The Value in Artificial Intelligence

  • Ramandeep Singh
  • Fatemeh Homayounieh
  • Rachel Vining
  • Subba R. Digumarthy
  • Mannudeep K. KalraEmail author
Part of the Medical Radiology book series (MEDRAD)


Past 5 years have seen burgeoning applications of machine learning (ML) in diverse radiological domains including thoracic radiology, neuroimaging, abdominal imaging, musculoskeletal imaging, and breast imaging. Deep learning technologies have been applied to improve image resolution at ultralow radiation dose. Publications abound on ML in chest CT have focused on detection and characterization of pulmonary nodules, as well as for rib and spine straightening and labeling, vessel segmentation, and estimation of CT fractional flow reserve. ML has also been applied for detecting lines, tubes, pneumothorax, pleural effusions, cardiomegaly, and pneumonia, on chest radiographs. Applications of ML in cerebral hemorrhage detection and prediction of stroke outcomes, appendicitis and renal colic prediction, hand bone age calculation or rib unfolding for fracture detection, and characterization of breast macro-calcifications and masses are also shown. We review fundamentals, applications, and limitations of machine learning in thoracic radiology, neuroimaging, abdominal imaging, musculoskeletal imaging, and breast imaging.


  1. Ahn CK, Yang Z, Heo C, Jin H, Park B, Kim JH (2018) A deep learning-enabled iterative reconstruction of ultra-low-dose CT: use of synthetic sinogram-based noise simulation technique. Proceedings SPIE 10573, Medical Imaging 2018: Physics of Medical Imaging, 1057335Google Scholar
  2. Armato SG, Gieger ML, Moran CJ, Blackburn JT, Doi K, Macmahan H (1999) Computerized detection of pulmonary nodules on CT scans. Radiographics 19(5):1303–1311PubMedGoogle Scholar
  3. Armato SG, Altman MB, Wilkie J, Sone S, Li F, Roy AS (2003) Automated lung nodule classification following automated nodule detection on CT: a serial approach. Med Phys 30(6):1188–1197PubMedGoogle Scholar
  4. Artificial Intelligence (2017) AI can spot large pneumothoraces on chest x-ray.
  5. Bier G, Schabel C, Othman A, Bongers MN, Schmehl J, Ditt H, Nikolaou K, Bamberg F, Notohamiprodjo M (2015) Enhanced reading time efficiency by use of automatically unfolded CT rib reformations in acute trauma. Eur J Radiol 84(11):2173–2180PubMedGoogle Scholar
  6. Bier G, Mustafa DF, Kloth C, Weisel K, Ditt H, Nikolaou K, Horger M (2016) Improved follow-up and response monitoring of thoracic cage involvement in multiple myeloma using a novel CT postprocessing software: the lessons we learned. Am J Roentgenol 206(1):57–63Google Scholar
  7. Bryan RN (2016) Machine learning applied to Alzheimer disease. Radiology 281(3):665–668PubMedGoogle Scholar
  8. Cai H, Peng Y, Ou C, Chen M, Li L (2014) Diagnosis of breast masses from dynamic contrast-enhanced and diffusion-weighted MR: a machine learning approach. PLoS One 9(1):e87387PubMedPubMedCentralGoogle Scholar
  9. Cantor-Rivera D, Khan AR, Goubran M, Mirsattari SM, Peters TM (2015) Detection of temporal lobe epilepsy using support vector machines in multi-parametric quantitative MR imaging. Comput Med Imaging Graph 41:14–28PubMedGoogle Scholar
  10. Chartrand G, Cheng PM, Vorontsov E, Drozdzal M, Turcotte S, Pal CJ, Kadoury S, Tang A (2017) Deep learning: a primer for radiologists. Radiographics 37(7):2113–2131PubMedGoogle Scholar
  11. Collij LE, Heeman F, Kuijer JP, Ossenkoppele R, Benedictus MR, Möller C, Verfaillie SC, Sanz-Arigita EJ, van Berckel BN, van der Flier WM, Scheltens P, Barkhof F, Wink AM (2016) Application of machine learning to arterial spin labeling in mild cognitive impairment and Alzheimer disease. Radiology 281(3):865–875PubMedGoogle Scholar
