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Advance in EM

AI Beyond Human Eyes

Dr KB Wong

Council Member, HKSEMS

A systematic review of diagnostic errors in the emergency department (ED) found that with estimated rates for diagnostic error (5.7%), misdiagnosis-related harms (2.0%), and serious misdiagnosis-related harms (0.3%) in EM setting. (1)


An estimated 795,000 Americans become permanently disabled or die annually across care settings because dangerous diseases are misdiagnosed (2). Medical errors, including healthcare-related adverse events, occur in 8–12% of hospitalisations in Europe (3). Common types of medical errors typically include surgical, diagnostic, medication, devices and equipment, systems failures, infections, falls, and healthcare technology (4). Medical errors are described as the third leading cause of death in the US (5). The prevalence and burden of missed diagnoses are significant, and this article will provide a brief overview of advancements in AI that see beyond our human capabilities.


AI includes machine learning, which encompasses a subset called artificial neural networks (ANNs). These networks mimic how biological neurons in the brain signal one another. The network architecture receives medical images as inputs. Subsequently, a Deep Convolutional Neural Network (DCNN) is utilised to perform dimension reduction, extracting crucial information from the images. This extracted information is then used to construct a data matrix, which is further converted into a vector. The vector is processed by a fully connected neural network, which subsequently determines the outputs. For instance, it can make predictions or classify the images. Machine learning algorithms can analyse large amounts of patient data, such as medical images, genetic information, and medical records, to identify patterns and make accurate determinations about the presence of disease or something entirely new.


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Photo from Serdar Abut, Hayrettin Okut, Rosey Zackula and Ken James Kallail Deep Neural Networks and Applications in Medical Research Published: 13 September 2023


Recent advancement 


What ECG can predict ? 

As an experienced physician, given an ECG, we are all able to estimate the gender of the patient with 50% accuracy. However, with the use of an AI model, ECG can predict the gender of the patient with an area under the curve (AUC) of 0.97 (7). Ejection fraction can be predicted by ECG with a deep learning model showing an AUC of 0.9472, with a sensitivity of 86.9% and specificity of 89.6% (8). ECG can also predict anaemia (9), AF and stroke (10), diabetes (11), CKD (12), and hyperthyroidism (13).


What CXR can predict ?

CXR in A&E is typically used to determine pulmonary disease. However, with the use of an AI model, CXR can predict a patient's race with an AUC of 0.91-0.99 (14), diabetes (15), and multiple cardiac functions and valvular heart diseases (16). These are entirely new areas that no radiologist or clinician could identify, but the machine did.


What can a retinal photo predict?

Retinal vessels may provide a readily accessible surrogate approach to studying vascular disease. With computer vision, a lot of new things can be identified that were not visible to the human eye. Retinal photos are able to predict diabetes (17), CKD (18), liver and gallbladder disease (19), heart calcium score (20), Alzheimer's disease (21), myocardial infarction (22), and Parkinson's disease (23).


The use of machine learning can process large amounts of data, generate new hypotheses, and provide insights that help clinicians apply novel interpretations of simple investigations. There is still a long road ahead for the integration of machine learning into our daily practice. Further large-scale prospective studies and validation of models will be needed. We hope that the use of AI will assist emergency physicians in diagnosis and help reduce medical errors.


References

  1. Newman-Toker DE, Peterson SM, Badihian S, et al. Diagnostic Errors in the Emergency Department: A Systematic Review. Rockville MD: Agency for Healthcare Research and Quality; 2022. p. 744.

  2. Newman-Toker DE, Nassery N, Schaffer AC, Yu-Moe CW, Clemens GD, Wang Z, Zhu Y, Saber Tehrani AS, Fanai M, Hassoon A, Siegal D. Burden of serious harms from diagnostic error in the USA. BMJ Qual Saf. 2024 Jan 19;33(2):109-120. doi: 10.1136/bmjqs-2021-014130. PMID: 37460118; PMCID: PMC10792094.

