IDEAS home Printed from https://ideas.repec.org/a/gam/jmathe/v10y2022i16p2939-d888803.html
   My bibliography  Save this article

Fracture Recognition in Paediatric Wrist Radiographs: An Object Detection Approach

Author

Listed:
  • Franko Hržić

    (Department of Computer Engineering, Faculty of Engineering, University of Rijeka, Vukovarska 58, 51000 Rijeka, Croatia)

  • Sebastian Tschauner

    (Division of Pediatric Radiology, Department of Radiology, Medical University of Graz, Auenbruggerplatz 34, 8036 Graz, Austria)

  • Erich Sorantin

    (Division of Pediatric Radiology, Department of Radiology, Medical University of Graz, Auenbruggerplatz 34, 8036 Graz, Austria)

  • Ivan Štajduhar

    (Department of Computer Engineering, Faculty of Engineering, University of Rijeka, Vukovarska 58, 51000 Rijeka, Croatia
    Center for Artificial Intelligence and Cybersecurity, University of Rijeka, Radmile Matejčić 2, 51000 Rijeka, Croatia)

Abstract

Wrist fractures are commonly diagnosed using X-ray imaging, supplemented by magnetic resonance imaging and computed tomography when required. Radiologists can sometimes overlook the fractures because they are difficult to spot. In contrast, some fractures can be easily spotted and only slow down the radiologists because of the reporting systems. We propose a machine learning model based on the YOLOv4 method that can help solve these issues. The rigorous testing on three levels showed that the YOLOv4-based model obtained significantly better results in comparison to the state-of-the-art method based on the U-Net model. In the comparison against five radiologists, YOLO 512 Anchor model-AI (the best performing YOLOv4-based model) was significantly better than the four radiologists (AI AUC-ROC = 0.965 , Radiologist average AUC-ROC = 0.831 ± 0.075 ). Furthermore, we have shown that three out of five radiologists significantly improved their performance when aided by the AI model. Finally, we compared our work with other related work and discussed what to consider when building an ML-based predictive model for wrist fracture detection. All our findings are based on a complex dataset of 19,700 pediatric X-ray images.

