Impacto de la IA en la Predicción y Detección Oportuna de Enfermedades: Una Revisión Sistemática
DOI:
https://doi.org/10.69681/lajae.v7i1.35Palabras clave:
Machine learning, diagnóstico médico, algoritmos predictivos, salud 4.0Resumen
La Inteligencia Artificial (IA) ha surgido como una herramienta transformadora en el entorno de la salud. Esta revisión tiene como objetivo evaluar los efectos de las tecnologías con base en IA en la predicción y detección oportuna de enfermedades. Se realizó una revisión sistemática de la literatura con base en los lineamientos de la metodología PRISMA. A partir de un total de 27,359 estudios identificados, 28 fueron incluidos debido a que demostraron la efectividad de los algoritmos de machine learning. Los resultados revelaron que los algoritmos se aplicaron principalmente para predecir enfermedades cardiovasculares (n = 9), neurológicas (n = 6), oncológicas (n = 5), hepáticas (n = 3), pulmonares (n = 3) e infecciosas (n = 2). Además, se midió la precisión de los principales algoritmos en 23 estudios y 19 reportaron valores superiores al 90%. La aplicación de técnicas de IA, ha demostrado un potencial significativo para mejorar el diagnóstico y la predicción de una amplia gama de enfermedades.
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D. Houfani, S. Slatnia, O. Kazar, H. Saouli, and A. Merizig, “Artificial intelligence in healthcare: a review on predicting clinical needs,” International Journal of Healthcare Management, vol. 15, no. 3. Taylor & Francis, p. 267, Feb. 28, 2021. doi: 10.1080/20479700.2021.1886478.
N. Caballé, J. L. Castillo, J. A. Gómez–Pulido, J. M. Gómez-Pulido, and M. L. Polo-Luque, “Machine Learning Applied to Diagnosis of Human Diseases: A Systematic Review,” Applied Sciences, vol. 10, no. 15. Multidisciplinary Digital Publishing Institute, p. 5135, Jul. 26, 2020. doi: 10.3390/app10155135.
B. J. Erickson, P. Korfiatis, Z. Akkus, and T. L. Kline, “Machine Learning for Medical Imaging,” Radiographics, vol. 37, no. 2. Radiological Society of North America, p. 505, Feb. 17, 2017. doi: 10.1148/rg.2017160130.
J. A. Cruz and D. S. Wishart, “Applications of machine learning in cancer prediction and prognosis.,” Feb. 11, 2007, National Institutes of Health. Accessed: Dec. 2024. [Online]. Available: https://pubmed.ncbi.nlm.nih.gov/19458758
R. Chalapathy and S. Chawla, “Deep Learning for Anomaly Detection: A Survey,” Jan. 01, 2019, Cornell University. doi: 10.48550/arXiv.1901.
M. Mann, R. P. Badoni, H. Soni, M. Al-Shehri, A. C. Kaushik, and D. Wei, “Utilization of Deep Convolutional Neural Networks for Accurate Chest X-Ray Diagnosis and Disease Detection,” Mar. 26, 2023, Springer Science+Business Media. doi: 10.1007/s12539-023-00562-2.
J. Ker, L. Wang, J. P. Rao, and C. C. T. Lim, “Deep Learning Applications in Medical Image Analysis,” Dec. 29, 2017, Institute of Electrical and Electronics Engineers. doi: 10.1109/access.2017.2788044.
A. Vellido, V. Ribas, C. Morales, A. R. Sanmartín, and J. C. Ruiz‐Rodríguez, “Machine learning in critical care: state-of-the-art and a sepsis case study,” Nov. 01, 2018, BioMed Central. doi: 10.1186/s12938-018-0569-2.
S. M. Varnosfaderani and M. Forouzanfar, “The Role of AI in Hospitals and Clinics: Transforming Healthcare in the 21st Century,” Mar. 29, 2024, Multidisciplinary Digital Publishing Institute. doi: 10.3390/bioengineering11040337.
K. Schenkelberg, U. Seidenberg, and F. Ansari, “Supervised Machine Learning for Knowledge-Based Analysis of Maintenance Impact on Profitability,” Jan. 01, 2020, Elsevier BV. doi: 10.1016/j.ifacol.2020.12.2830.
