Artificial Intelligence applications on Women's Health. Issues such as the prediction for bone loss, osteoporosis, stroke, myocardial infarction, ovarian cancer, endometrial cancer, cervical dysplasias and many more are discussed.

1. Yapay Zeka ve Uygulaması
Dr. TEVFİK YOLDEMİR BSc, MA, PhD
tevfik.yoldemir@marmara.edu.tr

2. Makine Öğrenmesi
denetimli öğrenme yöntemleri
• the support vector machine,
• decision tree,
• linear regression,
• logistic regression,
• naive Bayes, and
• random forest methods.
denetimli olmayan öğrenme yöntemleri
• K-means,
• mean shift,
• affinity propagation,
• hierarchical clustering, and
• Gaussian mixture modeling.
doi:10.5853/jos.2017.02054
doi:10.1136/svn-2017-000101

3. Maliyet Yarar Analizi
AJR:212, May 2019
doi:10.2214/AJR.18.20419

4. Makine Öğrenmesi Tabanlı Veriye Dayalı Tanı
Prognostics and health management of
electronics : fundamentals, machine learning,
and the internet of things / edited by Michael G.
Pecht Hoboken, NJ : John Wiley & Sons, 2018.

5. doi:10.5853/jos.2017.02054

6. doi:10.1136/svn-2017-000101

7. Destek vektör makinası
doi:10.5853/jos.2017.02054
doi:10.1136/svn-2017-000101
(A) Hyper-plane fails to distinguish the groups.
(B) Hyper-plane distinguishes the groups but with a small margin.
(C) Hyper-plane distinguishes the groups with the maximum margin.

8. Yapay Nöral ağ
doi:10.5853/jos.2017.02054

9. Yapay Nöral ağ
doi:10.1136/svn-2017-000101

10. Yapay Zeka ile ilgili yayınlar
J. Clin. Med. 2019, 8, 360 doi:10.3390/jcm8030360

11. AJR:213, July 2019
doi:0.2214/AJR.18.20464
Makine Öğrenmesi için veri mühendisliği

12. Memede kitle
A computer-aided diagnosis system using artificial
intelligence for the diagnosis and characterization
of breast masses on ultrasound. Medicine (2019) 98:3
doi:10.1097/MD.0000000000014146
S-Detect (Samsung Medison Co. Ltd., Seoul, South Korea) is a
recently developed CAD program that provides computer-based
analysis based on morphologic features, using a novel feature
extraction technique and support vector machine classifier that
provides final assessment data for breast masses in a dichotomized
form (possibly benign or possibly malignant) based on the
American College of Radiology Breast Imaging Reporting and
Data System (ACR BI-RADS) ultrasonographic descriptors.

15. Meme – Yapay zeka
Artificial intelligence in breast ultrasound. World J Radiol 2019 February 28; 11(2):
19-26 doi: 10.4329/wjr.v11.i2.19
Artificial intelligence in breast imaging Clinical Radiology, 2019
doi:10.1016/j.crad.2019.02.006
Breast Cancer Prognosis Using a Machine Learning Approach. Cancers 2019, 11, 328;
doi:10.3390/cancers11030328
Artificial intelligence methods for the diagnosis of breast cancer by image
processing: a review Breast Cancer - Targets and Therapy 2018:10 219–230
doi:10.2147/BCTT.S175311
Deep Learning Model to Assess Cancer Risk on the Basis of a Breast MR Image Alone.
AJR:213, July 2019 doi:10.2214/AJR.18.20813

16. İşyükünü azaltmak
Can we reduce the workload of mammographic screening
by automatic identification of normal exams with artificial
intelligence? A feasibility study. European Radiology
doi:10.1007/s00330-019-06186-9 9000 mammograms with cancer (one-third of which
are presented as lesions with calcifications) and
180,000 mammograms without abnormalities.

