Can artificial intelligence detect type 2 diabetes in women by evaluating the pectoral muscle on tomosynthesis: diagnostic study

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Yashar M. M., Izci I. B., Gungoren F. Z., EREN A., MERT A., DURUR SUBAŞI I.

Insights into Imaging, vol.15, no.1, 2024 (SCI-Expanded) identifier identifier

  • Publication Type: Article / Article
  • Volume: 15 Issue: 1
  • Publication Date: 2024
  • Doi Number: 10.1186/s13244-024-01661-4
  • Journal Name: Insights into Imaging
  • Journal Indexes: Science Citation Index Expanded (SCI-EXPANDED), Scopus, Academic Search Premier, EMBASE, Directory of Open Access Journals
  • Keywords: Artificial intelligence, Diabetes mellitus, Digital breast tomosynthesis, Glycosylated hemoglobin A1c, Pectoral muscle
  • Istanbul Medipol University Affiliated: Yes


Objectives: This retrospective single-center analysis aimed to evaluate whether artificial intelligence can detect type 2 diabetes mellitus by evaluating the pectoral muscle on digital breast tomosynthesis (DBT). Material method: An analysis of 11,594 DBT images of 287 consecutive female patients (mean age 60, range 40–77 years) was conducted using convolutional neural networks (EfficientNetB5). The inclusion criterion was left-sided screening images with unsuspicious interpretation who also had a current glycosylated hemoglobin A1c (HBA1c) % value. The exclusion criteria were inadequate imaging, history of breast cancer, and/or diabetes mellitus. HbA1c values between 5.6 and 6.4% were categorized as prediabetic, and those with values ≥ 6.5% were categorized as diabetic. A recorded HbA1c ≤ 5.5% served as the control group. Each group was divided into 3 subgroups according to age. Images were subjected to pattern analysis parameters then cropped and resized in a format to contain only pectoral muscle. The dataset was split into 85% for training and 15% for testing the model’s performance. The accuracy rate and F1-score were selected as performance indicators. Results: The training process was concluded in the 15th epoch, each comprising 1000 steps, with an accuracy rate of 92% and a loss of only 0.22. The average specificity and sensitivity for all 3 groups were 95%. The F1-score was 0.95. AUC-ROC was 0.995. PPV was 94%, and NPV was 98%. Conclusion: Our study presented a pioneering approach, applying deep learning for the detection of diabetes mellitus status in women using pectoral muscle images and was found to function with an accuracy rate of 92%. Critical relevance statement: AI can differentiate pathological changes within pectoral muscle tissue by assessing radiological images and maybe a potential diagnostic tool for detecting diabetes mellitus and other diseases that affect muscle tissues. Key points: • AI may have an opportunistic use as a screening exam for diabetes during digital breast tomosynthesis. • This technique allows for early and non-invasive detection of diabetes mellitus by AI. • AI may have broad applications in detecting pathological changes within muscle tissue. Graphical Abstract: (Figure presented.)