J. Chen, H. H. Cheong, and S. W. I. Siu, “xDeep-AcPEP: Deep Learning Method for Anticancer Peptide Activity Prediction Based on Convolutional Neural Network and Multitask Learning,” Jul. 30, 2021, American Chemical Society. doi: 10.1021/acs.jcim.1c00181.
F. Fernandes, H. Vicente, A. Abelha, J. Machado, P. Nováis, and J. Neves, “Artificial neural networks in diabetes control,” Jul. 01, 2015. doi: 10.1109/sai.2015.7237169.
M. L. Wang, “Algoritmos de minería de datos en la industria sanitaria,” Jan. 01, 2021, Cornell University. doi: 10.48550/arxiv.2104.09395.
B. Lund, “Four Categories of Academic Libraries: A Cluster Analysis Based on Collections, Expenditures, and Circulation per Student Data,” Oct. 01, 2017, Taylor & Francis. doi: 10.1080/14649055.2020.1794748.
G. L. T., J. A. R., and O. P. B., “Segmentación de Imágenes Médicas Digitales mediante Técnicas de Clustering,” Jul. 19, 2010, Universidad Nacional de Ancash “Santiago Antunez de Mayolo.” doi: 10.32911/as.2010.v3.n1.428.
M. C. de Souto, I. G. Costa, D. S. de Araujo, T. B. Ludermir, and A. Schliep, “Clustering cancer gene expression data: a comparative study,” Nov. 27, 2008, BioMed Central. doi: 10.1186/1471-2105-9-497.
J. Gardner and C. Brooks, “Evaluating Predictive Models of Student Success: Closing the Methodological Gap,” Aug. 05, 2018, Society for Learning Analytics Research. doi: 10.18608/jla.2018.52.7.
M. C. M. Delgado et al., “Indicadores de calidad en el enfermo crítico,” Jan. 01, 2008, Elsevier BV. doi: 10.1016/s0210-5691(08)70899-4.
D. J. Hand, “Assessing the Performance of Classification Methods,” Aug. 22, 2012, Wiley. doi: 10.1111/j.1751-5823.2012.00183.x.
D. Powers, “Evaluation: from Precision, Recall and F-measure to ROC, Informedness, Markedness and Correlation,” Dec. 15, 2011, Cornell University. Accessed: Nov. 2024. [Online]. Available: http://david.wardpowers.info/BM/Evaluation_%20From%20Precision%20and%20Recall%20....pdf
G. Dıcuonzo, G. Galeone, M. Shini, and A. Massari, “Towards the Use of Big Data in Healthcare: A Literature Review,” Healthcare, vol. 10, no. 7. Multidisciplinary Digital Publishing Institute, p. 1232, Jul. 01, 2022. doi: 10.3390/healthcare10071232.
I. R. Herrera and F. Gascón, “Nuevas herramientas en salud,” Jul. 03, 2012, Elsevier BV. doi: 10.1016/j.medcli.2012.05.010.
J. F. Á. de Tomás, M. Á. Mayer, and V. J. Q. Varela, “La inteligencia artificial y sus aplicaciones en medicina II: importancia actual y aplicaciones prácticas,” Atención Primaria, vol. 53, no. 1. Elsevier BV, p. 81, Jun. 19, 2020. doi: 10.1016/j.aprim.2020.04.014.
C. Kelly, A. Karthikesalingam, M. Suleyman, G. S. Corrado, and D. King, “Key challenges for delivering clinical impact with artificial intelligence,” Oct. 29, 2019, BioMed Central. doi: 10.1186/s12916-019-1426-2.
M. J. Page et al., “The PRISMA 2020 statement: an updated guideline for reporting systematic reviews,” Mar. 29, 2021. doi: 10.1136/bmj.n71.
J. P. T. Higgins, J. Savović, M. J. Page, R. G. Elbers, and J. A. C. Sterne, “Assessing risk of bias in a randomized trial,” Sep. 20, 2019. doi: 10.1002/9781119536604.ch8.
