1
|
Cheong RCT, Jawad S, Adams A, Campion T, Lim ZH, Papachristou N, Unadkat S, Randhawa P, Joseph J, Andrews P, Taylor P, Kunz H. Enhancing paranasal sinus disease detection with AutoML: efficient AI development and evaluation via magnetic resonance imaging. Eur Arch Otorhinolaryngol 2024; 281:2153-2158. [PMID: 38197934 PMCID: PMC10942883 DOI: 10.1007/s00405-023-08424-9] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/24/2023] [Accepted: 12/18/2023] [Indexed: 01/11/2024]
Abstract
PURPOSE Artificial intelligence (AI) in the form of automated machine learning (AutoML) offers a new potential breakthrough to overcome the barrier of entry for non-technically trained physicians. A Clinical Decision Support System (CDSS) for screening purposes using AutoML could be beneficial to ease the clinical burden in the radiological workflow for paranasal sinus diseases. METHODS The main target of this work was the usage of automated evaluation of model performance and the feasibility of the Vertex AI image classification model on the Google Cloud AutoML platform to be trained to automatically classify the presence or absence of sinonasal disease. The dataset is a consensus labelled Open Access Series of Imaging Studies (OASIS-3) MRI head dataset by three specialised head and neck consultant radiologists. A total of 1313 unique non-TSE T2w MRI head sessions were used from the OASIS-3 repository. RESULTS The best-performing image classification model achieved a precision of 0.928. Demonstrating the feasibility and high performance of the Vertex AI image classification model to automatically detect the presence or absence of sinonasal disease on MRI. CONCLUSION AutoML allows for potential deployment to optimise diagnostic radiology workflows and lay the foundation for further AI research in radiology and otolaryngology. The usage of AutoML could serve as a formal requirement for a feasibility study.
Collapse
Affiliation(s)
- Ryan Chin Taw Cheong
- Royal National ENT and Eastman Dental Hospitals, University College London Hospitals NHS, London, UK
| | - Susan Jawad
- Royal National ENT and Eastman Dental Hospitals, University College London Hospitals NHS, London, UK
| | | | | | | | - Nikolaos Papachristou
- Medical Physics and Digital Innovation Laboratory, School of Medicine, Aristotle University of Thessaloniki, Thessaloniki, Greece
| | - Samit Unadkat
- Royal National ENT and Eastman Dental Hospitals, University College London Hospitals NHS, London, UK
| | - Premjit Randhawa
- Royal National ENT and Eastman Dental Hospitals, University College London Hospitals NHS, London, UK
| | - Jonathan Joseph
- Royal National ENT and Eastman Dental Hospitals, University College London Hospitals NHS, London, UK
| | - Peter Andrews
- Royal National ENT and Eastman Dental Hospitals, University College London Hospitals NHS, London, UK
| | | | - Holger Kunz
- University College London, London, UK.
- School of Public Health, Imperial College London, London, UK.
| |
Collapse
|
2
|
Shah AK, Lavu MS, Hecht CJ, Burkhart RJ, Kamath AF. Understanding the use of artificial intelligence for implant analysis in total joint arthroplasty: a systematic review. ARTHROPLASTY 2023; 5:54. [PMID: 37919812 PMCID: PMC10623774 DOI: 10.1186/s42836-023-00209-z] [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: 08/03/2023] [Accepted: 09/01/2023] [Indexed: 11/04/2023] Open
Abstract
INTRODUCTION In recent years, there has been a significant increase in the development of artificial intelligence (AI) algorithms aimed at reviewing radiographs after total joint arthroplasty (TJA). This disruptive technology is particularly promising in the context of preoperative planning for revision TJA. Yet, the efficacy of AI algorithms regarding TJA implant analysis has not been examined comprehensively. METHODS PubMed, EBSCO, and Google Scholar electronic databases were utilized to identify all studies evaluating AI algorithms related to TJA implant analysis between 1 January 2000, and 27 February 2023 (PROSPERO study protocol registration: CRD42023403497). The mean methodological index for non-randomized studies score was 20.4 ± 0.6. We reported the accuracy, sensitivity, specificity, positive predictive value, and area under the curve (AUC) for the performance of each outcome measure. RESULTS Our initial search yielded 374 articles, and a total of 20 studies with three main use cases were included. Sixteen studies analyzed implant identification, two addressed implant failure, and two addressed implant measurements. Each use case had a median AUC and accuracy above 0.90 and 90%, respectively, indicative of a well-performing AI algorithm. Most studies failed to include explainability methods and conduct external validity testing. CONCLUSION These findings highlight the promising role of AI in recognizing implants in TJA. Preliminary studies have shown strong performance in implant identification, implant failure, and accurately measuring implant dimensions. Future research should follow a standardized guideline to develop and train models and place a strong emphasis on transparency and clarity in reporting results. LEVEL OF EVIDENCE Level III.
