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

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dc.authorid0000-0003-4325-3430
dc.authorid0000-0003-0958-6581
dc.authorid0000-0001-8945-2385
dc.authorid0000-0003-3122-4499
dc.contributor.authorYashar, Meltem
dc.contributor.authorİzci, İlayda Begüm
dc.contributor.authorGüngören, Fatma Zeynep
dc.contributor.authorEren, Abdulkadir
dc.contributor.authorMert, Ali
dc.contributor.authorDurur Subaşı, Irmak
dc.date.accessioned2024-04-30T13:48:11Z
dc.date.available2024-04-30T13:48:11Z
dc.date.issued2024
dc.departmentİstanbul Medipol Üniversitesi, Tıp Fakültesi, Dahili Tıp Bilimleri Bölümü, İç Hastalıkları Ana Bilim Dalı
dc.departmentİstanbul Medipol Üniversitesi, Tıp Fakültesi, Dahili Tıp Bilimleri Bölümü, Radyoloji Ana Bilim Dalı
dc.departmentİstanbul Medipol Üniversitesi, Uluslararası Tıp Fakültesi, Dahili Tıp Bilimleri Bölümü, Radyoloji Ana Bilim Dalı
dc.description.abstractObjectives 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 center dot AI may have an opportunistic use as a screening exam for diabetes during digital breast tomosynthesis. center dot This technique allows for early and non-invasive detection of diabetes mellitus by AI. center dot AI may have broad applications in detecting pathological changes within muscle tissue.Key points center dot AI may have an opportunistic use as a screening exam for diabetes during digital breast tomosynthesis. center dot This technique allows for early and non-invasive detection of diabetes mellitus by AI. center dot AI may have broad applications in detecting pathological changes within muscle tissue.Key points center dot AI may have an opportunistic use as a screening exam for diabetes during digital breast tomosynthesis. center dot This technique allows for early and non-invasive detection of diabetes mellitus by AI. center dot AI may have broad applications in detecting pathological changes within muscle tissue.
dc.identifier.citationYashar, M., İzci, İ. B., Güngören, F. Z., Eren, A., Mert, A. ve Durur Subaşı, I. (2024). Can artificial intelligence detect type 2 diabetes in women by evaluating the pectoral muscle on tomosynthesis: diagnostic study. Insights into Imaging. (15)1. http://dx.doi.org/10.1186/s13244-024-01661-4
dc.identifier.doi10.1186/s13244-024-01661-4
dc.identifier.issn1869-4101
dc.identifier.issue1
dc.identifier.pmid38536577
dc.identifier.scopus2-s2.0-85188890585
dc.identifier.scopusqualityQ1
dc.identifier.urihttp://dx.doi.org/10.1186/s13244-024-01661-4
dc.identifier.urihttps://hdl.handle.net/20.500.12511/12426
dc.identifier.volume15
dc.identifier.wos001195231500002en_US
dc.identifier.wosQ1en_US
dc.identifier.wosqualityQ1
dc.indekslendigikaynakWeb of Science
dc.indekslendigikaynakScopus
dc.indekslendigikaynakPubMed
dc.institutionauthorYashar, Meltem
dc.institutionauthorEren, Abdulkadir
dc.institutionauthorMert, Ali
dc.institutionauthorDurur Subaşı, Irmak
dc.language.isoen
dc.publisherSpringer Science and Business Media Deutschland GmbH
dc.relation.ispartofInsights into Imagingen_US
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanı
dc.relation.tubitakinfo:eu-repo/grantAgreement/TUBITAK/SOBAG/2209-A
dc.rightsAttribution 4.0 International*
dc.rightsinfo:eu-repo/semantics/openAccess
dc.rights.urihttp://creativecommons.org/licenses/by/4.0/.*
dc.subjectArtifcial İntelligence
dc.subjectDiabetes Mellitus
dc.subjectDigital Breast Tomosynthesis
dc.subjectGlycosylated Hemoglobin A1c
dc.subjectPectoral Muscle
dc.titleCan artificial intelligence detect type 2 diabetes in women by evaluating the pectoral muscle on tomosynthesis: diagnostic study
dc.typeArticle

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