Articles | Volume 11, issue 5
https://doi.org/10.5194/jbji-11-623-2026
© Author(s) 2026. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/jbji-11-623-2026
© Author(s) 2026. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Machine learning in diabetic-foot osteomyelitis: current applications, challenges, and future directions
Farzad Pourghazi
Division of Public Health, Infectious Diseases and Occupational Medicine, Department of Medicine, Mayo Clinic College of Medicine and Science, Mayo Clinic, Rochester, MN, USA
Seyed Mohammad Amin Alavi
Faculty of Medicine, Ahvaz Jundishapur University of Medical Sciences, 6135715753 Ahvaz, Iran
Francesco Petri
Division of Public Health, Infectious Diseases and Occupational Medicine, Department of Medicine, Mayo Clinic College of Medicine and Science, Mayo Clinic, Rochester, MN, USA
Department of Infectious Diseases, Luigi Sacco University Hospital, Regional Center for Infectious Diseases (CEREMI), 20157 Milan, Italy
Takahiro Matsuo
Department of Infectious Diseases, Infection Control, and Employee Health, The University of Texas MD Anderson Cancer Center, Houston, TX, USA
Sergio Alvarez Mulett
Division of Public Health, Infectious Diseases and Occupational Medicine, Department of Medicine, Mayo Clinic College of Medicine and Science, Mayo Clinic, Rochester, MN, USA
Fabio Borgonovo
Division of Public Health, Infectious Diseases and Occupational Medicine, Department of Medicine, Mayo Clinic College of Medicine and Science, Mayo Clinic, Rochester, MN, USA
Department of Infectious Diseases, Luigi Sacco University Hospital, Regional Center for Infectious Diseases (CEREMI), 20157 Milan, Italy
Andrea Gori
Department of Infectious Diseases, Luigi Sacco University Hospital, Regional Center for Infectious Diseases (CEREMI), 20157 Milan, Italy
Department of Biomedical and Clinical Sciences, University of Milan, 20157 Milan, Italy
Centre for Multidisciplinary Research in Health Science (MACH), University of Milan, 20157 Milan, Italy
Michael E. Torchia
Department of Orthopedic Surgery, Mayo Clinic, Rochester, MN, USA
Aaron J. Tande
Division of Public Health, Infectious Diseases and Occupational Medicine, Department of Medicine, Mayo Clinic College of Medicine and Science, Mayo Clinic, Rochester, MN, USA
Elie F. Berbari
CORRESPONDING AUTHOR
Division of Public Health, Infectious Diseases and Occupational Medicine, Department of Medicine, Mayo Clinic College of Medicine and Science, Mayo Clinic, Rochester, MN, USA
Latest update: 13 Oct 2026
Short summary
Diabetic-foot bone infection is difficult to diagnose, and machine learning may help clinicians use clinical information and medical images. We reviewed studies that developed or evaluated these models for diabetic-foot osteomyelitis. Most focused on diagnosis using either clinical and laboratory information or imaging. No study combined these sources, and testing in new groups was uncommon. Future research should combine clinical information with imaging and test models in other groups.
Diabetic-foot bone infection is difficult to diagnose, and machine learning may help clinicians...