Endocrinol Metab.  2019 Jun;34(2):124-131. 10.3803/EnM.2019.34.2.124.

Digital Medicine in Thyroidology: A New Era of Managing Thyroid Disease

• Moon JH
• Steinhubl

• KMID: 2450511
• DOI: http://doi.org/10.3803/EnM.2019.34.2.124

Abstract

Digital medicine has the capacity to affect all aspects of medicine, including disease prediction, prevention, diagnosis, treatment, and post-treatment management. In the field of thyroidology, researchers are also investigating potential applications of digital technology for the thyroid disease. Recent studies using artificial intelligence (AI)/machine learning (ML) have reported reasonable performance for the classification of thyroid nodules based on ultrasonographic (US) images. AI/ML-based methods have also shown good diagnostic accuracy for distinguishing between benign and malignant thyroid lesions based on cytopathologic findings. Assistance from AI/ML methods could overcome the limitations of conventional thyroid US and fine-needle aspiration cytology. A web-based database has been developed for thyroid cancer care. In addition to its role as a nationwide registry of thyroid cancer, it is expected to serve as a clinical platform to facilitate better thyroid cancer care and as a research platform providing comprehensive disease-specific big data. Evidence has been found that biosignal monitoring with wearable devices may predict thyroid dysfunction. This real-world thyroid function monitoring could aid in the management and early detection of thyroid dysfunction. In the thyroidology field, research involving the range of digital medicine technologies and their clinical applications is expected to be even more active in the future.

Keyword

Thyroid; Thyroid neoplasms; Hyperthyroidism; Hypothyroidism; Artificial intelligence; Machine learning; Database; Wearable electronic devices

MeSH Terms

Artificial Intelligence
Biopsy, Fine-Needle
Classification
Diagnosis
Hyperthyroidism
Hypothyroidism
Learning
Machine Learning
Thyroid Diseases*
Thyroid Gland*
Thyroid Neoplasms
Thyroid Nodule

Figure

• 

  Fig. 1 Representative screen shots of a web-based application for predicting the risk of thyrotoxicosis using wearable devices (https://thyroscope.org). There are four distinct modules: (A) about THYROSCOPE, (B) Input My TFT, (C) My TFT/HR Data, and (D) Calculating My Risk. Reprinted from THYROSCOPE, with permission from THYROSCOPE [39]. TFT, thyroid function test; HR, heart rate.

Reference

1. Global Market Insights. Digital Health Market Size By Technology [Tele-healthcare {Telecare (Activity Monitoring, Remote Medication Management), Telehealth (LTC Monitoring, Video Consultation)}, mHealth {Wearables (BP Monitors, Glucose Meter, Pulse Oximeter, Sleep Apnea Monitors, Neurological Monitors), Apps (Medical, Fitness)}, Health Analytics, Digital Health System (EHR, e-prescribing System)], Industry Analysis Report, Regional Outlook (U.S., Canada, Germany, UK, France, Spain, Italy, Russia, Poland, China, India, Japan, Australia, Brazil, Mexico, South Africa), Application Potential, Price Trends, Competitive Market Share & Forecast, 2019-2025 [Internet]. Digital Health Market Report 2019. Selbyville: Global Market Insights, Inc.;2019. cited 2019 May 26. Available from: https://www.gminsights.com/industry-analysis/digital-health-market.

2. Poole DL, Mackworth AK, Goebel R. Computational intelligence: a logical approach. New York: Oxford University Press;1998. p. 558.

3. Bishop CM. Pattern recognition and machine learning. New York: Springer;2006. p. 738.

4. Haugen BR, Alexander EK, Bible KC, Doherty GM, Mandel SJ, Nikiforov YE, et al. 2015 American Thyroid Association Management guidelines for adult patients with thyroid nodules and differentiated thyroid cancer: the American Thyroid Association guidelines task force on thyroid nodules and differentiated thyroid cancer. Thyroid. 2016; 26:1–133.

5. Smith-Bindman R, Lebda P, Feldstein VA, Sellami D, Goldstein RB, Brasic N, et al. Risk of thyroid cancer based on thyroid ultrasound imaging characteristics: results of a population-based study. JAMA Intern Med. 2013; 173:1788–1796.

