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Automated diagnosis of flatfoot using cascaded convolutional neural network for angle measurements in weight-bearing lateral radiographs

  • Musculoskeletal
  • Published:
European Radiology Aims and scope Submit manuscript

Abstract

Objectives

Diagnosis of flatfoot using a radiograph is subject to intra- and inter-observer variabilities. Here, we developed a cascade convolutional neural network (CNN)–based deep learning model (DLM) for an automated angle measurement for flatfoot diagnosis using landmark detection.

Methods

We used 1200 weight-bearing lateral foot radiographs from young adult Korean males for the model development. An experienced orthopedic surgeon identified 22 radiographic landmarks and measured three angles for flatfoot diagnosis that served as the ground truth (GT). Another orthopedic surgeon (OS) and a general physician (GP) independently identified the landmarks of the test dataset and measured the angles using the same method. External validation was performed using 100 and 17 radiographs acquired from a tertiary referral center and a public database, respectively.

Results

The DLM showed smaller absolute average errors from the GT for the three angle measurements for flatfoot diagnosis compared with both human observers. Under the guidance of the DLM, the average errors of observers OS and GP decreased from 2.35° ± 3.01° to 1.55° ± 2.09° and from 1.99° ± 2.76° to 1.56° ± 2.19°, respectively (both p < 0.001). The total measurement time decreased from 195 to 135 min in observer OS and from 205 to 155 min in observer GP. The absolute average errors of the DLM in the external validation sets were similar or superior to those of human observers in the original test dataset.

Conclusions

Our CNN model had significantly better accuracy and reliability than human observers in diagnosing flatfoot, and notably improved the accuracy and reliability of human observers.

Key Points

• Development of deep learning model (DLM) that allows automated angle measurements for landmark detection based on 1200 weight-bearing lateral radiographs for diagnosing flatfoot.

• Our DLM showed smaller absolute average errors for flatfoot diagnosis compared with two human observers.

• Under the guidance of the model, the average errors of two human observers decreased and total measurement time also decreased from 195 to 135 min and from 205 to 155 min.

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Data availability

The dataset of the Military Manpower Administration and our institution cannot be disclosed due to the National Personal Information Protection Act. However, the Lower Extremity RAdiographs (LERA) dataset is publicly available. The codes are available. (https://github.com/kevinkwshin/FlatNet).

Abbreviations

AUROC:

Area under the receiver operating characteristic curve

CNN:

Convolutional neural network

CPA:

Calcaneal pitch angle

DLM:

Deep learning model

GP:

General physician

GT:

Ground truth

ICC:

Intraclass correlation coefficient

LERA:

Lower Extremity Radiographs (public dataset)

MMA:

Military Manpower Administration

OS:

Orthopedic surgeon

SD:

Standard deviation

TCA:

Talocalcaneal angle

TMA:

Talo-first metatarsal angle

References

  1. Lee MS, Vanore JV, Thomas JL et al (2005) Diagnosis and treatment of adult flatfoot. J Foot Ankle Surg 44:78–113

    Article  PubMed  Google Scholar 

  2. Walters JL, Mendicino SS (2014) The flexible adult flatfoot: anatomy and pathomechanics. Clin Podiatr Med Surg 31:329–336

    Article  PubMed  Google Scholar 

  3. Rose GK, Welton EA, Marshall T (1985) The diagnosis of flat foot in the child. J Bone Joint Surg Br 67:71–78

    Article  CAS  PubMed  Google Scholar 

  4. Harris EJ, Vanore JV, Thomas JL et al (2004) Diagnosis and treatment of pediatric flatfoot. J Foot Ankle Surg 43:341–373

    Article  PubMed  Google Scholar 

  5. Kliegman B (2011) Nelson textbook of pediatrics, 19th edn. Elsevier, Philadelphia, pp 2335–2344

    Google Scholar 

  6. Pfeiffer M, Kotz R, Ledl T, Hauser G, Sluga M (2006) Prevalence of flat foot in preschool-aged children. Pediatrics 118:634–639

    Article  PubMed  Google Scholar 

  7. Simkin A, Leichter I, Giladi M, Stein M, Milgrom C (1989) Combined effect of foot arch structure and an orthotic device on stress fractures. Foot Ankle 10:25–29

