Fully automatic estimation of pelvic sagittal inclination from anterior-posterior radiography image using deep learning framework

Jodeiri, Ata; Zoroofi, Reza Aghaeizadeh; Hiasa, Yuta; Takao, Masaki; Sugano, Nobuhiko; Sato, Yoshinobu; Otake, Yoshito

doi:10.1016/j.cmpb.2019.105282

Cited by 22 publications

(15 citation statements)

References 21 publications

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“…However, we analyzed five parameters that were created from a variety of the landmarks visible on anteroposterior radiographs. Future research may provide a robust and accurate method to predict PSI from clinical radiographs using new computational technology, for example, deep learning 28 …”

Section: Discussionmentioning

confidence: 99%

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Can measurements from an anteroposterior radiograph predict pelvic sagittal inclination?

Uemura

Atkins

Okamoto

et al. 2020

Journal Orthopaedic Research

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Pelvic sagittal inclination (PSI) is often evaluated in patients with hip pathology using lateral radiographs. However, it would be useful if PSI could be predicted from an anteroposterior radiograph since this film is ubiquitous in the evaluation of hip pathology. Herein, computer‐modeling was applied to predict PSI from radiographic measurements assessed in the anteroposterior plane. Three‐dimensional surface models of the pelvis, femur, and sacrum were reconstructed from computed tomography images of 50 women with hip dysplasia. This study cohort was selected as changes in PSI alter femoral head coverage, which is relevant to the diagnosis and treatment of hip dysplasia, a known cause of hip osteoarthritis. Five radiographic parameters commonly used to independently estimate PSI were evaluated after bone surfaces were projected to an anteroposterior plane, including the symphysis to sacrococcygeal joint distance (S‐S distance), the pelvic foramen aspect ratio (PF ratio), the distance between the symphysis and a line connecting the femoral head centers (S‐H distance), the sacro‐femoral‐pubic angle (SFP angle), and the pelvic vertical ratio (PVR). Regression models determined the ability of these parameters to predict PSI from −20° to 20° at 1° increment. All five parameters showed a strong correlation with the PSI (all r > 0.9). From the regression models, PSI was estimated with a median (maximum) absolute error of 3.6° (18.4°), 3.8° (17.7°), 5.2° (17.9°), 5.8° (28.8°), and 3.2° (23.5°) for the S‐S distance, PF ratio, S‐H distance, SFP angle, and PVR, respectively. The regression model for S‐S distance had a mean slope of 2.18 that ranged from 1.98 to 2.41 when the sacrococcygeal joint was located superior to the symphysis. Results indicated that substantial errors occur when estimating the actual value of PSI from an anteroposterior radiograph. However, the change in PSI could be estimated from the S‐S distance, which may aid surgeons to successfully increase head coverage through periacetabular osteotomy and to locate the acetabular cup in a functional position for total hip arthroplasty.

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Section: Discussionmentioning

confidence: 99%

“…Future research may provide a robust and accurate method to predict PSI from clinical radiographs using new computational technology, for example, deep learning. 28…”

Section: Discussionmentioning

confidence: 99%

Can measurements from an anteroposterior radiograph predict pelvic sagittal inclination?

Uemura

Atkins

Okamoto

et al. 2020

Journal Orthopaedic Research

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“…Sixteen studies (32.7%) evaluated AI/ML applications to accurately predict patient reoperations, operating time, hospital LOS, discharges, readmissions, or surgical and inpatient costs [ [14] , [15] , [16] , [17] , [18] , [19] , [20] , [21] , [22] , [23] , [24] , [25] , [26] , [27] , [28] , [29] ]. In addition, 16 studies (32.7%) used patients’ preoperative risk factors and other patient-specific variables to optimize the patient selection and surgical planning process through the use of AI/ML-based predictions of surgical outcomes and postoperative complications [ [30] , [31] , [32] , [33] , [34] , [35] , [36] , [37] , [38] , [39] , [40] , [41] , [42] , [43] , [44] ]. The majority of the decision support studies evaluated AI/ML model performance using receiver operating characteristic/AUC, accuracy, sensitivity, and specificity.…”

Section: Resultsmentioning

confidence: 99%

“…Hip and knee arthroplasty typically involve an older and highly comorbid patient population, and these tools can be especially helpful in identifying patient-specific needs and risks within this population. Examples of how these models can enable providers to create and optimize personalized treatment plans include accurate identification of an implant from a previous surgery for revision procedures and classifying total knee arthroplasty (TKA) surgical candidates based on patient-specific risk factors [29][30][31][33][34][35][37][38][39][40][41][42]44,62]. Hyer et al demonstrated an AI/ML model which classified TKA and total hip arthroplasty patients based on surgical complexity scores [19].…”

Section: Discussionmentioning

confidence: 99%

Artificial Learning and Machine Learning Decision Guidance Applications in Total Hip and Knee Arthroplasty: A Systematic Review

et al. 2021

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Background: Artificial intelligence (AI) and machine learning (ML) modeling in hip and knee arthroplasty (total joint arthroplasty [TJA]) is becoming more commonplace. This systematic review aims to quantify the accuracy of current AI-and ML-based application for cognitive support and decision-making in TJA. Methods: A comprehensive search of publications was conducted through the EMBASE, Medline, and PubMed databases using relevant keywords to maximize the sensitivity of the search. No limits were placed on level of evidence or timing of the study. Findings were reported according to the PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) guidelines. Analysis of variance testing with post-hoc Tukey test was applied to compare the area under the curve (AUC) of the models. Results: After application of inclusion and exclusion criteria, 49 studies were included in this review. The application of AI/ML-based models and average AUC is as follows: cost prediction-0.77, LOS and discharges-0.78, readmissions and reoperations-0.66, preoperative patient selection/planning-0.79, adverse events and other postoperative complications-0.84, postoperative pain-0.83, postoperative patient-reported outcomes measures and functional outcome-0.81. Significant variability in model AUC across the different decision support applications was found (P < .001) with the AUC for readmission and reoperation models being significantly lower than that of the other decision support categories. Conclusions: AI/ML-based applications in TJA continue to expand and have the potential to optimize patient selection and accurately predict postoperative outcomes, complications, and associated costs. On average, the AI/ML models performed best in predicting postoperative complications, pain, and patientreported outcomes and were less accurate in predicting hospital readmissions and reoperations.

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“…This study demonstrated encouraging results, with an accuracy rate of 80%. Future development of this model will enable the recognition of individual dynamic changes of PSI to enable patient-specific placement of the acetabular component in patients undergoing THA [ 51 ].…”

Section: Introductionmentioning

confidence: 99%

Artificial intelligence in arthroplasty

2021

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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.

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Fully automatic estimation of pelvic sagittal inclination from anterior-posterior radiography image using deep learning framework

Cited by 22 publications

References 21 publications

Can measurements from an anteroposterior radiograph predict pelvic sagittal inclination?

Can measurements from an anteroposterior radiograph predict pelvic sagittal inclination?

Artificial Learning and Machine Learning Decision Guidance Applications in Total Hip and Knee Arthroplasty: A Systematic Review

Artificial intelligence in arthroplasty

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