6. Brito JP, Gionfriddo MR, Al Nofal A, Boehmer KR, Leppin AL, Reading C, et al. The accuracy of thyroid nodule ultrasound to predict thyroid cancer: systematic review and meta-analysis. J Clin Endocrinol Metab. 2014; 99:1253–1263.

7. Shin JH, Baek JH, Chung J, Ha EJ, Kim JH, Lee YH, et al. Ultrasonography diagnosis and imaging-based management of thyroid nodules: revised Korean Society of Thyroid Radiology consensus statement and recommendations. Korean J Radiol. 2016; 17:370–395.

8. Park CS, Kim SH, Jung SL, Kang BJ, Kim JY, Choi JJ, et al. Observer variability in the sonographic evaluation of thyroid nodules. J Clin Ultrasound. 2010; 38:287–293.

9. Acharya UR, Swapna G, Sree SV, Molinari F, Gupta S, Bardales RH, et al. A review on ultrasound-based thyroid cancer tissue characterization and automated classification. Technol Cancer Res Treat. 2014; 13:289–301.

10. Song W, Li S, Liu J, Qin H, Zhang B, Zhang S, et al. Multitask cascade convolution neural networks for automatic thyroid nodule detection and recognition. IEEE J Biomed Health Inform. 2019; 23:1215–1224.

11. Chi J, Walia E, Babyn P, Wang J, Groot G, Eramian M. Thyroid nodule classification in ultrasound images by fine-tuning deep convolutional neural network. J Digit Imaging. 2017; 30:477–486.

12. Acharya UR, Faust O, Sree SV, Molinari F, Garberoglio R, Suri JS. Cost-effective and non-invasive automated benign and malignant thyroid lesion classification in 3D contrast-enhanced ultrasound using combination of wavelets and textures: a class of ThyroScan™ algorithms. Technol Cancer Res Treat. 2011; 10:371–380.

13. Ding J, Cheng H, Ning C, Huang J, Zhang Y. Quantitative measurement for thyroid cancer characterization based on elastography. J Ultrasound Med. 2011; 30:1259–1266.

14. Acharya UR, Faust O, Sree SV, Molinari F, Suri JS. ThyroScreen system: high resolution ultrasound thyroid image characterization into benign and malignant classes using novel combination of texture and discrete wavelet transform. Comput Methods Programs Biomed. 2012; 107:233–241.

15. Acharya UR, Vinitha Sree S, Krishnan MM, Molinari F, Garberoglio R, Suri JS. Non-invasive automated 3D thyroid lesion classification in ultrasound: a class of ThyroScan™ systems. Ultrasonics. 2012; 52:508–520.

16. Chang Y, Paul AK, Kim N, Baek JH, Choi YJ, Ha EJ, et al. Computer-aided diagnosis for classifying benign versus malignant thyroid nodules based on ultrasound images: a comparison with radiologist-based assessments. Med Phys. 2016; 43:554.

17. Prochazka A, Gulati S, Holinka S, Smutek D. Classification of thyroid nodules in ultrasound images using direction-independent features extracted by two-threshold binary decomposition. Technol Cancer Res Treat. 2019; 18:1533033819830748.

18. Ma J, Wu F, Zhu J, Xu D, Kong D. A pre-trained convolutional neural network based method for thyroid nodule diagnosis. Ultrasonics. 2017; 73:221–230.

19. Ko SY, Lee JH, Yoon JH, Na H, Hong E, Han K, et al. Deep convolutional neural network for the diagnosis of thyroid nodules on ultrasound. Head Neck. 2019; 41:885–891.

20. Wang L, Yang S, Yang S, Zhao C, Tian G, Gao Y, et al. Automatic thyroid nodule recognition and diagnosis in ultrasound imaging with the YOLOv2 neural network. World J Surg Oncol. 2019; 17:12.

21. Karakitsos P, Cochand-Priollet B, Guillausseau PJ, Pouliakis A. Potential of the back propagation neural network in the morphologic examination of thyroid lesions. Anal Quant Cytol Histol. 1996; 18:494–500.

22. Karakitsos P, Cochand-Priollet B, Pouliakis A, Guillausseau PJ, Ioakim-Liossi A. Learning vector quantizer in the investigation of thyroid lesions. Anal Quant Cytol Histol. 1999; 21:201–208.