Collapse
Affiliation(s)
- Aakash K Shah
- Department of Orthopaedic Surgery, Cleveland Clinic Foundation, Cleveland, OH, 44195, USA
| | - Monish S Lavu
- Department of Orthopaedic Surgery, Cleveland Clinic Foundation, Cleveland, OH, 44195, USA
| | - Christian J Hecht
- Department of Orthopaedic Surgery, Cleveland Clinic Foundation, Cleveland, OH, 44195, USA
| | - Robert J Burkhart
- Department of Orthopaedic Surgery, University Hospitals, Cleveland, OH, 44106, USA
| | - Atul F Kamath
- Department of Orthopaedic Surgery, Cleveland Clinic Foundation, Cleveland, OH, 44195, USA.
- Center for Hip Preservation, Orthopaedic and Rheumatologic Institute, Cleveland Clinic Foundation, 9500 Euclid Avenue, Mail Code A41, Cleveland, OH, 44195, USA.
| |
Collapse
|
3
|
Automated Detection of Surgical Implants on Plain Knee Radiographs Using a Deep Learning Algorithm. Medicina (B Aires) 2022; 58:medicina58111677. [DOI: 10.3390/medicina58111677] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/07/2022] [Revised: 11/16/2022] [Accepted: 11/17/2022] [Indexed: 11/22/2022] Open
Abstract
Background and Objectives: The number of patients who undergo multiple operations on a knee is increasing. The objective of this study was to develop a deep learning algorithm that could detect 17 different surgical implants on plain knee radiographs. Materials and Methods: An internal dataset consisted of 5206 plain knee antero-posterior X-rays from a single, tertiary institute for model development. An external set contained 238 X-rays from another tertiary institute. A total of 17 different types of implants including total knee arthroplasty, unicompartmental knee arthroplasty, plate, and screw were labeled. The internal dataset was approximately split into a train set, a validation set, and an internal test set at a ratio of 7:1:2. You Only look Once (YOLO) was selected as the detection network. Model performances with the validation set, internal test set, and external test set were compared. Results: Total accuracy, total sensitivity, total specificity value of the validation set, internal test set, and external test set were (0.978, 0.768, 0.999), (0.953, 0.810, 0.990), and (0.956, 0.493, 0.975), respectively. Means ± standard deviations (SDs) of diagonal components of confusion matrix for these three subsets were 0.858 ± 0.242, 0.852 ± 0.182, and 0.576 ± 0.312, respectively. True positive rate of total knee arthroplasty, the most dominant class of the dataset, was higher than 0.99 with internal subsets and 0.96 with an external test set. Conclusion: Implant identification on plain knee radiographs could be automated using a deep learning technique. The detection algorithm dealt with overlapping cases while maintaining high accuracy on total knee arthroplasty. This could be applied in future research that analyzes X-ray images with deep learning, which would help prompt decision-making in clinics.
