References
- Bouayadi O, Bensalah M, Rahmani N, et al. Serum protein electrophoresis: study of 410 electrophoretic profiles. Pan Afr Med J. 2019;32:161.
- Lee AY, Cassar PM, Johnston AM, et al. Clinical use and interpretation of serum protein electrophoresis and adjunct assays. Br J Hosp Med (Lond). 2017;78(2):C18–c20. doi: 10.12968/hmed.2017.78.2.C18.
- O'Connell TX, Horita TJ, Kasravi B. Understanding and interpreting serum protein electrophoresis. Am Fam Physician. 2005;71:105–112.
- Bossuyt X. Separation of serum proteins by automated capillary zone electrophoresis. Clin Chem Lab Med. 2003;41:762–772.
- McCudden CR, Jacobs JFM, Keren D, et al. Recognition and management of common, rare, and novel serum protein electrophoresis and immunofixation interferences. Clin Biochem. 2018;51:72–79. doi: 10.1016/j.clinbiochem.2017.08.013.
- Booth RA, McCudden CR, Balion CM, et al. Candidate recommendations for protein electrophoresis reporting from the Canadian society of clinical chemists monoclonal gammopathy working group. Clin Biochem. 2018;51:10–20. doi: 10.1016/j.clinbiochem.2017.10.013.
- Chabrun F, Dieu X, Ferre M, et al. Achieving Expert-Level interpretation of serum protein electrophoresis through deep learning driven by human reasoning. Clin Chem. 2021;67(10):1406–1414. doi: 10.1093/clinchem/hvab133.
- Lichtinghagen R, Pietsch D, Brand K. Evaluation of an automated capillary electrophoresis system for serum protein electrophoresis with the determination of gender-specific reference values. Clinical Laboratory. 2010;56:119–126.
- Katzmann J, Snyder M, Rajkumar S, et al. Long-term biological variation of serum protein electrophoresis M-spike, urine M-spike, and monoclonal serum free light chain quantification: implications for monitoring monoclonal gammopathies. Clin Chem. 2011;57(12):1687–1692. doi: 10.1373/clinchem.2011.171314.
- Kyle RA, Rajkumar SV. Criteria for diagnosis, staging, risk stratification and response assessment of multiple myeloma. Leukemia. 2009;23(1):3–9. doi: 10.1038/leu.2008.291.
- Greenberg AJ, Rajkumar SV, Vachon CM. Familial monoclonal gammopathy of undetermined significance and multiple myeloma: epidemiology, risk factors, and biological characteristics. Blood. 2012;119(23):5359–5366. doi: 10.1182/blood-2011-11-387324.
- Go RS, Rajkumar SV. How I manage monoclonal gammopathy of undetermined significance. Blood. 2018;131(2):163–173. doi: 10.1182/blood-2017-09-807560.
- Rajkumar SV, Dimopoulos MA, Palumbo A, et al. International myeloma working group updated criteria for the diagnosis of multiple myeloma. Lancet Oncol. 2014;15(12):e538-548–e548. doi: 10.1016/S1470-2045(14)70442-5.
- Kyle RA, Larson DR, Therneau TM, et al. Long-Term follow-up of monoclonal gammopathy of undetermined significance. N Engl J Med. 2018;378(3):241–249. doi: 10.1056/NEJMoa1709974.
- Atkin C, Richter A, Sapey E. What is the significance of monoclonal gammopathy of undetermined significance? Clin Med (Lond). 2018;18(5):391–396. doi: 10.7861/clinmedicine.18-5-391.
- Cowan A, Green D, Kwok M, et al. Diagnosis and management of multiple myeloma: a review. JAMA. 2022;327(5):464–477. doi: 10.1001/jama.2022.0003.
- Keren DF, Bocsi G, Billman BL, et al. Laboratory detection and initial diagnosis of monoclonal gammopathies. Arch Pathol Lab Med. 2022;146(5):575–590. doi: 10.5858/arpa.2020-0794-CP.
- Blanchard J, Steiger E, O'Neil M, et al. Effect of protein depletion and repletion on liver structures, nitrogen content and serum proteins. Ann Surg. 1979;190(2):144–150. doi: 10.1097/00000658-197908000-00004.
- Rodriguez-Ballestas E, Reid-Adam J. Nephrotic syndrome. Pediatr Rev. 2022;43(2):87–99. doi: 10.1542/pir.2020-001230.
- Sthaneshwar P, Thambiah S, Mat Salleh M, et al. Survey on serum protein electrophoresis and recommendations for standardised reporting. Malaysian J Pathol. 2021;43:281–290.