  3. WHO Regional Office for Europe. Patient safety, data and statistics [Internet]. 2021. Available from: https://www.euro.who.int/en/health-topics/Health-systems/patient-safety/data-and-statistics. Accessed 2021 Jan 29.

  4. Rodziewicz TL, Houseman B, Hipskind JE. Medical Error Reduction and Prevention. 2023 May.

  5. Makary MA, Daniel M. Medical error—the third leading cause of death in the US. BMJ. 2016 May 3;353. doi: 10.1136/bmj.i2139. PMID: 27143499.

  6. Abut S, Okut H, Zackula R, Kallail KJ. Deep Neural Networks and Applications in Medical Research. Published: 13 September 2023.

  7. Attia ZI, Friedman PA, Noseworthy PA, Lopez-Jimenez F, Ladewig DJ, Satam G, Pellikka PA, Munger TM, Asirvatham SJ, Scott CG, Carter RE, Kapa S. Age and sex estimation using artificial intelligence from standard 12-lead ECGs. Circ Arrhythm Electrophysiol. 2019 Sep;12(9). doi: 10.1161/CIRCEP.119.007284. Epub 2019 Aug 27. PMID: 31450977; PMCID: PMC7661045.

  8. Chen HY, Lin CS, Fang WH, Lou YS, Cheng CC, Lee CC, Lin C. Artificial intelligence-enabled electrocardiography predicts left ventricular dysfunction and future cardiovascular outcomes: a retrospective analysis. J Pers Med. 2022 Mar 13;12(3):455. doi: 10.3390/jpm12030455. PMID: 35330455; PMCID: PMC8950054.

  9. Kwon JM, Cho Y, Jeon KH, Cho S, Kim KH, Baek SD, Jeung S, Park J, Oh BH. A deep learning algorithm to detect anaemia with ECGs: a retrospective, multicentre study. Lancet Digit Health. 2020 Jul;2(7). doi: 10.1016/S2589-7500(20)30108-4. Epub 2020 Jun 23. PMID: 33328095.

  10. Raghunath S, Pfeifer JM, Kelsey CR, Nemani A, Ruhl JA, Hartzel DN, Ulloa Cerna AE, Jing L, van Maanen DP, Leader JB, Schneider G, Morland TB, Chen R, Zimmerman N, Fornwalt BK, Haggerty CM. An ECG-based machine learning model for predicting new-onset atrial fibrillation is superior to age and clinical features in identifying patients at high stroke risk. J Electrocardiol. 2023 Jan-Feb;76:61-65. doi: 10.1016/j.jelectrocard.2022.11.001. Epub 2022 Nov 8. PMID: 36436476.

  11. Lin CS, Lee YT, Fang WH, Lou YS, Kuo FC, Lee CC, Lin C. Deep learning algorithm for management of diabetes mellitus via electrocardiogram-based glycated hemoglobin (ECG-HbA1c): a retrospective cohort study. J Pers Med. 2021 Jul 27;11(8):725. doi: 10.3390/jpm11080725. PMID: 34442369; PMCID: PMC8398464.

  12. Holmstrom L, Christensen M, Yuan N, Weston Hughes J, Theurer J, Jujjavarapu M, Fatehi P, Kwan A, Sandhu RK, Ebinger J, Cheng S, Zou J, Chugh SS, Ouyang D. Deep learning-based electrocardiographic screening for chronic kidney disease. Commun Med (Lond). 2023 May 26;3(1):73. doi: 10.1038/s43856-023-00278-w. PMID: 37237055; PMCID: PMC10220039.

  13. Choi B, Jang JH, Son M, Lee MS, Jo YY, Jeon JY, Jin U, Soh M, Park RW, Kwon JM. Electrocardiographic biomarker based on machine learning for detecting overt hyperthyroidism. Eur Heart J Digit Health. 2022 Apr 20;3(2):255-264. doi: 10.1093/ehjdh/ztac013. PMID: 36713007; PMCID: PMC9707932.