K. G. M. Moons et al., “PROBAST: A Tool to Assess Risk of Bias and Applicability of Prediction Model Studies: Explanation and Elaboration,” Dec. 31, 2018, American College of Physicians. doi: 10.7326/m18-1377.
E. Lella et al., “Machine Learning and DWI Brain Communicability Networks for Alzheimer’s Disease Detection,” Jan. 31, 2020, Multidisciplinary Digital Publishing Institute. doi: 10.3390/app10030934.
Y. Muhammad, M. Tahir, M. Hayat, and K. T. Chong, “Early and accurate detection and diagnosis of heart disease using intelligent computational model,” Nov. 12, 2020, Nature Portfolio. doi: 10.1038/s41598-020-76635-9.
F. Deshmukh and S. S. Merchant, “Explainable Machine Learning Model for Predicting GI Bleed Mortality in the Intensive Care Unit,” Apr. 25, 2020, Lippincott Williams & Wilkins. doi: 10.14309/ajg.0000000000000632.
Y. Wu and Y. Fang, “Stroke Prediction with Machine Learning Methods among Older Chinese,” Mar. 12, 2020, Multidisciplinary Digital Publishing Institute. doi: 10.3390/ijerph17061828.
T. Meng et al., “Identifying Facial Features and Predicting Patients of Acromegaly Using Three-Dimensional Imaging Techniques and Machine Learning,” Jul. 29, 2020, Frontiers Media. doi: 10.3389/fendo.2020.00492.
M. Syafrudin et al., “A Self-Care Prediction Model for Children with Disability Based on Genetic Algorithm and Extreme Gradient Boosting,” Sep. 15, 2020, Multidisciplinary Digital Publishing Institute. doi: 10.3390/math8091590.
M. Seifallahi, H. Soltanizadeh, A. H. Mehraban, and F. Khamseh, “Alzheimer’s disease detection using skeleton data recorded with Kinect camera,” Nov. 16, 2019, Springer Science+Business Media. doi: 10.1007/s10586-019-03014-z.
P. Rajpurkar et al., “CheXaid: deep learning assistance for physician diagnosis of tuberculosis using chest x-rays in patients with HIV,” Sep. 09, 2020, Nature Portfolio. doi: 10.1038/s41746-020-00322-2.
F. Mostafa, E. Hasan, M. Williamson, and H. Khan, “Statistical Machine Learning Approaches to Liver Disease Prediction,” Dec. 01, 2021, Multidisciplinary Digital Publishing Institute. doi: 10.3390/livers1040023.
L. Bloch and C. M. Friedrich, “Comparison of Automated Volume Extraction with FreeSurfer and FastSurfer for Early Alzheimer’s Disease Detection with Machine Learning,” Jun. 01, 2021. doi: 10.1109/cbms52027.2021.00096.
X. Qian et al., “A Cardiovascular Disease Prediction Model Based on Routine Physical Examination Indicators Using Machine Learning Methods: A Cohort Study,” Jun. 17, 2022, Frontiers Media. doi: 10.3389/fcvm.2022.854287.
H. Gong, M. Wang, H. Zhang, M. F. Elahe, and M. Jin, “An Explainable AI Approach for the Rapid Diagnosis of COVID-19 Using Ensemble Learning Algorithms,” Jun. 21, 2022, Frontiers Media. doi: 10.3389/fpubh.2022.874455.
K. S. K. Reddy and K. V. Kanimozhi, “Novel Intelligent Model for Heart Disease Prediction using Dynamic KNN (DKNN) with improved accuracy over SVM,” Feb. 16, 2022. doi: 10.1109/icbats54253.2022.9758996.
P. Theerthagiri, “Predictive analysis of cardiovascular disease using gradient boosting based learning and recursive feature elimination technique,” Sep. 06, 2022, Elsevier BV. doi: 10.1016/j.iswa.2022.200121.
S. Liu et al., “Generalizable deep learning model for early Alzheimer’s disease detection from structural MRIs,” Oct. 17, 2022, Nature Portfolio. doi: 10.1038/s41598-022-20674-x.
