shear and Contact Pressure on Wear Mechanisms of PEEK and CFR-PEEK in Total Hip Joint Replacements. LUBRICANTS 2022. [DOI: 10.3390/lubricants10050078] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 02/01/2023]
Abstract
With the increasing market demand for artificial hip joints, total hip joint replacement has gradually become an effective means of treating a series of hip joint diseases. In order to improve the service life of artificial hip joints, some new artificial hip joint materials, including polyetheretherketone (PEEK) and carbon fiber reinforced polyetheretherketone (CFR-PEEK), have been developed. In this paper, pin-on-plate wear tests under different cross-shear ratios and contact pressures were carried out to study the wear mechanism and worn surface topography of PEEK and CFR-PEEK. The experimental results showed that the wear of PEEK was associated with cross-shear, while CFR-PEEK was not. When the cross-shear ratio was 0.039 and contact pressure was 3.18 MPa, PEEK had poor wear resistance and its wear factor was about eight times that of ultra-high molecular weight polyethylene (UHMWPE). The wear resistance of CFR-PEEK had a significant advantage, since its wear factor was about 30% of that of PEEK. The wear factors of PEEK and CFR-PEEK increased as the contact pressure increased. The arithmetic average of the height amplitude of the surface, Sa, also increased gradually according to the topography of the worn surface. The wear mechanisms of PEEK and CFR-PEEK were scratching, plough cutting, and abrasion Since CFR-PEEK had good wear resistance and insensitivity to cross-shear motion, it is suitable for making artificial hip joints under low contact pressure condition.
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Borjali A, Chen AF, Bedair HS, Melnic CM, Muratoglu OK, Morid MA, Varadarajan KM. Comparing the performance of a deep convolutional neural network with orthopedic surgeons on the identification of total hip prosthesis design from plain radiographs. Med Phys 2021; 48:2327-2336. [PMID: 33411949 DOI: 10.1002/mp.14705] [Citation(s) in RCA: 11] [Impact Index Per Article: 3.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/01/2020] [Revised: 12/24/2020] [Accepted: 12/27/2020] [Indexed: 01/09/2023] Open
Abstract
PURPOSE A crucial step in the preoperative planning for a revision total hip replacement (THR) surgery is the accurate identification of the failed implant design, especially if one or more well-fixed/functioning components are to be retained. Manual identification of the implant design from preoperative radiographic images can be time-consuming and inaccurate, which can ultimately lead to increased operating room time, more complex surgery, and increased healthcare costs. METHOD In this study, we present a novel approach to identifying THR femoral implants' design from plain radiographs using a convolutional neural network (CNN). We evaluated a total of 402 radiographs of nine different THR implant designs including, Accolade II (130 radiographs), Corail (89 radiographs), M/L Taper (31 radiographs), Summit (31 radiographs), Anthology (26 radiographs), Versys (26 radiographs), S-ROM (24 radiographs), Taperloc Standard Offset (24 radiographs), and Taperloc High Offset (21 radiographs). We implemented a transfer learning approach and adopted a DenseNet-201 CNN architecture by replacing the final classifier with nine fully connected neurons. Furthermore, we used saliency maps to explain the CNN decision-making process by visualizing the most important pixels in a given radiograph on the CNN's outcome. We also compared the CNN's performance with three board-certified and fellowship-trained orthopedic surgeons. RESULTS The CNN achieved the same or higher performance than at least one of the surgeons in identifying eight of nine THR implant designs and underperformed all of the surgeons in identifying one THR implant design (Anthology). Overall, the CNN achieved a lower Cohen's kappa (0.78) than surgeon 1 (1.00), the same Cohen's kappa as surgeon 2 (0.78), and a slightly higher Cohen's kappa than surgeon 3 (0.76) in identifying all the nine THR implant designs. Furthermore, the saliency maps showed that the CNN generally focused on each implant's unique design features to make a decision. Regarding the time spent performing the implant identification, the CNN accomplished this task in ~0.06 s per radiograph. The surgeon's identification time varied based on the method they utilized. When using their personal experience to identify the THR implant design, they spent negligible time. However, the identification time increased to an average of 8.4 min (standard deviation 6.1 min) per radiograph when they used another identification method (online search, consulting with the orthopedic company representative, and using image atlas), which occurred in about 17% of cases in the test subset (40 radiographs). CONCLUSIONS CNNs such as the one developed in this study can be used to automatically identify the design of a failed THR femoral implant preoperatively in just a fraction of a second, saving time and in some cases improving identification accuracy.