- Vavricka SR, Burri E, Beglinger C, et al. Serum protein electrophoresis: an underused but very useful test. Digestion. 2009;79(4):203–210. doi: 10.1159/000212077.
- Rajkomar A, Dean J, Kohane I. Machine learning in medicine. N Engl J Med. 2019;380(14):1347–1358. doi: 10.1056/NEJMra1814259.
- Turner KA, Frinack JL, Ettore MW, et al. An international multi-center serum protein electrophoresis accuracy and M-protein isotyping study. Part I: factors impacting limit of quantitation of serum protein electrophoresis. Clin Chem Lab Med. 2020;58(4):533–546. doi: 10.1515/cclm-2019-1104.
- Tate J, Caldwell G, Daly J, et al. Recommendations for standardized reporting of protein electrophoresis in Australia and New Zealand. Ann Clin Biochem. 2012;49(Pt 3):242–256. doi: 10.1258/acb.2011.011158.
- Caillon H, Fraissinet F, Denis MG, et al. Identification of 5-fluorocytosine as a new interfering compound in serum capillary zone electrophoresis. Clin Chem Lab Med. 2017;55(3):e56–e58. doi: 10.1515/cclm-2016-0526.
- Bossuyt X, Peetermans WE. Effect of piperacillin-tazobactam on clinical capillary zone electrophoresis of serum proteins. Clin Chem. 2002;48(1):204–205. doi: 10.1093/clinchem/48.1.204.
- Lonial S, Dimopoulos M, Palumbo A, et al. Elotuzumab therapy for relapsed or refractory multiple myeloma. N Engl J Med. 2015;373(7):621–631. doi: 10.1056/NEJMoa1505654.
- Willrich MA, Ladwig PM, Andreguetto BD, et al. Monoclonal antibody therapeutics as potential interferences on protein electrophoresis and immunofixation. Clin Chem Lab Med. 2016;54:1085–1093.
- Qiu LL, Levinson SS, Keeling KL, et al. Convenient and effective method for removing fibrinogen from serum specimens before protein electrophoresis. Clin Chem. 2003;49(6 Pt 1):868–872. doi: 10.1373/49.6.868.
- Bossuyt X. Advances in serum protein electrophoresis. Adv Clin Chem. 2006;42:43–80.
- Moss MA. Moving towards harmonized reporting of serum and urine protein electrophoresis. Clin Chem Lab Med. 2016;54:973–979.
- LeCun Y, Bengio Y, Hinton G. Deep learning. Nature. 2015;521(7553):436–444. doi: 10.1038/nature14539.
- Jiang F, Jiang Y, Zhi H, et al. Artificial intelligence in healthcare: past, present and future. Stroke Vasc Neurol. 2017;2(4):230–243. doi: 10.1136/svn-2017-000101.
- Esteva A, Robicquet A, Ramsundar B, et al. A guide to deep learning in healthcare. Nat Med. 2019;25(1):24–29. doi: 10.1038/s41591-018-0316-z.
- Yang HC, Islam MM, Jack Li YC. Potentiality of deep learning application in healthcare. Comput Methods Programs Biomed. 2018;161: a 1. doi: 10.1016/j.cmpb.2018.05.014.
- Chan HP, Samala RK, Hadjiiski LM, et al. Deep learning in medical image analysis. Adv Exp Med Biol. 2020;1213:3–21.
- Miotto R, Wang F, Wang S, et al. Deep learning for healthcare: review, opportunities and challenges. Brief Bioinform. 2018;19(6):1236–1246. doi: 10.1093/bib/bbx044.
- Deo R. Machine learning in medicine. Circulation. 2015;132(20):1920–1930. doi: 10.1161/CIRCULATIONAHA.115.001593.
- Kononenko I. Machine learning for medical diagnosis: history, state of the art and perspective. Artif Intell Med. 2001;23(1):89–109. doi: 10.1016/s0933-3657(01)00077-x.
- Hosmer DW, Jr Lemeshow S, Sturdivant RX. Model-building strategies and methods for logistic regression. In: Hosmer DW, Lemeshow S, Sturdivant RX, editors. Applied logistic regression. John Wiley & Sons; 2013.
- Cortes C, Vapnik V. Support-vector networks. Mach Learn. 1995;20(3):273–297. doi: 10.1007/BF00994018.
- Breiman L. Random forests. Mach Learn. 2001;45(1):5–32. doi: 10.1023/A:1010933404324.