17. Osteoporozis riski
Artificial neural network optimizes self-examination of
osteoporosis risk in women. Journal of International Medical
Research 2019 doi: 10.1177/0300060519850648
eğitim doğrulama test etme
ANN (Yapay nöral ağ)

18. Osteoporozis risk tahmini
Osteoporosis Risk Prediction for Bone Mineral Density Assessment of
Postmenopausal Women Using Machine Learning Yonsei Med J 54(6):1321-1330,
2013 doi:10.3349/ymj.2013.54.6.1321

19. Tüm kalça
Femur boynu
Lomber vertebra
doi:10.3349/ymj.2013.54.6.1321

20. Osteoporozda risk belirleme
Artificial intelligence on the identification of risk groups for osteoporosis, a
general review. BioMed Eng OnLine (2018) 17:12
doi:10.1186/s12938-018-0436-1

21. Kırılganlığa bağlı kırıklar
Artificial Intelligence Applied to
Osteoporosis: A Performance
Comparison of Machine Learning
Algorithms in Predicting Fragility
Fractures From MRI Data
Journal of Magnetic Resonance
Imaging 2018.
doi: 10.1002/jmri.26280

22. doi: 10.1002/jmri.26280

23. Kalça Kırığı Öngörüsü
Prediction of Hip Fracture in
Post-menopausal Women using
Artificial Neural Network Approach
978-1-5090-2809-2/17/$31.00 ©2017 IEEE 1167 postmenopozal kadın
Model I included femoral neck BMD and lumbar spine BMD only;
Model II included non-BMD clinical factors (e.g., age, weight, height, history of fall, and previous hip fracture); and
Model III included both of BMD and non-BMD

24. Kemik mineral yoğunluğu- kemik kayıp hızı
Artificial neural networks to predict future bone mineral density
and bone loss rate in Japanese postmenopausal women
BMC Res Notes (2017) 10:590 doi: 10.1186/s13104-017-2910-4
Akaike’s information criterion (AIC), Schwartz’s Bayesian
information criterion (BIC), multiple correlation coefficients (R2 )

25. Osteoporotik kalça kırıklarını öngörme
Prediction of osteoporotic hip fracture in postmenopausal women through
patient-specific FE analyses and machine learning Computer Methods and
Programs in Biomedicine 193 (2020) doi:10.1016/j.cmpb.2020.105484
Age, height weight, BMD

26. doi:10.1016/j.cmpb.2020.105484
19 Attributes
Clinical Attributes

27. Osteoporozis risk öngörüsü
Application of machine learning approaches for
osteoporosis risk prediction in postmenopausal women.
Archives of Osteoporosis (2020) 15: 169
doi:10.1007/s11657-020-00802-8 1792 postmenopozal kadın

28. Omurga kırığını öngörme
Bone strain index as a predictor of further
vertebral fracture in osteoporotic women:
An artificial intelligence-based analysis
PLoS ONE 16(2): e0245967.
doi:10.1371/journal.pone.0245967
Trabekuler kemik skoru Kemik gerginlik indeksi

29. Kırık öngörüsü -diabet ve osteoporozis
Hybrid deep learning model for risk prediction of
fracture in patients with diabetes and
osteoporosis. Front. Med. 2022, 16(3): 496–506
doi:10.1007/s11684-021-0828-7
Logistic Regression (LR)
Support Vector Machine (SVM)
Decision Tree (DT)
K-Nearest Neighbor (KNN)
Random Forest (RF)
Extremely Randomized Trees (ERT)
Gradient Boosting Decision Tree (GBDT)
AdaBoost
CatBoost
Extreme Gradient Boosting (XGBoost)
Deep Neural Network (DNN)
147 işlenmemiş veri
18 kırılma riskini etkileyen faktör
14 419 diabetli, bunların 8002
diabetes ve osteoporozis.
training set : test set -> 4:1