  12. Dal Moro F, Abate A, Lanckriet GR, Arandjelovic G, Gasparella P, Bassi P, Mancini M, Pagano F (2006) A novel approach for accurate prediction of spontaneous passage of ureteral stones: support vector machines. Kidney Int 69(1):157–160PubMedGoogle Scholar
  13. Erickson BJ, Korfiatis P, Akkus Z, Kline TL (2017) Machine learning for medical imaging. Radiographics 37(2):505–515PubMedPubMedCentralGoogle Scholar
  14. Firmino M, Angelo G, Morais H, Dantas MR, Valentim R (2016) Computer-aided detection (CADe) and diagnosis (CADx) system for lung cancer with likelihood of malignancy. Biomed Eng Online 15(1):2–7PubMedPubMedCentralGoogle Scholar
  15. Giger ML, Bae KT, MacMahon H (1994) Computerized detection of pulmonary nodules in computed tomography images. Investig Radiol 29:459–465Google Scholar
  16. Gillies RJ, Kinahan PE, Hricak H et al (2016) Radiomics: images are more than pictures, they are data. Radiology 278:563–577Google Scholar
  17. Golkov V, Dosovitskiy A, Sperl JI, Menzel MI, Czisch M, Samann P, Brox T, Cremers D (2016) Q-space deep learning: twelve-fold shorter and model-free diffusion MRI scans. IEEE Trans Med Imaging 35(5):1344–1351PubMedGoogle Scholar
  18. Ha JY, Jeon KN, Bae K, Choi BH (2017) Effect of bone reading CT software on radiologist performance in detecting bone metastases from breast cancer. Br J Radiol 90:20160809PubMedPubMedCentralGoogle Scholar
  19. Hammernik K, Klatzer T, Kobler E, Recht MP, Sodickson DK, Pock T, Knoll F (2017) Learning a variational network for reconstruction of accelerated MRI data. arXiv preprint arXiv:1704.00447. Accessed 14 Nov 2017
  20. Hawkins S, Wang H, Liu Y, Garcia A, Stringfield O, Krewer H et al (2016) Predicting malignant nodules from screening CT scans. J Thorac Oncol 11:2120–2128PubMedPubMedCentralGoogle Scholar
  21. Hinton GE, Osindero S, Teh Y-W (2006) A fast learning algorithm for deep belief nets. Neural Comput 18(7):1527–1554PubMedGoogle Scholar
  22. Hoog AH, Meme HK, van Deutekom H et al (2011) High sensitivity of chest radiograph reading by clinical officers in a tuberculosis prevalence survey. Int J Tuberc Lung Dis 15(10):1308–1314PubMedGoogle Scholar
  23. Hua K-L, Hsu C-H, Hidayati SC, Cheng W-H, Chen Y-J (2015) Computer-aided classification of lung nodules on computed tomography images via deep learning technique. OncoTargets Ther 8:2015–2022Google Scholar
  24. Hwang S, Kim HE, Jeong J, Kim HJ (2016) A novel approach for tuberculosis screening based on deep convolutional neural networks. In: Tourassi GD, Armato SG (eds) Proceedings of SPIE: medical imaging 2016—title, vol 9785. International Society for Optics and Photonics, Bellingham, WA, p 97852WGoogle Scholar
  25. Itu L, Rapaka S, Passerini T, Georgescu B, Schwemmer C, Schoebinger M, Flohr T, Sharma P, Comaniciu D (2016) A machine-learning approach for computation of fractional flow reserve from coronary computed tomography. J Appl Physiol 121:42–52PubMedGoogle Scholar
  26. Jaeger S, Karargyris A, Candemir S et al (2014) Automatic tuberculosis screening using chest radiographs. IEEE Trans Med Imaging 33(2):233–245PubMedGoogle Scholar
  27. Jiang F, Jiang Y, Zhi H et al (2017) Artificial intelligence in healthcare: past, present and future. Stroke Vasc Neurol 0:e000101Google Scholar
  28. Kligerman S, Cai L, White CS (2013) The effect of computer-aided detection on radiologist performance in the detection of lung cancers previously missed on a chest radiograph. J Thorac Imaging 28(4):244–252PubMedGoogle Scholar
