Challenges in introduction of artificial intelligence in medical practice - a review of clinical trials concerning adaptation of artificial intelligence in medicine

Mielnik, P.; Fojcik, M.; Kulbacki, M.; Segen, J.

Artykuł - szczegóły

Tytuł artykułu

Challenges in introduction of artificial intelligence in medical practice - a review of clinical trials concerning adaptation of artificial intelligence in medicine

Autorzy

Mielnik P. , Fojcik M. , Kulbacki M. , Segen J.

Identyfikatory

Warianty tytułu

Problemy z wprowadzaniem technologii sztucznej Inteligencji do praktyki medycznej

Języki publikacji

Abstrakty

An interest in Artificial Intelligence [AI] as science is growing in the last years. It has become gradually more used in the medicine. Methodology of development and testing of AI algorithms is generally well established. Use of AI in medicine requires elaboration of standards of its validation in clinical settings. This paper is a review of literature concerning clinical trials on AI adaptation in medicine.

Zainteresowanie technologią sztucznej inteligencji nieustannie wzrasta. Również w medycynie znajduje ta technologia coraz częściej praktyczne zastosowanie. Mimo dynamicznego jej rozwoju brakuje nadal standardów dla klinicznej weryfikacji jej skuteczności w praktyce medycznej. Artykuł jest przeglądem publikacji dotyczących badań klinicznych nad zastosowaniem tej technologii.

Słowa kluczowe

artificial intelligence diagnostics standards clinical testing validation

sztuczna inteligencja diagnostyka standardy testy kliniczne weryfikacja

Wydawca

Wydawnictwo Politechniki Śląskiej

Czasopismo

Studia Informatica

Rocznik

2016

Tom

Vol. 37, nr 3B

Strony

21--32

Opis fizyczny

Bibliogr. 35 poz.

Twórcy

autor

Mielnik P.

pawel.franciszek.mielnik@helse-forde.no

Section for Rheumatology, Department for Neurology, Rheumatology and Physical Medicine, Helse Førde, Førde 6800, Norway

autor

Fojcik M.

marcin.fojcik@hisf.no

Faculty of Engineering and Science, Sogn og Fjordane University College, Svanehaugvegen 2, 6812 Førde, Norway

autor

Kulbacki M.

kulbacki@pjwstk.edu.pl

Polish-Japanese Academy of Information Technology, Koszykowa 86, 02-008 Warsaw, Poland

autor

Segen J.

segen@pjwstk.edu.pl

Polish-Japanese Academy of Information Technology, Koszykowa 86, 02-008 Warsaw, Poland