    Article  CAS  PubMed  Google Scholar 

  8. Tomaru Y, Kamada H, Tsukagoshi Y et al (2019) Screening for musculoskeletal problems in children using a questionnaire. J Orthop Sci 24:159–165

    Article  PubMed  Google Scholar 

  9. Lee EC, Kim MO, Kim HS, Hong SE (2017) Changes in resting calcaneal stance position angle following insole fitting in children with flexible flatfoot. Ann Rehabil Med 41:257–265

    Article  PubMed  PubMed Central  Google Scholar 

  10. Bok SK, Kim BO, Lim JH, Ahn SY (2014) Effects of custom-made rigid foot orthosis on pes planus in children over 6 years old. Ann Rehabil Med 38:369–375

    Article  PubMed  PubMed Central  Google Scholar 

  11. Hohmann E, Myburgh J, Keough N, Tetsworth K, Glatt V (2019) Inter- and intraclass correlations for three standard foot radiographic measurements for plantar surface angles. Which measure is most reliable? Foot Ankle Surg 25:646–653

    Article  PubMed  Google Scholar 

  12. Shelton TJ, Singh S, Bent Robinson E et al (2019) The influence of percentage weight-bearing on foot radiographs. Foot Ankle Spec 12:363–369

    Article  PubMed  Google Scholar 

  13. Tao X, Chen W, Tang K (2019) Surgical procedures for treatment of adult acquired flatfoot deformity: a network meta-analysis. J Orthop Surg Res 14:62

    Article  PubMed  PubMed Central  Google Scholar 

  14. Abousayed MM, Alley MC, Shakked R, Rosenbaum AJ (2017) Adult-acquired flatfoot deformity: etiology, diagnosis, and management. JBJS Rev 5:e7

    Article  PubMed  Google Scholar 

  15. Gould N (1982) Graphing the adult foot and ankle. Foot Ankle 2:213–219

    Article  CAS  PubMed  Google Scholar 

  16. Davids JR, Gibson TW, Pugh LI (2005) Quantitative segmental analysis of weight-bearing radiographs of the foot and ankle for children: normal alignment. J Pediatr Orthop 25:769–776

    Article  PubMed  Google Scholar 

  17. Steel MW 3rd, Johnson KA, DeWitz MA, Ilstrup DM (1980) Radiographic measurements of the normal adult foot. Foot Ankle 1:151–158

    Article  PubMed  Google Scholar 

  18. Aronson J, Nunley J, Frankovitch K (1983) Lateral talocalcaneal angle in assessment of subtalar valgus: follow-up of seventy Grice-Green arthrodeses. Foot Ankle 4:56–63

    Article  CAS  PubMed  Google Scholar 

  19. Okuda R, Kinoshita M, Yasuda T, Jotoku T, Kitano N, Shima H (2007) The shape of the lateral edge of the first metatarsal head as a risk factor for recurrence of hallux valgus. J Bone Joint Surg Am 89:2163–2172

    Article  PubMed  Google Scholar 

  20. Lee KT, Kim KC, Park YU, Kim TW, Lee YK (2011) Radiographic evaluation of foot structure following fifth metatarsal stress fracture. Foot Ankle Int 32:796–801

    Article  PubMed  Google Scholar 

  21. Kido M, Ikoma K, Ikeda R et al (2020) Reproducibility of radiographic methods for assessing longitudinal tarsal axes Part 2: Severe cavus or flatfoot study. Foot (Edinb) 42:101631

    Article  PubMed  Google Scholar 

  22. Bock P Pittermann M Chraim M Rois S (2018) The inter- and intraobserver reliability for the radiological parameters of flatfoot, before and after surgery. Bone Joint J 100-B 596–602

  23. Kao EF, Lu CY, Wang CY, Yeh WC, Hsia PK (2018) Fully automated determination of arch angle on weight-bearing foot radiograph. Comput Methods Programs Biomed 154:79–88

    Article  PubMed  Google Scholar 

  24. Kim IH, Kim YG, Kim S, Park JW, Kim N (2021) Comparing intra-observer variation and external variations of a fully automated cephalometric analysis with a cascade convolutional neural net. Sci Rep 11:7925