30. 18 kırılma riskini etkileyen faktör
doi:10.1007/s11684-021-0828-7

31. Sarkopeni risk öngörüsü
Sarcopenia feature selection and risk
prediction using machine learning.
Medicine 2019;98:43(e17699
doi:10.1097/MD.0000000000017699
4020 katılımcı (erkek: 1698; kadın: 2322)

32. Vazomotor semptomlar
Machine Learning Approaches to Identify
Factors Associated with Women's Vasomotor
Symptoms Using General Hospital Data.
J Korean Med Sci. 2021 May 3;36(17):e122
doi:10.3346/jkms.2021.36.e122
3,298 kadın
104 bağımsız değişken
75:25 test: doğrulama

33. Vaginal kuruluk
Artificial intelligence approaches to the
determinants of women’s vaginal dryness using
general hospital data. Journal of Obstetrics and
Gynaecology, 42:5, 1518-1523,
doi: 10.1080/01443615.2021.2013785
3298 kadın
eğitim: onaylama -> 75:25
ortalama kare hatası

34. Rastgele Orman değişkenlerindeki
İlk 20 değişken
doi: 10.1080/01443615.2021.2013785

35. Kardiovaskuler ölüm
Beatquency domain and machine learning improve prediction
of cardiovascular death after acute coronary syndrome
Scientific RepoRts | 6:34540 | DOI: 10.1038/srep34540
N=2302
Makina öğrenmesi eğitim:test -> 2:1
heart rate variability (HRV) Low Frequency High Frequency (LF/HF)
machine learning (Weighted HRV, WHRV)

36. 2255: 765
DOI: 10.1038/srep34540
principle component analysis (PCA)
non-centered Minimum Curvilinear Embedding (MCE)

37. Gögüs ağrı olanlarda MI tahmini
An artificial intelligence approach to early predict non-ST-
elevation myocardial infarction Patients with Chest Pain
Computer Methods and Programs in Biomedicine (2019),
doi: 10.1016/j.cmpb.2019.01.013
ANN (Yapay nöral ağ) 60:20:20
eğitim:
doğrulama:
test etme:

38. Kalp yetmezliği öngörüsü
Predicting incident heart failure in
women with machine learning: The
Women’s Health Initiative Cohort.
Canadian Journal of Cardiology
doi:10.1016/j.cjca.2021.08.006
Least Absolute Shrinkage and Selection
Operator (LASSO) -10 belirteç
Classification and Regression Trees
(CART) -11 belirteç
ARIC: Atherosclerosis Risk in Communities
43,709 kadın
1,227 değişken

39. Kardiovasküler hastalık taraması
Cardiovascular Disease
Screening in Women:
Leveraging Artificial
Intelligence and Digital Tools
Circulation Research.
2022;130:673–690.
doi: 10.1161/CIRCRESAHA.121.319876

40. Koroner arter hastalığı
Coronary Artery Disease Detection Using Artificial Intelligence Techniques: A
Survey of Trends, Geographical Differences and Diagnostic Features 1991-2020
Computers in Biology and Medicine, doi:10.1016/j.compbiomed.2020.104095.

41. doi:10.1016/j.compbiomed.2020.104095

42. Koronerarterhastalığı
EKG–MakineÖğretisi
doi:10.1016/j.compbiomed.2020.104095

43. Koronerarterhastalığı
HRV-MakineÖğretisi
doi:10.1016/j.compbiomed.2020.104095

44. Koronerarterhastalığı
EKG–DerinÖğreti
doi:10.1016/j.compbiomed.2020.104095

45. İskemik inme
Artificial intelligence to diagnose ischemic stroke and identify large vessel
occlusions: a systematic review. J NeuroIntervent Surg 2020;12:156–164.
doi:10.1136/neurintsurg-2019-015135

46. doi:10.1136/neurintsurg-2019-015135

47. doi:10.1136/neurintsurg-2019-015135

48. Inme
Artificial Intelligence Applications in Stroke
Stroke. 2020;51:2573–2579. DOI: 10.1161/STROKEAHA.119.027479
deep convolutional
neural networks (CNNs)
deep learning (DL)
machine learning (ML)