  14. Gichoya JW, Banerjee I, Bhimireddy AR, Burns JL, Celi LA, Chen LC, Correa R, Dullerud N, Ghassemi M, Huang SC, Kuo PC, Lungren MP, Palmer LJ, Price BJ, Purkayastha S, Pyrros AT, Oakden-Rayner L, Okechukwu C, Seyyed-Kalantari L, Trivedi H, Wang R, Zaiman Z, Zhang H. AI recognition of patient race in medical imaging: a modelling study. Lancet Digit Health. 2022 Jun;4(6). doi: 10.1016/S2589-7500(22)00063-2. Epub 2022 May 11. PMID: 35568690; PMCID: PMC9650160.

  15. Pyrros A, Borstelmann SM, Mantravadi R, Zaiman Z, Thomas K, Price B, Greenstein E, Siddiqui N, Willis M, Shulhan I, Hines-Shah J, Horowitz JM, Nikolaidis P, Lungren MP, Rodríguez-Fernández JM, Gichoya JW, Koyejo S, Flanders AE, Khandwala N, Gupta A, Garrett JW, Cohen JP, Layden BT, Pickhardt PJ, Galanter W. Opportunistic detection of type 2 diabetes using deep learning from frontal chest radiographs. Nat Commun. 2023 Jul 7;14(1):4039. doi: 10.1038/s41467-023-39631-x. PMID: 37419921; PMCID: PMC10328953.

  16. Ueda D, Matsumoto T, Ehara S, Yamamoto A, Walston SL, Ito A, Shimono T, Shiba M, Takeshita T, Fukuda D, Miki Y. Artificial intelligence-based model to classify cardiac functions from chest radiographs: a multi-institutional, retrospective model development and validation study. Lancet Digit Health. 2023 Aug;5(8).

  17. Abràmoff MD, Lavin PT, Birch M, Shah N, Folk JC. Pivotal trial of an autonomous AI-based diagnostic system for detection of diabetic retinopathy in primary care offices. NPJ Digit Med. 2018 Aug 28;1:39.

  18. Sabanayagam C, Xu D, Ting DSW, Nusinovici S, Banu R, Hamzah H, Lim C, Tham YC, Cheung CY, Tai ES, Wang YX, Jonas JB, Cheng CY, Lee ML, Hsu W, Wong TY. A deep learning algorithm to detect chronic kidney disease from retinal photographs in community-based populations. Lancet Digit Health. 2020 Jun;2(6).

  19. Yuan TH, Yue ZS, Zhang GH, Wang L, Dou GR. Beyond the liver: Liver-eye communication in clinical and experimental aspects. Front Mol Biosci. 2021 Dec 24;8:823277.

  20. Rim TH, Lee CJ, Tham YC, Cheung N, Yu M, Lee G, Kim Y, Ting DSW, Chong CCY, Choi YS, Yoo TK, Ryu IH, Baik SJ, Kim YA, Kim SK, Lee SH, Lee BK, Kang SM, Wong EYM, Kim HC, Kim SS, Park S, Cheng CY, Wong TY. Deep-learning-based cardiovascular risk stratification using coronary artery calcium scores predicted from retinal photographs. Lancet Digit Health. 2021 May;3(5).

  21. Cheung CY, Ran AR, Wang S, Chan VTT, Sham K, Hilal S, Venketasubramanian N, Cheng CY, Sabanayagam C, Tham YC, Schmetterer L, McKay GJ, Williams MA, Wong A, Au LWC, Lu Z, Yam JC, Tham CC, Chen JJ, Dumitrascu OM, Heng PA, Kwok TCY, Mok VCT, Milea D, Chen CL, Wong TY. A deep learning model for detection of Alzheimer's disease based on retinal photographs: a retrospective, multicentre case-control study. Lancet Digit Health. 2022 Nov;4(11).

  22. Predicting myocardial infarction through retinal scans and minimal personal information. Nat Mach Intell. 2022;4:55–61.

  23. Lenharo M. AI detects eye disease and risk of Parkinson's from retinal images. Nature. 2023 Sep 13. doi: 10.1038/d41586-023-02881-2.



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