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  25. Kim J, Kim I, Kim YJ et al (2021) Accuracy of automated identification of lateral cephalometric landmarks using cascade convolutional neural networks on lateral cephalograms from nationwide multi-centres. Orthod Craniofac Res 24(Suppl 2):59–67

    Article  PubMed  Google Scholar 

  26. Guo J, Mu Y, Xue D et al (2021) Automatic analysis system of calcaneus radiograph: Rotation-invariant landmark detection for calcaneal angle measurement, fracture identification and fracture region segmentation. Comput Methods Programs Biomed 206:106124

    Article  PubMed  Google Scholar 

  27. Yang W, Ye Q, Ming S et al (2020) Feasibility of automatic measurements of hip joints based on pelvic radiography and a deep learning algorithm. Eur J Radiol 132:109303

    Article  PubMed  Google Scholar 

  28. Ryu SM, Shin K, Shin SW et al (2022) Automated landmark identification for diagnosis of the deformity using a cascade convolutional neural network (FlatNet) on weight-bearing lateral radiographs of the foot. Comput Biol Med 148:105914

    Article  PubMed  Google Scholar 

  29. Dutta A, Zisserman A (2019) The VIA annotation software for images, audio and video proceedings of the 27th ACM International Conference on Multimedia, 2276 2279

  30. Varma M, Lu M, Gardner R et al (2019) Automated abnormality detection in lower extremity radiographs using deep learning. Nat Mach Intell 1:578–583

    Article  Google Scholar 

  31. Delong ER, Delong DM, Clarkepearson DI (1988) Comparing the areas under 2 or more correlated receiver operating characteristic curves - a nonparametric approach. Biometrics 44:837–845

    Article  CAS  PubMed  Google Scholar 

  32. Bland JM, Altman DG (1996) Measurement error and correlation coefficients. BMJ 313:41–42

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  33. Flores DV, Mejia Gomez C, Fernandez Hernando M, Davis MA, Pathria MN (2019) Adult acquired flatfoot deformity: anatomy, biomechanics, staging, and imaging findings. Radiographics 39:1437–1460

    Article  PubMed  Google Scholar 

  34. DiGiovanni JE, Smith SD (1976) Normal biomechanics of the adult rearfoot: a radiographic analysis. J Am Podiatry Assoc 66:812–824

    Article  CAS  PubMed  Google Scholar 

  35. Koo TK, Li MY (2016) A guideline of selecting and reporting intraclass correlation coefficients for reliability research. J Chiropr Med 15:155–163

    Article  PubMed  PubMed Central  Google Scholar 

  36. Muller R, Buttner P (1994) A critical discussion of intraclass correlation coefficients. Stat Med 13:2465–2476

    Article  CAS  PubMed  Google Scholar 

  37. Liljequist D, Elfving B, Roaldsen KS (2019) Intraclass correlation – A discussion and demonstration of basic features. PLoS One 14(7):e0219854. https://doi.org/10.1371/journal.pone.0219854

  38. Regulation Army (2004) Standards of medical fitness. Headquarters, Department of the Army Washington, DC

  39. Ryu SM, Lee TK, Lee SH (2022) Prevalence of flatfoot among young Korean males and the correlation among flatfoot angles measured in weight-bearing lateral radiographs. Medicine (Baltimore) 101(30):e29720. https://doi.org/10.1097/MD.0000000000029720

  40. Yang C-H, Chou K-T, Chung M-B, Chuang KS, Huang T-C (2015) Automatic detection of calcaneal-fifth metatarsal angle using radiograph: a computer-aided diagnosis of flat foot for military new recruits in Taiwan. PLoS One 10(6):e0131387. https://doi.org/10.1371/journal.pone.0131387

  41. Winklhofer S, Berger N, Ruder T et al (2014) Cardiothoracic ratio in postmortem computed tomography: reliability and threshold for the diagnosis of cardiomegaly. Forensic Sci Med Pathol 10:44–49

    Article  PubMed  Google Scholar 

  42. Fernandezfeliberti R, Flynn J, Ramirez N, Trautmann M, Alegria M (1995) Effectiveness of Tlso bracing in the conservative treatment of idiopathic scoliosis. J Pediatr Orthop 15:176–181

  43. Robinson AHN, Limbers JP (2005) Modern concepts in the treatment of hallux valgus. J Bone Joint Surg Br 87b:1038–1045

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Acknowledgements

We thank Dr. Joon Seo Lim from the Scientific Publications Team at Asan Medical Center for his assistance with English language editing and preparing this manuscript.