M. Feng et al., “Application of an Interpretable Machine Learning Model to Predict Lymph Node Metastasis in Patients with Laryngeal Carcinoma,” Nov. 12, 2022, Hindawi Publishing Corporation. doi: 10.1155/2022/6356399.
C. B. M. Karthi and A. Kalaivani, “Heart Disease Prediction Based on Age Detection using Novel Logistic Regression over Decision Tree,” Feb. 14, 2023, Russian New University. doi: 10.18137/cardiometry.2022.25.17181724.
S. Hadianti, “Optimization of The Machine Learning Approach using Optuna in Heart Disease Prediction,” Sep. 07, 2023. doi: 10.37034/medinftech.v1i3.15.
J. James P, C. B. E, and R. M, “Improved Coronary Artery Disease Prediction Using Persistence-Based Artificial Bee Colony Optimization Algorithm,” May 01, 2023, IJRAR-International Journal of Research and Analytical Reviews (IJRAR).
A. J. W. Lim et al., “Robust SNP-based prediction of rheumatoid arthritis through machine-learning-optimized polygenic risk score,” Feb. 07, 2023, BioMed Central. doi: 10.1186/s12967-023-03939-5.
G. S. Karthick and P. B. Pankajavalli, “Chronic obstructive pulmonary disease prediction using Internet of things-spiro system and fuzzy-based quantum neural network classifier,” Aug. 27, 2022, Elsevier BV. doi: 10.1016/j.tcs.2022.08.021.
L. Ye and C. Guo, “Heart failure disease prediction and stratification with temporal electronic health records data using patient representation,” Jan. 01, 2023, Elsevier BV. doi: 10.1016/j.bbe.2022.12.008.
S. W. A. Sherazi, H. Zheng, and J. Y. Lee, “A Machine Learning-Based Applied Prediction Model for Identification of Acute Coronary Syndrome (ACS) Outcomes and Mortality in Patients during the Hospital Stay,” Jan. 25, 2023, Multidisciplinary Digital Publishing Institute. doi: 10.3390/s23031351.
F. Li, P. Wu, H. H. Ong, J. F. Peterson, W. Wei, and J. Zhao, “Evaluating and mitigating bias in machine learning models for cardiovascular disease prediction,” Jan. 25, 2023, Elsevier BV. doi: 10.1016/j.jbi.2023.104294.
S. T. Arasteh et al., “Federated Learning for Secure Development of AI Models for Parkinson’s Disease Detection Using Speech from Different Languages,” Aug. 14, 2023. doi: 10.21437/interspeech.2023-2108.
P. Roshanitabrizi et al., “Analysis of Mitral Regurgitation Jet for Rheumatic Heart Disease Detection in Doppler Echocardiography,” Apr. 18, 2023. doi: 10.1109/isbi53787.2023.10230680.
F. R. Anas et al., “Early-stage parkinson’s disease detection based on
action unit derivatives,” Sep. 28, 2023, olloque en TéléSANté et dispositifs biomédicaux ( JETSAN).
K. Brown et al., “Using Artificial Intelligence for Rheumatic Heart Disease Detection by Echocardiography: Focus on Mitral Regurgitation,” Jan. 16, 2024, Wiley. doi: 10.1161/jaha.123.031257.
M. Vázquez-Marrufo, E. Sarrias-Arrabal, M. Garcia‐Torres, R. Martı́n-Clemente, and G. Izquierdo, “A systematic review of the application of machine-learning algorithms in multiple sclerosis,” Neurología (English Edition), vol. 38, no. 8. Elsevier BV, p. 577, Jul. 14, 2022. doi: 10.1016/j.nrleng.2020.10.013.
N. Kaieski, C. A. da Costa, R. da R. Righi, P. S. Lora, and B. M. Eskofier, “Application of artificial intelligence methods in vital signs analysis of hospitalized patients: A systematic literature review,” Aug. 06, 2020, Elsevier BV. doi: 10.1016/j.asoc.2020.106612.
A. N. Reiz, M. Á. A. de la Hoz, and M. S. García, “Big Data Analysis y Machine Learning en medicina intensiva,” Medicina Intensiva, vol. 43, no. 7. Elsevier BV, p. 416, Dec. 24, 2018. doi: 10.1016/j.medin.2018.10.007.