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Affiliation(s)
- Alireza Borjali
- Department of Orthopaedic, Harris Orthopaedics Laboratory, Massachusetts General Hospital, Boston, MA, USA
- Department of Orthopaedic Surgery, Harvard Medical School, Boston, MA, USA
| | - Antonia F Chen
- Department of Orthopaedic Surgery, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, USA
| | - Hany S Bedair
- Department of Orthopaedics, Massachusetts General Hospital, Boston, MA, USA
- Kaplan Joint Center, Department of Orthopedics, Newton-Wellesley Hospital, Newton, MA, USA
| | - Christopher M Melnic
- Department of Orthopaedic Surgery, Harvard Medical School, Boston, MA, USA
- Department of Orthopaedics, Massachusetts General Hospital, Boston, MA, USA
- Kaplan Joint Center, Department of Orthopedics, Newton-Wellesley Hospital, Newton, MA, USA
| | - Orhun K Muratoglu
- Department of Orthopaedic, Harris Orthopaedics Laboratory, Massachusetts General Hospital, Boston, MA, USA
- Department of Orthopaedic Surgery, Harvard Medical School, Boston, MA, USA
| | - Mohammad A Morid
- Department of Information Systems and Analytics, Santa Clara University Leavey School of Business, Santa Clara, CA, USA
| | - Kartik M Varadarajan
- Department of Orthopaedic, Harris Orthopaedics Laboratory, Massachusetts General Hospital, Boston, MA, USA
- Department of Orthopaedic Surgery, Harvard Medical School, Boston, MA, USA
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Borjali A, Magnéli M, Shin D, Malchau H, Muratoglu OK, Varadarajan KM. Natural language processing with deep learning for medical adverse event detection from free-text medical narratives: A case study of detecting total hip replacement dislocation. Comput Biol Med 2020; 129:104140. [PMID: 33278631 DOI: 10.1016/j.compbiomed.2020.104140] [Citation(s) in RCA: 18] [Impact Index Per Article: 4.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/01/2020] [Revised: 11/18/2020] [Accepted: 11/19/2020] [Indexed: 12/11/2022]
Abstract
BACKGROUND Accurate and timely detection of medical adverse events (AEs) from free-text medical narratives can be challenging. Natural language processing (NLP) with deep learning has already shown great potential for analyzing free-text data, but its application for medical AE detection has been limited. METHOD In this study, we developed deep learning based NLP (DL-NLP) models for efficient and accurate hip dislocation AE detection following primary total hip replacement from standard (radiology notes) and non-standard (follow-up telephone notes) free-text medical narratives. We benchmarked these proposed models with traditional machine learning based NLP (ML-NLP) models, and also assessed the accuracy of International Classification of Diseases (ICD) and Current Procedural Terminology (CPT) codes in capturing these hip dislocation AEs in a multi-center orthopaedic registry. RESULTS All DL-NLP models outperformed all of the ML-NLP models, with a convolutional neural network (CNN) model achieving the best overall performance (Kappa = 0.97 for radiology notes, and Kappa = 1.00 for follow-up telephone notes). On the other hand, the ICD/CPT codes of the patients who sustained a hip dislocation AE were only 75.24% accurate. CONCLUSIONS We demonstrated that a DL-NLP model can be used in largescale orthopaedic registries for accurate and efficient detection of hip dislocation AEs. The NLP model in this study was developed with data from the most frequently used electronic medical record (EMR) system in the U.S., Epic. This NLP model could potentially be implemented in other Epic-based EMR systems to improve AE detection, and consequently, quality of care and patient outcomes.