- Chen R, Jaye DL, Roback JD, et al. Automated serum protein electrophoresis interpretation using machine Learning-Based algorithm for paraprotein detection. Am J Clin Pathol. 2020;154(Supplement_1):S7–S8. doi: 10.1093/ajcp/aqaa137.013.
- Chen R, Jaye DL, Roback JD, et al. Lightweight, open source, easy-use algorithm and web service for paraprotein screening using spatial frequency domain analysis of electrophoresis studies. J Pathol Inform. 2022;13:100128. doi: 10.1016/j.jpi.2022.100128.
- Quinlan JR. Induction of decision trees. Mach Learn. 1986;1(1):81–106. doi: 10.1007/BF00116251.
- Li H, Racine-Brzostek S, Xi N, et al. Learning to detect monoclonal protein in electrophoresis images. In: 2021 International Conference on Visual Communications and Image Processing (VCIP). Munich, Germany; 2021. pp. 1–5. doi: 10.1109/VCIP53242.2021.9675332.
- Kratzer M, Ivandic B, Fateh-Moghadam A. Neuronal network analysis of serum electrophoresis. J Clin Pathol. 1992;45(7):612–615. doi: 10.1136/jcp.45.7.612.
- Li J, Cheng J-h, Shi J-y, et al. Brief introduction of back propagation (BP) neural network algorithm and its improvement. In: Jin D, Lin S, editors. Advances in computer science and information engineering. Berlin, Heidelberg: Springer Berlin Heidelberg; 2012. p. 553–558.
- Männer GA, Schweiger CR, Söregi G, et al. Detection of monoclonal gammopathies in serum electrophoresis by neural networks. Clin Chem. 1993;39(9):1984–1985. doi: 10.1093/clinchem/39.9.1984.
- Jonsson M, Carlson J, Jeppsson JO, et al. Computer-supported detection of M-components and evaluation of immunoglobulins after capillary electrophoresis. Clin Chem. 2001;47(1):110–117. doi: 10.1093/clinchem/47.1.110.
- Ognibene A, Motta R, Caldini A, et al. Artificial neural network-based algorithm for the evaluation of serum protein capillary electrophoresis. Clin Chem Lab Med. 2004;42(12):1451–1452. doi: 10.1515/CCLM.2004.271.
- Hawkins DM. The problem of overfitting. J Chem Inf Comput Sci. 2004;44(1):1–12. doi: 10.1021/ci0342472.
- Ognibene A, Graziani MS, Caldini A, et al. Computer-assisted detection of monoclonal components: results from the multicenter study for the evaluation of CASPER (computer assisted serum protein electrophoresis recognizer) algorithm. Clin Chem Lab Med. 2008;46:1183–1188.
- Dorizzi RM, Zanardi V, Agnoletti R, et al. Assessment of an expert system for the automated validation of electrophoretic profiles. Clin Lab. 2015;61(1-2):191–194. doi: 10.7754/clin.lab.2014.140715.
- Kramer MA. Autoassociative neural networks. Comp Chem Eng. 1992;16(4):313–328. doi: 10.1016/0098-1354(92)80051-A.
- Altinier S, Sarti L, Varagnolo M, et al. An expert system for the classification of serum protein electrophoresis patterns. Clin Chem Lab Med. 2008;46:1458–1463.
- Borrillo F, Infusino I, Birindelli S, et al. Use of neurosoft expert system improves turnaround time in a laboratory section specialized in protein diagnostics: a two-year experience. Clin Chem Lab Med. 2021;59(9):e367–e369. doi: 10.1515/cclm-2021-0146.
- Lee N, Jeong S, Jeon K, et al. Development and validation of a deep learning-based protein electrophoresis classification algorithm. PLoS One. 2022;17(8):e0273284. doi: 10.1371/journal.pone.0273284.
- Huang G, Liu Z, Maaten LVD, et al. Densely connected convolutional networks. In: 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR). 2017. pp. 2261–2269. doi: 10.1109/CVPR.2017.243.
- Simonyan K, Zisserman A. Very deep convolutional networks for Large-Scale image recognition. CoRR. arXiv:1409.1556. 2014.
- Szegedy C, Vanhoucke V, Ioffe S, et al. Rethinking the inception architecture for computer vision. In: 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR). 2016. pp. 2818–2826. doi: 10.1109/CVPR.2016.308.
- Selvaraju RR, Cogswell M, Das A, et al. Grad-CAM: visual explanations from deep networks via Gradient-Based localization. In: 2017 IEEE International Conference on Computer Vision (ICCV). 2017. pp. 618–626. doi: 10.1109/ICCV.2017.74.