Collapse
|
4
|
Gurung B, Liu P, Harris PDR, Sagi A, Field RE, Sochart DH, Tucker K, Asopa V. Artificial intelligence for image analysis in total hip and total knee arthroplasty : a scoping review. Bone Joint J 2022; 104-B:929-937. [PMID: 35909383 DOI: 10.1302/0301-620x.104b8.bjj-2022-0120.r2] [Citation(s) in RCA: 13] [Impact Index Per Article: 6.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 12/22/2022]
Abstract
AIMS Total hip arthroplasty (THA) and total knee arthroplasty (TKA) are common orthopaedic procedures requiring postoperative radiographs to confirm implant positioning and identify complications. Artificial intelligence (AI)-based image analysis has the potential to automate this postoperative surveillance. The aim of this study was to prepare a scoping review to investigate how AI is being used in the analysis of radiographs following THA and TKA, and how accurate these tools are. METHODS The Embase, MEDLINE, and PubMed libraries were systematically searched to identify relevant articles. The Preferred Reporting Items for Systematic Reviews and Meta-Analyses extension for scoping reviews and Arksey and O'Malley framework were followed. Study quality was assessed using a modified Methodological Index for Non-Randomized Studies tool. AI performance was reported using either the area under the curve (AUC) or accuracy. RESULTS Of the 455 studies identified, only 12 were suitable for inclusion. Nine reported implant identification and three described predicting risk of implant failure. Of the 12, three studies compared AI performance with orthopaedic surgeons. AI-based implant identification achieved AUC 0.992 to 1, and most algorithms reported an accuracy > 90%, using 550 to 320,000 training radiographs. AI prediction of dislocation risk post-THA, determined after five-year follow-up, was satisfactory (AUC 76.67; 8,500 training radiographs). Diagnosis of hip implant loosening was good (accuracy 88.3%; 420 training radiographs) and measurement of postoperative acetabular angles was comparable to humans (mean absolute difference 1.35° to 1.39°). However, 11 of the 12 studies had several methodological limitations introducing a high risk of bias. None of the studies were externally validated. CONCLUSION These studies show that AI is promising. While it already has the ability to analyze images with significant precision, there is currently insufficient high-level evidence to support its widespread clinical use. Further research to design robust studies that follow standard reporting guidelines should be encouraged to develop AI models that could be easily translated into real-world conditions. Cite this article: Bone Joint J 2022;104-B(8):929-937.
Collapse
Affiliation(s)
- Binay Gurung
- South West London Elective Orthopaedic Centre, Epsom, UK
| | - Perry Liu
- South West London Elective Orthopaedic Centre, Epsom, UK
| | | | - Amit Sagi
- South West London Elective Orthopaedic Centre, Epsom, UK.,Barzilai Medical Centre, Ashkelon, Israel
| | - Richard E Field
- South West London Elective Orthopaedic Centre, Epsom, UK.,St George's, University of London, London, UK
| | | | - Keith Tucker
- South West London Elective Orthopaedic Centre, Epsom, UK.,Orthopaedics Data Evaluation Panel, London, UK
| | - Vipin Asopa
- South West London Elective Orthopaedic Centre, Epsom, UK
| |
Collapse
|
5
|
Sultan H, Owais M, Choi J, Mahmood T, Haider A, Ullah N, Park KR. Artificial Intelligence-Based Solution in Personalized Computer-Aided Arthroscopy of Shoulder Prostheses. J Pers Med 2022; 12:jpm12010109. [PMID: 35055427 PMCID: PMC8780458 DOI: 10.3390/jpm12010109] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/01/2021] [Revised: 12/30/2021] [Accepted: 01/07/2022] [Indexed: 01/01/2023] Open
Abstract
Background: Early recognition of prostheses before reoperation can reduce perioperative morbidity and mortality. Because of the intricacy of the shoulder biomechanics, accurate classification of implant models before surgery is fundamental for planning the correct medical procedure and setting apparatus for personalized medicine. Expert surgeons usually use X-ray images of prostheses to set the patient-specific apparatus. However, this subjective method is time-consuming and prone to errors. Method: As an alternative, artificial intelligence has played a vital role in orthopedic surgery and clinical decision-making for accurate prosthesis placement. In this study, three different deep learning-based frameworks are proposed to identify different types of shoulder implants in X-ray scans. We mainly propose an efficient ensemble network called the Inception Mobile Fully-Connected Convolutional Network (IMFC-Net), which is comprised of our two designed convolutional neural networks and a classifier. To evaluate the performance of the IMFC-Net and state-of-the-art models, experiments were performed with a public data set of 597 de-identified patients (597 shoulder implants). Moreover, to demonstrate the generalizability of IMFC-Net, experiments were performed with two augmentation techniques and without augmentation, in which our model ranked first, with a considerable difference from the comparison models. A gradient-weighted class activation map technique was also used to find distinct implant characteristics needed for IMFC-Net classification decisions. Results: The results confirmed that the proposed IMFC-Net model yielded an average accuracy of 89.09%, a precision rate of 89.54%, a recall rate of 86.57%, and an F1.score of 87.94%, which were higher than those of the comparison models. Conclusion: The proposed model is efficient and can minimize the revision complexities of implants.
Collapse
|