  29. Kumar K (2012) Artificial neural networks for diagnosis of kidney stones disease. Int J Comput Sci Information Technol 7:20–25Google Scholar
  30. Lakhani P, Sundaram B (2017) Deep learning at chest radiography: automated classification of pulmonary tuberculosis by using convolutional neural networks. Radiology (2):574–582PubMedPubMedCentralGoogle Scholar
  31. Lambin P, Rios-Velazquez E, Leijenaar R et al (2012) Radiomics: extracting more information from medical images using advanced feature analysis. Eur J Cancer 48:441–446PubMedPubMedCentralGoogle Scholar
  32. Lee SH, Lee SM, Goo JM et al (2014) Usefulness of texture analysis in differentiating transient from persistent part-solid nodules(PSNs): a retrospective study. PLoS One 9:e85167PubMedPubMedCentralGoogle Scholar
  33. Lee H, Mansouri M, Tajmir S et al (2017a) A deep-learning system for fully-automated peripherally inserted central catheter (PICC) tip detection. J Digit Imaging. PubMedCentralGoogle Scholar
  34. Lee J-G, Jun S, Cho Y-W et al (2017b) Deep learning in medical imaging: general overview. Korean J Radiol 18(4):570–584PubMedPubMedCentralGoogle Scholar
  35. Lodwick GS, Keats TE, Dorst JP (1963) The coding of roentgen images for computer analysis as applied to lung cancer. Radiology 81(2):185–200PubMedGoogle Scholar
  36. Maduskar P, Muyoyeta M, Ayles H, Hogeweg L, Peters-Bax L, van Ginneken B (2013) Detection of tuberculosis using digital chest radiography: automated reading vs. interpretation by clinical officers. Int J Tuberc Lung Dis 17(12):1613–1620PubMedGoogle Scholar
  37. Maldonado F, Boland JM, Raghunath S et al (2013) Noninvasive characterization of the histopathologic features of pulmonary nodules of the lung adenocarcinoma spectrum using computer-aided nodule assessment and risk yield (CANARY)—a pilot study. J Thorac Oncol 8:452–460PubMedPubMedCentralGoogle Scholar
  38. Manikandan T, Bharathi N (2016) Lung cancer detection using fuzzy auto-seed cluster means morphological segmentation and SVM classifier. J Med Syst 40(7):1Google Scholar
  39. Melendez J, Sánchez CI, Philipsen RH et al (2016) An automated tuberculosis screening strategy combining X-ray-based computer-aided detection and clinical information. Sci Rep 6:252–265Google Scholar
  40. Messay T, Hardie RC, Rogers SK (2010) A new computationally efficient CAD system for pulmonary nodule detection in CT imagery. Med Image Anal 14(3):390–406PubMedGoogle Scholar
  41. Pande T, Cohen C, Pai M, Ahmad Khan F (2016) Computer-aided detection of pulmonary tuberculosis on digital chest radiographs: a systematic review. Int J Tuberc Lung Dis 20(9):1226–1230PubMedGoogle Scholar
  42. Park SY, Seo JS, Lee SC, Kim SM (2013) Application of an artificial intelligence method for diagnosing acute appendicitis: the support vector machine. In: Park J, Stojmenovic I, Choi M, Xhafa F (eds) Future information technology. Lecture notes in electrical engineering, vol 276. Springer, Berlin, HeidelbergGoogle Scholar
  43. Rios Velazquez E, Parmar C, Liu Y, Coroller TP, Cruz G, Stringfield O et al (2017) Somatic mutations drive distinct imaging phenotypes in lung cancer. Cancer Res 77(14):3922–3930PubMedGoogle Scholar
  44. Riverain (2004) Riverain medical introduces artificial intelligence system for CHEST X-RAY early lung cancer detection. PR Newswire.
  45. Rothenberg SA, Patel JB, Herscu MH, et al (2016) Evaluation of a machine learning approach to protocol MRI examinations: initial experience predicting use of contrast by neuroradiologists in MRI protocols. Paper presented at Radiology Society of North America, 102nd Scientific Assembly and Annual Meeting, Chicago, ILGoogle Scholar