Bibliografia

1. Wojciechowski K., Smołka B., Cupek R., Ziębiński A., Nurzyńska K., Kulbacki M., Segen J., Fojcik M., Mielnik P., Hein S.: A Machine-Learning Approach to the Automated Assessment of Joint Synovitis Activity. Computational Collective Intelligence 8th International Conference, ICCCI 2016, Halkidiki, Greece, September 28-30, 2016; Proceedings, Part II, Proceedings, Part II in of the series Lecture Notes in Computer Science Nguyen N.-T., Manolopoulos Y., Iliadis L., Trawiński B. (Eds.).
2. FreeSurfer. [Online]. Available: http://surfer.nmr.mgh.harvard.edu/. [Accessed: 07-Dec-2015].
3. McCarthy C.S., Ramprashad A., Thompson C., et al.: A comparison of FreeSurfer-generated data with and without manual intervention. Front. Neurosci., vol. 9, Oct. 2015.
4. Butts A.: Freesurfer Vs. Manual Tracing: Detecting Future Cognitive Decline In Healthy Older Adults At-Risk For Alzheimer’s Disease. 2013.
5. McCrae C., O’Shea A., Boissoneault J., et al.: Fibromyalgia patients have reduced hippocampal volume compared with healthy controls. J. Pain Res., Jan. 2015, p. 47.
6. Phillips J.L., Batten L.A., Tremblay P., et al.: A Prospective, Longitudinal Study of the Effect of Remission on Cortical Thickness and Hippocampal Volume in Patients with Treatment-Resistant Depression. Int. J. Neuropsychopharmacol., vol. 18, no. 8, Jun. 2015, DOI: 10.1093/ijnp/pyv037.
7. Kim E.Y., Magnotta V.A., Liu D., Johnson H.J.: Stable Atlas-based Mapped Prior (STAMP) machine-learning segmentation for multicenter large-scale MRI data. Magn. Reson. Imaging, vol. 32, no. 7, Sep. 2014, p. 832÷844.
8. Bron E.E., Smits M., van der Flier W.M., et al.: Standardized evaluation of algorithms for computer-aided diagnosis of dementia based on structural MRI: The CADDementia challenge. NeuroImage, vol. 111, May 2015, p. 562÷579.
9. Sorensen L., Pai A., Anker C., et al.: Dementia diagnosis using MRI cortical thickness, shape, texture, and volumetry. Proc MICCAI Workshop Chall. Comput.-Aided Diagn. Dement. Based Struct. MRI Data, 2014, p. 111÷118.
10. Roth H.R., Lu L., Seff A., et al.: A new 2.5 D representation for lymph node detection using random sets of deep convolutional neural network observations, [in:] Medical Image Computing and Computer-Assisted Intervention–MICCAI 2014, Springer, 2014, p. 520÷527.
11. Mustapha A., Hussain A., Samad S.A., et al.: Design and development of a content-based medical image retrieval system for spine vertebrae irregularity. Biomed. Eng. Online, vol. 14, no. 1, 2015, p. 6.
12. Yang X., Wu N., Cheng G., et al.: Automated Segmentation of the Parotid Gland Based on Atlas Registration and Machine Learning: A Longitudinal MRI Study in Head-and-Neck Radiation Therapy. Int. J. Radiat. Oncol., vol. 90, no. 5, Dec. 2014, p. 1225÷1233.
13. Maier O., Schröder C., Forkert N.D., et al.: Classifiers for Ischemic Stroke Lesion Segmentation: A Comparison Study. PloS One, vol. 10, no. 12, 2015, DOI: 10.1371/journal.pone.0145118.
14. Ardakani A.A., Gharbali A., Mohammadi A.: Application of Texture Analysis Method for Classification of Benign and Malignant Thyroid Nodules in Ultrasound Images. Iran. J. Cancer Prev., vol. 8, no. 2, 2015, p. 116.
15. Bragman F.J., McClelland J.R., Modat M., et al.: Multi-scale Analysis of Imaging Features and Its Use in the Study of COPD Exacerbation Susceptible Phenotypes, [in:] Medical Image Computing and Computer-Assisted Intervention–MICCAI 2014, Springer, 2014, p. 417÷424.
16. García Molina J.F., Zheng L., Sertdemir M., et al.: Incremental Learning with SVM for Multimodal Classification of Prostatic Adenocarcinoma. PLoS ONE, vol. 9, no. 4, Apr. 2014, DOI: 10.1371/journal.pone.0093600.
17. Depeursinge A., Kurtz C., Beaulieu C., et al.: Predicting Visual Semantic Descriptive Terms From Radiological Image Data: Preliminary Results With Liver Lesions in CT. IEEE Trans. Med. Imaging, vol. 33, no. 8, Aug. 2014, p. 1669÷1676.
18. Fehr D., Veeraraghavan H., Wibmer A., et al.: Automatic classification of prostate cancer Gleason scores from multiparametric magnetic resonance images. Proc. Natl. Acad. Sci., vol. 112, no. 46, Nov. 2015, p. E6265÷E6273.
19. Ataer-Cansizoglu E., Bolon-Canedo V., Campbell J.P., et al.: Computer-Based Image Analysis for Plus Disease Diagnosis in Retinopathy of Prematurity: Performance of the “i-ROP” System and Image Features Associated With Expert Diagnosis. Transl. Vis. Sci. Technol., vol. 4, no. 6, 2015, p. 5.
20. Fergus P., Hignett D., Hussain A., et al.: Automatic Epileptic Seizure Detection Using Scalp EEG and Advanced Artificial Intelligence Techniques. BioMed Res. Int., vol. 2015, 2015, p. 1÷17.
21. Siuly S., Kabir E., Wang H., Zhang Y.: Exploring Sampling in the Detection of Multicategory EEG Signals. Comput. Math. Methods Med., vol. 2015, 2015.
22. Cruz M.R., Martins C., Dias J., Pinto J.S.: A Validation of an Intelligent Decision-Making Support System for the Nutrition Diagnosis of Bariatric Surgery Patients. JMIR Med. Inform., vol. 2, no. 2, Jul. 2014, p. e8.
23. Jiang R., You R., Pei X.-Q., et al.: Development of a ten-signature classifier using a support vector machine integrated approach to subdivide the M1 stage into M1a and M1b stages of nasopharyngeal carcinoma with synchronous metastases to better predict patients’ survival. Oncotarget, Nov. 2015.
24. Mehta D.D., Van Stan J.H., Zañartu M., et al.: Using Ambulatory Voice Monitoring to Investigate Common Voice Disorders: Research Update. Front. Bioeng. Biotechnol., vol. 3, Oct. 2015.
25. Pipberger H.V., Stallmann F.W.: Use of computers in ECG interpretation. Am. Heart J., vol. 64, Aug. 1962, p. 285÷286.
26. Ho T.-W., Huang C.-W., Lin C.-M., et al.: A telesurveillance system with automatic electrocardiogram interpretation based on support vector machine and rule-based processing. JMIR Med. Inform., vol. 3, no. 2, 2015, p. e21.
27. Turkki R., Linder N., Holopainen T., et al.: Assessment of tumour viability in human lung cancer xenografts with texture-based image analysis. J. Clin. Pathol., May 2015, p. jclinpath-2015-202888.
28. Forsiden - Max Manus AS. [Online]. Available: http://www.maxmanus.no/. [Accessed: 11-Jan-2016].
29. Mathewson F.A.: Electrocardiogram interpretation by computer. Can. Med. Assoc. J., vol. 108, no. 10, May 1973, p. 1207÷1208.
30. Park J., Kang K.: Intelligent Classification of Heartbeats for Automated Real-Time ECG Monitoring. Telemed. E-Health, vol. 20, no. 12, Dec. 2014, p. 1069÷1077.
31. Rautaharju P.M.: Eyewitness to history: Landmarks in the development of computerized electrocardiography. J. Electrocardiol., Nov. 2015.
32. ECG - Philips. [Online]. Available: http://www.healthcare.philips.com/main/products/patient_monitoring/products/ecg/. [Accessed: 28-Dec-2015].
33. Diagnostic ECG - Products. [Online]. Available: http://www3.gehealthcare.com/en/products/categories/diagnostic_ecg. [Accessed: 28-Dec-2015].
34. Medical devices - European Commission. [Online]. Available: http://ec.europa.eu/growth/single-market/european-standards/harmonised-standards/medical-devices/index_en.htm. [Accessed: 29-Dec-2015].
35. Dietterich T.G.: Machine-learning research. AI Mag., vol. 18, no. 4, 1997, p. 97.

Uwagi

Opracowanie ze środków MNiSW w ramach umowy 812/P-DUN/2016 na działalność upowszechniającą naukę (zadania 2017).

Typ dokumentu

Bibliografia

Identyfikator YADDA

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