- Smith J, Raines G, Schneider HG. A comparison between high resolution serum protein electrophoresis and screening immunofixation for the detection of monoclonal gammopathies in serum. Clin Chem Lab Med. 2018;56(2):256–263. doi: 10.1515/cclm-2017-0266.
- Hu H, Xu W, Jiang T, et al. Expert-Level immunofixation electrophoresis image recognition based on explainable and generalizable deep learning. Clin Chem. 2023;69(2):130–139. doi: 10.1093/clinchem/hvac190.
- He K, Zhang X, Ren S, et al. Deep residual learning for image recognition. In: 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2016. pp. 770–778. doi: 10.1109/CVPR.2016.90.
- Sandler M, Howard A, Zhu M, et al. MobileNetV2: inverted residuals and linear bottlenecks. In: 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition, 2018. pp. 4510–4520.
- Wang H, Wang Z, Du M, et al. Score-CAM: score-Weighted visual explanations for convolutional neural networks. In: 2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW), 2020. pp. 111–119. doi: 10.1109/CVPRW50498.2020.00020.
- Wei XY, Yang ZQ, Zhang XL, et al. Deep collocative learning for immunofixation electrophoresis image analysis. IEEE Trans Med Imaging. 2021;40(7):1898–1910. doi: 10.1109/TMI.2021.3068404.
- Ronneberger O, Fischer P, Brox T. U-Net: convolutional networks for biomedical image segmentation. ArXiv; abs/1505.04597. 2015
- Chabrun F, Dieu X, Reynier P, et al. In reply to performance of deep learning in the interpretation of serum protein electrophoresis. Clin Chem. 2022;68(10):1341–1343. doi: 10.1093/clinchem/hvac145.
- He H, Wang L, Wang X, et al. Performance of deep learning in the interpretation of serum protein electrophoresis. Clin Chem. 2022;68(10):1340–1341. doi: 10.1093/clinchem/hvac144.
- Pasolli E, Melgani F. Active learning methods for electrocardiographic signal classification. IEEE Trans Inf Technol Biomed. 2010;14(6):1405–1416. doi: 10.1109/TITB.2010.2048922.
- Gal Y, Islam R, Ghahramani Z. Deep bayesian active learning with image data Proceedings of the 34th International Conference on Machine Learning - Volume 70. Sydney, NSW, Australia, 2017. pp. 1183–1192.
- Yang L, Zhang Y, Chen J, et al. Suggestive annotation: a deep active learning framework for biomedical image segmentation. In: Descoteaux M, Maier-Hein L, Franz A, Jannin P, Collins DL, Duchesne S, editors. Medical image computing and computer assisted intervention − MICCAI 2017. Cham: Springer International Publishing; 2017. pp. 399–407.
- Mahapatra D, Bozorgtabar B , Thiran J-P, Reyes M. Efficient active learning for image classification and segmentation using a sample selection and conditional generative adversarial network. In: Frangi AF, Schnabel JA, Davatzikos C, Alberola-López. C, Fichtinger G, editors. Medical image computing and computer assisted intervention – MICCAI 2018. Cham: Springer International Publishing; 2018. pp. 580–588.
- Zhou Z, Shin JY, Gurudu SR, et al. Active, continual fine tuning of convolutional neural networks for reducing annotation efforts. Med Image Anal. 2021;71:101997. doi: 10.1016/j.media.2021.101997.
- Zhou W, Xie Y. An overview on interactive medical image segmentation. Comput Math Methods Med. 2013;2013:1–13. doi: 10.1155/2013/325903.
- Settles B. Active learning literature survey. 2009.
- McMahan HB, Moore E, Ramage D, et al. Communication-Efficient learning of deep networks from decentralized data. In: International Conference on Artificial Intelligence and Statistics, 2016.
- Sheller MJ, Edwards B, Reina GA, et al. Federated learning in medicine: facilitating multi-institutional collaborations without sharing patient data. Sci Rep. 2020;10(1):12598. doi: 10.1038/s41598-020-69250-1.
- Dayan I, Roth HR, Zhong A, et al. Federated learning for predicting clinical outcomes in patients with COVID-19. Nat Med. 2021;27(10):1735–1743. doi: 10.1038/s41591-021-01506-3.
- Rieke N, Hancox J, Li W, et al. The future of digital health with federated learning. NPJ Digit Med. 2020;3(1):119. doi: 10.1038/s41746-020-00323-1.