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Affiliation(s)
- Alireza Borjali
- Department of Orthopaedic Surgery, Harris Orthopaedics Laboratory, Massachusetts General Hospital, Boston, MA, USA; Department of Orthopaedic Surgery, Harvard Medical School, Boston, MA, USA
| | - Martin Magnéli
- Department of Orthopaedic Surgery, Harris Orthopaedics Laboratory, Massachusetts General Hospital, Boston, MA, USA; Department of Orthopaedic Surgery, Harvard Medical School, Boston, MA, USA; Karolinska Institutet, Department of Clinical Sciences, Danderyd Hospital, Stockholm, Sweden
| | - David Shin
- Department of Orthopaedic Surgery, Harris Orthopaedics Laboratory, Massachusetts General Hospital, Boston, MA, USA
| | - Henrik Malchau
- Department of Orthopaedic Surgery, Harris Orthopaedics Laboratory, Massachusetts General Hospital, Boston, MA, USA; Department of Orthopaedic Surgery, Sahlgrenska University Hospital, Sweden
| | - Orhun K Muratoglu
- Department of Orthopaedic Surgery, Harris Orthopaedics Laboratory, Massachusetts General Hospital, Boston, MA, USA; Department of Orthopaedic Surgery, Harvard Medical School, Boston, MA, USA
| | - Kartik M Varadarajan
- Department of Orthopaedic Surgery, Harris Orthopaedics Laboratory, Massachusetts General Hospital, Boston, MA, USA; Department of Orthopaedic Surgery, Harvard Medical School, Boston, MA, USA.
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Abstract
Due to the increasing maintenance costs of hydraulic machines related to the damages caused by cavitation erosion and/or erosion of solid particles, as well as in tribological connections, surface protection of these components is very important. Up to now, numerous investigations of resistance of coatings, mainly nitride coatings, such as CrN, TiN, TiCN, (Ti,Cr)N coatings and multilayer TiN/Ti, ZrN/CrN and TN/(Ti,Al)N coatings, produced by physical vapor deposition (PVD) method using different techniques of deposition, such as magnetron sputtering, arc evaporation or ion plating, to cavitation erosion, solid particle erosion and wear have been made. The results of these investigations, degradation processes and main test devices used are presented in this paper. An effect of deposition of mono- and multi-layer PVD coatings on duration of incubation period, cumulative weight loss and erosion rate, as well as on wear rate and coefficient of friction in tribological tests is discussed. It is shown that PVD coating does not always provide extended incubation time and/or improved resistance to mentioned types of damage. The influence of structure, hardness, residence to plastic deformation and stresses in the coatings on erosion and wear resistance is discussed. In the case of cavitation erosion and solid particle erosion, a limit value of the ratio of hardness (H) to Young’s modulus (E) exists at which the best resistance is gained. In the case of tribological tests, the higher the H/E ratio and the lower the coefficient of friction, the lower the wear rate, but there are also many exceptions.
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Kurdi A, Alhazmi N, Alhazmi H, Tabbakh T. Practice of Simulation and Life Cycle Assessment in Tribology-A Review. MATERIALS (BASEL, SWITZERLAND) 2020; 13:E3489. [PMID: 32784652 PMCID: PMC7476053 DOI: 10.3390/ma13163489] [Citation(s) in RCA: 11] [Impact Index Per Article: 2.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 06/14/2020] [Revised: 07/28/2020] [Accepted: 08/04/2020] [Indexed: 02/01/2023]
Abstract
To simulate today's complex tribo-contact scenarios, a methodological breakdown of a complex design problem into simpler sub-problems is essential to achieve acceptable simulation outcomes. This also helps to manage iterative, hierarchical systems within given computational power. In this paper, the authors reviewed recent trends of simulation practices in tribology to model tribo-contact scenario and life cycle assessment (LCA) with the help of simulation. With the advancement of modern computers and computing power, increasing effort has been given towards simulation, which not only saves time and resources but also provides meaningful results. Having said that, like every other technique, simulation has some inherent limitations which need to be considered during practice. Keeping this in mind, the pros and cons of both physical experiments and simulation approaches are reviewed together with their interdependency and how one approach can benefit the other. Various simulation techniques are outlined with a focus on machine learning which will dominate simulation approaches in the future. In addition, simulation of tribo-contacts across different length scales and lubrication conditions is discussed in detail. An extension of the simulation approach, together with experimental data, can lead towards LCA of components which will provide us with a better understanding of the efficient usage of limited resources and conservation of both energy and resources.
