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

Meltem M Yashar; Ilayda Begum Izci; Fatma Zeynep Gungoren; Abdulkadir A Eren; Ali A Mert; Irmak I Durur-Subasi

doi:10.1186/s13244-024-01661-4

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

Insights Imaging. 2024 Mar 27;15(1):99. doi: 10.1186/s13244-024-01661-4.

Authors

Meltem M Yashar¹, Ilayda Begum Izci², Fatma Zeynep Gungoren³, Abdulkadir A Eren⁴, Ali A Mert⁵, Irmak I Durur-Subasi⁶

Affiliations

¹ Faculty of Medicine, Istanbul Medipol University, Istanbul, Turkey. yasarmel@hotmail.com.
² Deloitte, Istanbul, Turkey.
³ Department of Radiology, Florence Nightingale Hospital, Istanbul, Turkey.
⁴ Faculty of Medicine, Department of Radiology, Istanbul Medipol University, Istanbul, Turkey.
⁵ Faculty of Medicine, Department of Internal Medicine, Istanbul Medipol University, Istanbul, Turkey.
⁶ International Faculty of Medicine, Department of Radiology, Istanbul Medipol University, Istanbul, Turkey.

Abstract

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.

Keywords: Artificial intelligence; Diabetes mellitus; Digital breast tomosynthesis; Glycosylated hemoglobin A1c; Pectoral muscle.