  46. Rui X, Cheng L, Long Y, Fu L, Alessio AM, Asma E, Kinahan PE, De Man B (2015) Ultra-low dose CT attenuation correction for PET/CT: analysis of sparse view data acquisition and reconstruction algorithms. Phys Med Biol 60(19):7437–7460PubMedPubMedCentralGoogle Scholar
  47. Sohn JH, Trivedi H, Mesterhazy J, Al-adel F, Vu T, Rybkin A, Ohliger M (2017) Development and validation of machine learning based natural language classifiers to automatically assign MRI abdomen/pelvis protocols from free-text clinical indications. Paper presented at Society of Imaging Informatics in Medicine, Annual Meeting, Pittsburgh, PAGoogle Scholar
  48. Suk H-I, Shen D (2013) Deep learning-based feature representation for AD/MCI classification. In: Mori K, Sakuma I, Sato Y, Barillot C, Navab N (eds) Medical image computing and computer-assisted intervention—MICCAI. Springer, BerlinGoogle Scholar
  49. Suzuki K, Armato SG III, Li F, Sone S, Doi K (2003) Massive training artificial neural network (mtann) for reduction of false positives in computerized detection of lung nodules in low-dose computed tomography. Med Phys 30(7):1602–1617PubMedGoogle Scholar
  50. Wang C, Elazab A, Wu J, Hu Q (2017a) Lung nodule classification using deep feature fusion in chest radiography. Comput Med Imaging Graph 57:10–18PubMedGoogle Scholar
  51. Wang J, Wu CJ, Bao ML, Zhang J, Wang XN, Zhang YD (2017b) Machine learning-based analysis of MR radiomics can help to improve the diagnostic performance of PI-RADS v2 in clinically relevant prostate cancer. Eur Radiol 27(10):4082–4090Google Scholar
  52. Wernick MN, Yang Y, Brankov JG, Yourganov G, Strother SC (2010) Machine learning in medical imaging. IEEE Signal Process Mag 27(4):25–38PubMedPubMedCentralGoogle Scholar
  53. Xu H, Tao X, Sundararajan R (2010) Proceedings of the third international workshop on pulmonary image analysis. CreateSpace Independent Publishing Platform, Beijing. Computer Aided Detection for Pneumoconiosis Screening on Digital Chest Radiographs;9:129–138Google Scholar
  54. Yan Z, Zhang S, Tan C, Qin H, Belaroussi B, Yu HJ, Miller C, Metaxas DN (2015) Atlas-based liver segmentation and hepatic fat-fraction assessment for clinical trials. Comput Med Imaging Graph 41:80–92PubMedGoogle Scholar
  55. Yu P, Xu H, Zhu Y, Yang C, Sun X, Zhao J (2011) An automatic computer-aided detection scheme for pneumoconiosis on digital chest radiographs. J Digit Imaging 24(3):382–393PubMedGoogle Scholar
  56. Zaharchuk G, Gong E, Wintermark M, Rubin D, Langlotz CP (2018) Deep learning in neuroradiology. Am J Neuroradiol 2:5543Google Scholar
  57. Zhu B, Luo W, Li B et al (2014) The development and evaluation of a computerized diagnosis scheme for pneumoconiosis on digital chest radiographs. Biomed Eng Online 13:141PubMedPubMedCentralGoogle Scholar

Further Reading

  1. Artificial Intelligence. intelligence. Accessed 16 Jan 2018
  2. ClearRead CT Vessel Suppress Clear read vessel suppress. Accessed 6 Feb 2018
  3. Deep Learning In Wikipedia, the free encyclopedia. Accessed 16 Jan 2018 (16:08)
  4. Imaging Analytics Accessed 8 May 2018
  5. Turing Test. In Wikipedia, the free encyclopedia. Accessed 16 Jan 2018 (16:15)

Copyright information

© Springer Nature Switzerland AG  2019

Authors and Affiliations

  • Ramandeep Singh
    • 1
    • 2
  • Fatemeh Homayounieh
    • 1
    • 2
  • Rachel Vining
    • 1
  • Subba R. Digumarthy
    • 1
    • 2
  • Mannudeep K. Kalra
    • 1
    • 2
    Email author
  1. 1.Division of Thoracic ImagingMassachusetts General HospitalBostonUSA
  2. 2.Harvard Medical SchoolBostonUSA

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