49. Servikal displazi tekrarlaması
Artificial intelligence estimates the impact of
human papillomavirus types in influencing the
risk of cervical dysplasia recurrence: progress
toward a more personalized approach.
European Journal of Cancer Prevention 2018
doi: 10.1097/CEJ.0000000000000432
Eğitim: test -> 80:20
5104 kadın

50. Kolposkopi
Application of deep learning to the
classification of images from colposcopy
ONCOLOGY LETTERS 15: 3518-3523, 2018
DOI: 10.3892/ol.2018.7762

51. Kolposkopi
Development and validation of an
artificial intelligence system for
grading colposcopic impressions
and guiding biopsies
BMC Medicine (2020) 18:406.
doi:10.1186/s12916-020-01860-y
Colposcopic Artificial
Intelligence Auxiliary
Diagnostic System
[CAIADS]

52. Endometrium kanseri
The utility of artificial neural networks and
classification and regression trees for the prediction
of endometrial cancer in postmenopausal women.
Public health 164 ( 2018 ) 1-6
doi:10.1016/j.puhe.2018.07.012
The CART system -IBM SPSS Statistics,
21, for Windows (SPSS Inc., Chicago, USA).
The ANN model -the Matlab for Windows
(the MathWorks Inc.)

53. Endometrium kanseri
Using deep learning with convolutional neural network approach to identify the invasion depth of
endometrial cancer in myometrium using MR images: a pilot study. Int J Environ Res Public Health
2020;17:5993
Deep learning for the determination of myometrial invasion depth and automatic lesion identification in
endometrial cancer MR imaging: a preliminary study in a single institution. Eur Radiol 2020;30:4985–94
Multiplanar MRI-based predictive model for preoperative assessment of lymph node metastasis in
endometrial cancer. Front Oncol 2019;9:1007.
Image analysis and multi-layer perceptron artificial neural networks for the discrimination between
benign and malignant endometrial lesions. Diagn Cytopathol 2017;45:202–11.
Automated system for diagnosing endometrial cancer by adopting deep-learning technology in
hysteroscopy. Plos One 2021;16:e0248526.

54. Over kanseri tanımlama
Analytical Validation of a Deep Neural Network
Algorithm for the Detection of Ovarian Cancer
JCO Clin Cancer Inform 6:e2100192
doi:10.1200/CCI.21.00192

55. Over kanseri
Evaluation of a convolutional neural network for ovarian tumor differentiation based on
magnetic resonance imaging. Eur Radiol 2020;1–12
Evaluation of machine learning methods with Fourier Transform features for classifying
ovarian tumors based on ultrasound images. PLoS One 2019;14:e0219388.
Improved deep learning network based in combination with cost-sensitive learning for early
detection of ovarian cancer in color ultrasound detecting system. J Med Syst 2019;43:251
Deep learning provides a new computed tomography-based prognostic biomarker for
recurrence prediction in high-grade serous ovarian cancer. Radiother Oncol 2019;132:171–7
Application of artificial intelligence for preoperative diagnostic and prognostic prediction in
epithelial ovarian cancer based on blood biomarkers. Clin Cancer Res 2019; 25:3006–15

56. Kronik hastalık riskini öngörmede Makine
Öğrenmesi kullanma süreçleri
doi:10.1097/MD.0000000000017699

57. İlginiz için teşekkür ederim
Telemedicine and women's health. Climacteric. 2022 Oct;25(5):425-426.
doi:10.1080/13697137.2022.2106725.
Internet Of Things and women's health. Climacteric. 2020 Oct;23(5):423-425.
doi:10.1080/13697137.2020.1811563.
Artificial intelligence and women's health. Climacteric. 2020 Feb;23(1):1-2.
doi:10.1080/13697137.2019.1682804.
profdr.tevfikyoldemir