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Affiliation(s)
- Abdulaziz Kurdi
- National Center for Building and Construction Technology, King Abdulaziz City for Science and Technology, P.O. Box 6086, Riyadh 11442, Saudi Arabia;
- Material Science Institute, King Abdulaziz City for Science and Technology, P.O. Box 6086, Riyadh 11442, Saudi Arabia;
| | - Nahla Alhazmi
- Material Science Institute, King Abdulaziz City for Science and Technology, P.O. Box 6086, Riyadh 11442, Saudi Arabia;
- National Center for Composite and High-Performance Materials, Center of Excellence for Aeronautics, King Abdulaziz City for Science and Technology, P.O. Box 6086, Riyadh 11442, Saudi Arabia
| | - Hatem Alhazmi
- National Center for Environmental Technology, King Abdulaziz City for Science and Technology, P.O. Box 6086, Riyadh 11442, Saudi Arabia;
| | - Thamer Tabbakh
- Material Science Institute, King Abdulaziz City for Science and Technology, P.O. Box 6086, Riyadh 11442, Saudi Arabia;
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Borjali A, Chen AF, Muratoglu OK, Morid MA, Varadarajan KM. Detecting total hip replacement prosthesis design on plain radiographs using deep convolutional neural network. J Orthop Res 2020; 38:1465-1471. [PMID: 31997411 DOI: 10.1002/jor.24617] [Citation(s) in RCA: 42] [Impact Index Per Article: 10.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 10/02/2019] [Revised: 09/12/2020] [Accepted: 01/13/2020] [Indexed: 02/04/2023]
Abstract
Identifying the design of a failed implant is a key step in the preoperative planning of revision total joint arthroplasty. Manual identification of the implant design from radiographic images is time-consuming and prone to error. Failure to identify the implant design preoperatively can lead to increased operating room time, more complex surgery, increased blood loss, increased bone loss, increased recovery time, and overall increased healthcare costs. In this study, we present a novel, fully automatic and interpretable approach to identify the design of total hip replacement (THR) implants from plain radiographs using deep convolutional neural network (CNN). CNN achieved 100% accuracy in the identification of three commonly used THR implant designs. Such CNN can be used to automatically identify the design of a failed THR implant preoperatively in just a few seconds, saving time and improving the identification accuracy. This can potentially improve patient outcomes, free practitioners' time, and reduce healthcare costs.
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Affiliation(s)
- Alireza Borjali
- Department of Orthopaedic Surgery, Harris Orthopaedics Laboratory, Massachusetts General Hospital, Boston, Massachusetts.,Department of Orthopaedic Surgery, Harvard Medical School, Boston, Massachusetts
| | - Antonia F Chen
- Department of Orthopaedic Surgery, Brigham and Women's Hospital, Harvard Medical School, Boston, Massachusetts
| | - Orhun K Muratoglu
- Department of Orthopaedic Surgery, Harris Orthopaedics Laboratory, Massachusetts General Hospital, Boston, Massachusetts.,Department of Orthopaedic Surgery, Harvard Medical School, Boston, Massachusetts
| | - Mohammad A Morid
- Department of Information Systems and Analytics, Santa Clara University Leavey School of Business, Santa Clara, California
| | - Kartik M Varadarajan
- Department of Orthopaedic Surgery, Harris Orthopaedics Laboratory, Massachusetts General Hospital, Boston, Massachusetts.,Department of Orthopaedic Surgery, Harvard Medical School, Boston, Massachusetts
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Borjali A, Chen AF, Muratoglu OK, Morid MA, Varadarajan KM. Deep Learning in Orthopedics: How Do We Build Trust in the Machine? ACTA ACUST UNITED AC 2020. [DOI: 10.1089/heat.2019.0006] [Citation(s) in RCA: 13] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/18/2022]
Affiliation(s)
- Alireza Borjali
- Department of Orthopaedic Surgery, Harvard Medical School, Boston, Massachusetts
- Harris Orthopaedics Laboratory, Department of Orthopaedic, Massachusetts General Hospital, Boston, Massachusetts
| | - Antonia F. Chen
- Department of Orthopaedic Surgery, Brigham and Women's Hospital, Harvard Medical School, Boston, Massachusetts
| | - Orhun K. Muratoglu
- Department of Orthopaedic Surgery, Harvard Medical School, Boston, Massachusetts
- Harris Orthopaedics Laboratory, Department of Orthopaedic, Massachusetts General Hospital, Boston, Massachusetts
| | - Mohammad A. Morid
- Department of Information Systems and Analytics, Santa Clara University Leavey School of Business, Santa Clara, California
| | - Kartik M. Varadarajan
- Department of Orthopaedic Surgery, Harvard Medical School, Boston, Massachusetts
- Harris Orthopaedics Laboratory, Department of Orthopaedic, Massachusetts General Hospital, Boston, Massachusetts
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