Suggested Citation

  • Franko Hržić & Sebastian Tschauner & Erich Sorantin & Ivan Štajduhar, 2022. "Fracture Recognition in Paediatric Wrist Radiographs: An Object Detection Approach," Mathematics, MDPI, vol. 10(16), pages 1-23, August.
    • Handle: RePEc:gam:jmathe:v:10:y:2022:i:16:p:2939-:d:888803
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/2227-7390/10/16/2939/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/2227-7390/10/16/2939/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Quinn McNemar, 1947. "Note on the sampling error of the difference between correlated proportions or percentages," Psychometrika, Springer;The Psychometric Society, vol. 12(2), pages 153-157, June.
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Uttam Bandyopadhyay & Atanu Biswas & Shirsendu Mukherjee, 2009. "Adaptive two-treatment two-period crossover design for binary treatment responses incorporating carry-over effects," Statistical Methods & Applications, Springer;Società Italiana di Statistica, vol. 18(1), pages 13-33, March.
    2. Bester Tawona Mudereri & Elfatih M. Abdel-Rahman & Shepard Ndlela & Louisa Delfin Mutsa Makumbe & Christabel Chiedza Nyanga & Henri E. Z. Tonnang & Samira A. Mohamed, 2022. "Integrating the Strength of Multi-Date Sentinel-1 and -2 Datasets for Detecting Mango ( Mangifera indica L.) Orchards in a Semi-Arid Environment in Zimbabwe," Sustainability, MDPI, vol. 14(10), pages 1-23, May.
    3. Nosi, Costanza & D’Agostino, Antonella & Pratesi, Carlo Alberto & Barbarossa, Camilla, 2021. "Evaluating a social marketing campaign on healthy nutrition and lifestyle among primary-school children: A mixed-method research design," Evaluation and Program Planning, Elsevier, vol. 89(C).
    4. John E. Core, 2010. "Discussion of Chief Executive Officer Equity Incentives and Accounting Irregularities," Journal of Accounting Research, Wiley Blackwell, vol. 48(2), pages 273-287, May.
    5. Preety Srivastava & Xueyan Zhao, 2010. "What Do the Bingers Drink? Micro‐Unit Evidence on Negative Externalities and Drinker Characteristics of Alcohol Consumption by Beverage Types," Economic Papers, The Economic Society of Australia, vol. 29(2), pages 229-250, June.
    6. Hanousek Jan & Kočenda Evžen & Novotný Jan, 2012. "The identification of price jumps," Monte Carlo Methods and Applications, De Gruyter, vol. 18(1), pages 53-77, January.
    7. Monnery, Benjamin & Wolff, François-Charles & Henneguelle, Anaïs, 2020. "Prison, semi-liberty and recidivism: Bounding causal effects in a survival model," International Review of Law and Economics, Elsevier, vol. 61(C).
    8. Holger Schwender & Margaret A. Taub & Terri H. Beaty & Mary L. Marazita & Ingo Ruczinski, 2012. "Rapid Testing of SNPs and Gene–Environment Interactions in Case–Parent Trio Data Based on Exact Analytic Parameter Estimation," Biometrics, The International Biometric Society, vol. 68(3), pages 766-773, September.
    9. Matysková, Ludmila & Rogers, Brian & Steiner, Jakub & Sun, Keh-Kuan, 2020. "Habits as adaptations: An experimental study," Games and Economic Behavior, Elsevier, vol. 122(C), pages 391-406.
    10. André, Kévin, 2013. "Applying the Capability Approach to the French Education System: An Assessment of the "Pourquoi pas moi ?"," ESSEC Working Papers WP1316, ESSEC Research Center, ESSEC Business School.
    11. repec:hal:journl:hal-00880246 is not listed on IDEAS
    12. Ruiz-Frau, A. & Krause, T. & Marbà , N., 2018. "The use of sociocultural valuation in sustainable environmental management," Ecosystem Services, Elsevier, vol. 29(PA), pages 158-167.
    13. repec:cup:judgdm:v:8:y:2013:i:3:p:278-298 is not listed on IDEAS
    14. Shaub, David, 2020. "Fast and accurate yearly time series forecasting with forecast combinations," International Journal of Forecasting, Elsevier, vol. 36(1), pages 116-120.
    15. AlMalki, Hameeda A. & Durugbo, Christopher M., 2023. "Evaluating critical institutional factors of Industry 4.0 for education reform," Technological Forecasting and Social Change, Elsevier, vol. 188(C).
    16. Guevara, C. Angelo & Fukushi, Mitsuyoshi, 2016. "Modeling the decoy effect with context-RUM Models: Diagrammatic analysis and empirical evidence from route choice SP and mode choice RP case studies," Transportation Research Part B: Methodological, Elsevier, vol. 93(PA), pages 318-337.
    17. Bovi, M., 2005. "Economic Clubs and European Commitment. Evidence from the International Business Cycles," International Journal of Applied Econometrics and Quantitative Studies, Euro-American Association of Economic Development, vol. 2(2), pages 101-122.
    18. Glueck Deborah H & Mandel Jan & Karimpour-Fard Anis & Hunter Lawrence & Muller Keith E, 2008. "Exact Calculations of Average Power for the Benjamini-Hochberg Procedure," The International Journal of Biostatistics, De Gruyter, vol. 4(1), pages 1-20, June.
    19. Ali Bakdur & Fumito Masui & Michal Ptaszynski, 2021. "Predicting Increase in Demand for Public Buses in University Students Daily Life Needs: Case Study Based on a City in Japan," Sustainability, MDPI, vol. 13(9), pages 1-28, May.
    20. Alexandra I. Khalyasmaa & Pavel V. Matrenin & Stanislav A. Eroshenko & Vadim Z. Manusov & Andrey M. Bramm & Alexey M. Romanov, 2022. "Data Mining Applied to Decision Support Systems for Power Transformers’ Health Diagnostics," Mathematics, MDPI, vol. 10(14), pages 1-25, July.
    21. Arnaldo Rabello de Aguiar Vallim Filho & Daniel Farina Moraes & Marco Vinicius Bhering de Aguiar Vallim & Leilton Santos da Silva & Leandro Augusto da Silva, 2022. "A Machine Learning Modeling Framework for Predictive Maintenance Based on Equipment Load Cycle: An Application in a Real World Case," Energies, MDPI, vol. 15(10), pages 1-41, May.
    22. José Antonio Roldán-Nofuentes & Tulsi Sagar Sheth & José Fernando Vera-Vera, 2024. "Hypothesis Test to Compare Two Paired Binomial Proportions: Assessment of 24 Methods," Mathematics, MDPI, vol. 12(2), pages 1-24, January.

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:gam:jmathe:v:10:y:2022:i:16:p:2939-:d:888803. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: MDPI Indexing Manager (email available below). General contact details of provider: https://www.mdpi.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.