23. Ippolito AM, De Laurentiis M, La Rosa GL, Eleuteri A, Tagliaferri R, De Placido S, et al. Neural network analysis for evaluating cancer risk in thyroid nodules with an indeterminate diagnosis at aspiration cytology: identification of a low-risk subgroup. Thyroid. 2004; 14:1065–1071.

24. Shapiro NA, Poloz TL, Shkurupij VA, Tarkov MS, Poloz VV, Demin AV. Application of artificial neural network for classification of thyroid follicular tumors. Anal Quant Cytol Histol. 2007; 29:87–94.

25. Daskalakis A, Kostopoulos S, Spyridonos P, Glotsos D, Ravazoula P, Kardari M, et al. Design of a multi-classifier system for discriminating benign from malignant thyroid nodules using routinely H&E-stained cytological images. Comput Biol Med. 2008; 38:196–203.

26. Varlatzidou A, Pouliakis A, Stamataki M, Meristoudis C, Margari N, Peros G, et al. Cascaded learning vector quantizer neural networks for the discrimination of thyroid lesions. Anal Quant Cytol Histol. 2011; 33:323–334.

27. Gopinath B, Shanthi N. Development of an automated medical diagnosis system for classifying thyroid tumor cells using multiple classifier fusion. Technol Cancer Res Treat. 2015; 14:653–662.

28. Margari N, Mastorakis E, Pouliakis A, Gouloumi AR, Asimis E, Konstantoudakis S, et al. Classification and regression trees for the evaluation of thyroid cytomorphological characteristics: a study based on liquid based cytology specimens from thyroid fine needle aspirations. Diagn Cytopathol. 2018; 46:670–681.

29. Sanyal P, Mukherjee T, Barui S, Das A, Gangopadhyay P. Artificial intelligence in cytopathology: a neural network to identify papillary carcinoma on thyroid fine-needle aspiration cytology smears. J Pathol Inform. 2018; 9:43.

30. Liu C, Huang Y, Ozolek JA, Hanna MG, Singh R, Rohde GK. SetSVM: an approach to set classification in nuclei-based cancer detection. IEEE J Biomed Health Inform. 2019; 23:351–361.

31. Sim I. Two ways of knowing: big data and evidence-based medicine. Ann Intern Med. 2016; 164:562–563.

32. Mehra S, Tuttle RM, Bergman D, Bernet V, Brett E, Cobin R, et al. Improving the quality of thyroid cancer care: how does the Thyroid Cancer Care Collaborative cross the Institute of Medicine's Quality Chasm. Thyroid. 2014; 24:615–624.

33. Mehra S, Tuttle RM, Milas M, Orloff L, Bergman D, Bernet V, et al. Database and registry research in thyroid cancer: striving for a new and improved national thyroid cancer database. Thyroid. 2015; 25:157–168.

34. Toral V, Garcia A, Romero FJ, Morales DP, Castillo E, Parrilla L, et al. Wearable system for biosignal acquisition and monitoring based on reconfigurable technologies. Sensors (Basel). 2019; 19:E1590.

35. Lee JE, Lee DH, Oh TJ, Kim KM, Choi SH, Lim S, et al. Clinical feasibility of monitoring resting heart rate using a wearable activity tracker in patients with thyrotoxicosis: prospective longitudinal observational study. JMIR Mhealth Uhealth. 2018; 6:e159.

36. Tamura T, Maeda Y, Sekine M, Yoshida M. Wearable photoplethysmographic sensors: past and present. Electronics. 2014; 3:282–302.

37. Dooley EE, Golaszewski NM, Bartholomew JB. Estimating accuracy at exercise intensities: a comparative study of self-monitoring heart rate and physical activity wearable devices. JMIR Mhealth Uhealth. 2017; 5:e34.

38. Wang R, Blackburn G, Desai M, Phelan D, Gillinov L, Houghtaling P, et al. Accuracy of wrist-worn heart rate monitors. JAMA Cardiol. 2017; 2:104–106.

39. THYROSCOPE. Detect thyroid disease by your biosignals [Internet]. Scottsdale: THYROSCOPE;c2017. cited 2019 May 26. Available from: https://thyroscope.org.