We also thank Inhwan Kim for his contribution to the python code of deep learning model development.

Funding

This research was supported by Basic Science Research Program through the National Research Foundation of Korea (NRF) funded by the Ministry of Education. (2021R1A6A3A01088445).

Author information

Authors and Affiliations

  1. Department of Biomedical Engineering, Asan Medical Institute of Convergence Science and Technology, Asan Medical Center, University of Ulsan College of Medicine, 26, Olympic-ro 43-gil, Songpa-gu, Seoul, 05506, Republic of Korea

    Seung Min Ryu, Keewon Shin & Hyunjung Kim

  2. Department of Orthopedic Surgery, Asan Medical Center, University of Ulsan College of Medicine, 88, Olympic-ro 43-gil, Songpa-gu, Seoul, 05505, Republic of Korea

    Seung Min Ryu, Chang Hyun Doh & Young Rak Choi

  3. Department of Radiology, Research Institute of Radiology, Asan Medical Center, University of Ulsan College of Medicine, 88, Olympic-ro 43-gil, Songpa-gu, Seoul, 05506, Republic of Korea

    Soo Wung Shin & Namkug Kim

  4. Department of Computer Science and Engineering, Seoul National University, 1, Gwanak-ro, Gwanak-gu, Seoul, 08826, Republic of Korea

    Soo Wung Shin

  5. Department of Orthopedic Surgery, Chonnam National University Hospital, 42, Jebong-ro, Dong-gu, Gwangju, 61469, Republic of Korea

    Sun Ho Lee

  6. Department of Anesthesiology and Pain Medicine, Seoul Medical Center, 156, Sinnae-ro, Jungnang-gu, Seoul, 02053, Republic of Korea

    Su Min Seo, Seung-Uk Cheon & Seung-Ah Ryu

  7. Department of Clinical Epidemiology and Biostatistics, Asan Medical Center, University of Ulsan College of Medicine, 88, Olympic-ro 43-gil, Songpa-gu, Seoul, 05505, Republic of Korea

    Min-Ju Kim

  8. Department of Convergence Medicine, Asan Medical Institute of Convergence Science and Technology, Asan Medical Center, University of Ulsan College of Medicine, 26, Olympic-ro 43-gil, Songpa-gu, Seoul, 05506, Republic of Korea

    Namkug Kim

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  1. Seung Min Ryu
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Corresponding author

Correspondence to Namkug Kim.

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Guarantor

The scientific guarantor of this publication is Seung Min Ryu from the University of Ulsan College of Medicine and Namkug Kim from Asan Medical Center.

Conflict of interest

The authors of this manuscript declare no relationships with any companies whose products or services may be related to the subject matter of the article.

Statistics and biometry

The scientific guarantor of this publication is Min-Ju Kim from the University of Ulsan College of Medicine. No complex statistical methods were necessary for this paper.

Informed consent

This retrospective study was conducted according to the principles of the Declaration of Helsinki and in accordance with the current scientific guidelines. The research protocol was approved by the Institutional Review Board of our institution, which waived the requirement for informed consent considering the retrospective nature of the study and de-identification characteristics of the dataset, in accordance with the Health Insurance Portability and Accountability Act privacy rule. (S2021-0922-0001).

Ethical approval

This retrospective study was conducted according to the principles of the Declaration of Helsinki and in accordance with the current scientific guidelines. The research protocol was approved by the Institutional Review Board of our institution, which waived the requirement for informed consent considering the retrospective nature of the study and de-identification characteristics of the dataset, in accordance with the Health Insurance Portability and Accountability Act privacy rule. (S2021-0922–0001).

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  • multicenter study

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Ryu, S.M., Shin, K., Shin, S.W. et al. Automated diagnosis of flatfoot using cascaded convolutional neural network for angle measurements in weight-bearing lateral radiographs. Eur Radiol 33, 4822–4832 (2023). https://doi.org/10.1007/s00330-023-09442-1

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