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

Sultan, Haseeb; Owais, Muhammad; Park, Chanhum; Mahmood, Tahir; Haider, Adnan; Park, Kang Ryoung

doi:10.3390/jpm11060482

Cited by 30 publications

(29 citation statements)

References 55 publications

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“…The results obtained in studies where DL algorithms were proposed are 80% and above [ 13 , 15 , 16 , 17 , 20 ]. In our study, the highest test accuracy values obtained with DenseNet201 + LR, DenseNet201 + LSVM, DenseNet201 + MLP and DenseNet201 + AB algorithms ranged from 96% to 85%.…”

Section: Resultsmentioning

confidence: 99%

“…The Squeeze and Excitation block integrated into the ResNet module was used in the X-Net model, and the X-Net model achieved 82% accuracy. Sultan et al [ 16 ], in 2021, proposed the DRE-Net network, which consists of combining modified ResNet and DenseNet networks to divide shoulder implants into four classes. An accuracy of 85.92% was achieved with the DRE-Net network.…”

Section: Introductionmentioning

confidence: 99%

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A Novel Hybrid Machine Learning Based System to Classify Shoulder Implant Manufacturers

et al. 2022

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It is necessary to know the manufacturer and model of a previously implanted shoulder prosthesis before performing Total Shoulder Arthroplasty operations, which may need to be performed repeatedly in accordance with the need for repair or replacement. In cases where the patient’s previous records cannot be found, where the records are not clear, or the surgery was conducted abroad, the specialist should identify the implant manufacturer and model during preoperative X-ray controls. In this study, an auxiliary expert system is proposed for classifying manufacturers of shoulder implants on the basis of X-ray images that is automated, objective, and based on hybrid machine learning models. In the proposed system, ten different hybrid models consisting of a combination of deep learning and machine learning algorithms were created and statistically tested. According to the experimental results, an accuracy of 95.07% was achieved using the DenseNet201 + Logistic Regression model, one of the proposed hybrid machine learning models (p < 0.05). The proposed hybrid machine learning algorithms achieve the goal of low cost and high performance compared to other studies in the literature. The results lead the authors to believe that the proposed system could be used in hospitals as an automatic and objective system for assisting orthopedists in the rapid and effective determination of shoulder implant types before performing revision surgery.

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Section: Resultsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

A Novel Hybrid Machine Learning Based System to Classify Shoulder Implant Manufacturers

et al. 2022

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“…Several studies have found that DL is superior to experts in terms of the interpretation of images. 4,38 For example, Sultan et al 38 developed a DL system that identifies shoulder arthroplasty implants on plain radiographs and compared it with a human expert. This algorithm was shown to be useful to less experienced and even experienced surgeons for the identification of previously inserted implants before revision surgery.…”

Section: Discussionmentioning

confidence: 99%

Prediction of Retear After Arthroscopic Rotator Cuff Repair Based on Intraoperative Arthroscopic Images Using Deep Learning

Cho

Kim

2023

Am J Sports Med

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Background: It is challenging to predict retear after arthroscopic rotator cuff repair (ARCR). The usefulness of arthroscopic intraoperative images as predictors of the ARCR prognosis has not been analyzed. Purpose: To evaluate the usefulness of arthroscopic images for the prediction of retear after ARCR using deep learning (DL) algorithms. Study Design: Cohort study (Diagnosis); Level of evidence, 2. Methods: In total, 1394 arthroscopic intraoperative images were retrospectively obtained from 580 patients. Repaired tendon integrity was evaluated using magnetic resonance imaging performed within 2 years after surgery. Images obtained immediately after ARCR were included. We used 3 DL architectures to predict retear based on arthroscopic images. Three pretrained DL algorithms (VGG16, DenseNet, and Xception) were used for transfer learning. Training and test sets were split into 8:2. Threefold stratified validation was used to fine-tune the hyperparameters using the training data set. The validation results of each fold were evaluated. The performance of each model in the test set was evaluated in terms of accuracy, area under the receiver operating characteristic curve (AUC), F1-score, sensitivity, and specificity. Results: In total, 1138 and 256 arthroscopic images were obtained from 514 patients and 66 patients in the nonretear and retear groups, respectively. The mean validation accuracy of each model was 83% for VGG16, 89% for Xception, and 91% for DenseNet. The accuracy for the test set was 76% for VGG16, 87% for Xception, and 91% for DenseNet. The AUC was highest for DenseNet (0.92); it was 0.83 for VGG16 and 0.91 for Xception. For the test set, the specificity and sensitivity were 0.93 and 0.84 for DenseNet, 0.89 and 0.84 for Xception, and 0.70 and 0.80 for VGG16, respectively. Conclusion: The application of DL algorithms to intraoperative arthroscopic images has demonstrated a high level of accuracy in predicting retear occurrences.

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“…DL has become one of the most exciting and popularized areas for clinical applications of AI in orthopaedics 28,68,74 as evidenced by the considerable increase in number of investigators applying this technology for common clinical problems 29,75–96 (Table III). Examples of clinically relevant applications of AI in imaging include the surveillance for prosthesis loosening/failure and brand recognition of different prosthetic implants 29,75–96 (Table III).…”

Section: Medical Imagingmentioning

confidence: 99%

Enabling Personalized Medicine in Orthopaedic Surgery Through Artificial Intelligence

Huffman,

Pasqualini,

Khan

et al. 2024

JBJS Reviews

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» The application of artificial intelligence (AI) in the field of orthopaedic surgery holds potential for revolutionizing health care delivery across 3 crucial domains: (I) personalized prediction of clinical outcomes and adverse events, which may optimize patient selection, surgical planning, and enhance patient safety and outcomes; (II) diagnostic automated and semiautomated imaging analyses, which may reduce time burden and facilitate precise and timely diagnoses; and (III) forecasting of resource utilization, which may reduce health care costs and increase value for patients and institutions.» Computer vision is one of the most highly studied areas of AI within orthopaedics, with applications pertaining to fracture classification, identification of the manufacturer and model of prosthetic implants, and surveillance of prosthesis loosening and failure.» Prognostic applications of AI within orthopaedics include identifying patients who will likely benefit from a specified treatment, predicting prosthetic implant size, postoperative length of stay, discharge disposition, and surgical complications. Not only may these applications be beneficial to patients but also to institutions and payors because they may inform potential cost expenditure, improve overall hospital efficiency, and help anticipate resource utilization.» AI infrastructure development requires institutional financial commitment and a team of clinicians and data scientists with expertise in AI that can complement skill sets and knowledge. Once a team is established and a goal is determined, teams (1) obtain, curate, and label data; (2) establish a reference standard; (3) develop an AI model; (4) evaluate the performance of the AI model; (5) externally validate the model, and (6) reinforce, improve, and evaluate the model's performance until clinical implementation is possible.» Understanding the implications of AI in orthopaedics may eventually lead to wide-ranging improvements in patient care. However, AI, while holding tremendous promise, is not without methodological and ethical limitations that are essential to address. First, it is important to ensure external validity of programs before their use in a clinical setting. Investigators should maintain high quality data records and registry surveillance, exercise caution when evaluating others' reported AI applications, and increase transparency of the methodological conduct of current models to improve external validity and avoid propagating bias. By addressing these challenges and responsibly embracing the potential of AI, the medical field may eventually be able to harness its power to improve patient care and outcomes.

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

Cited by 30 publications

References 55 publications

A Novel Hybrid Machine Learning Based System to Classify Shoulder Implant Manufacturers

A Novel Hybrid Machine Learning Based System to Classify Shoulder Implant Manufacturers

Prediction of Retear After Arthroscopic Rotator Cuff Repair Based on Intraoperative Arthroscopic Images Using Deep Learning

Enabling Personalized Medicine in Orthopaedic Surgery Through Artificial Intelligence

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