S. Romiti, M. Vinciguerra, W. Saade, I. A. Cortajarena, and E. Greco, “Artificial Intelligence (AI) and Cardiovascular Diseases: An Unexpected Alliance,” Cardiology Research and Practice, vol. 2020. Hindawi Publishing Corporation, p. 1, Jun. 27, 2020. doi: 10.1155/2020/4972346.
S. Sanchez-Martinez et al., “Machine Learning for Clinical Decision-Making: Challenges and Opportunities in Cardiovascular Imaging,” Frontiers in Cardiovascular Medicine, vol. 8. Frontiers Media, Jan. 04, 2022. doi: 10.3389/fcvm.2021.765693.
S. Subramani et al., “Cardiovascular diseases prediction by machine learning incorporation with deep learning,” Apr. 17, 2023, Frontiers Media. doi: 10.3389/fmed.2023.1150933.
Á. J. Soto et al., “Diagnóstico de metástasis en pacientes con cáncer de próstata resistente a la castración: algoritmo de decisión en pruebas de imagen,” Actas Urológicas Españolas, vol. 43, no. 2. Elsevier BV, p. 55, Aug. 03, 2018. doi: 10.1016/j.acuro.2018.05.011.
J. A. Cruz and D. S. Wishart, “Applications of Machine Learning in Cancer Prediction and Prognosis,” Jan. 01, 2006, SAGE Publishing. doi: 10.1177/117693510600200030.
L. B. Cardoso et al., “Machine learning for predicting survival of colorectal cancer patients,” Jun. 01, 2023, Nature Portfolio. doi: 10.1038/s41598-023-35649-9.
R. Alloro and E. Sinagra, “Artificial intelligence and colorectal cancer: How far can you go?,” Apr. 26, 2021. doi: 10.35713/aic.v2.i2.7.
N. Jahan, Md. S. Anower, and R. Hassan, “Automated Diagnosis of Pneumonia from Classification of Chest X-Ray Im ages using EfficientNet,” Feb. 27, 2021. doi: 10.1109/icict4sd50815.2021.9397055.
Sato; Masaya; Morimoto; Kentaro; Kajihara; Shigeki; Tateishi; Ryosuke; Shiina; Shuichiro; Koike; Kazuhiko; Yatomi; Yutaka, “Machine-learning Approach for the Development of a Novel Predictive Model for the Diagnosis of Hepatocellular Carcinoma - Scientific Reports.” Feb. 2023. Accessed: Dec. 10, 2024. [Online]. Available: https://www.nature.com/articles/s41598-019-44022-8
R. A. Mancisidor, “Using multimodal learning and deep generative models for corporate bankruptcy prediction,” Jan. 01, 2022, Cornell University. doi: 10.48550/arxiv.2211.08405.
Y. Liu et al., “On the Hidden Mystery of OCR in Large Multimodal Models,” Jan. 01, 2023, Cornell University. doi: 10.48550/arXiv.2305.
R. M. Ferrer, “Valoraciones éticas para una inteligencia artificial adecuada a la privacidad,” Dec. 30, 2021, Spanish National Research Council. doi: 10.3989/arbor.2021.802006.
R. Benjamins and I. S. García, “Towards a framework for understanding societal and ethical implications of Artificial Intelligence,” in Dykinson eBooks, 2020, p. 89. doi: 10.2307/j.ctv153k50r.8.
O. de C. y T. del C. de los Diputados, “Inteligencia artificial y salud,” European Organization for Nuclear Research, Nov. 2022. doi: 10.57952/tcsx-b678.
J. A. Martínez, J. Mejía, and M. Muñoz, “Security analysis of the Internet of Things: A systematic literature review,” Oct. 01, 2016. doi: 10.1109/cimps.2016.7802809.
S. T. Hashemi, O. M. E. E., and H. Kaur, “Cost estimation and prediction in construction projects: a systematic review on machine learning techniques,” SN Applied Sciences, vol. 2, no. 10. Springer Nature, Sep. 15, 2020. doi: 10.1007/s42452-020-03497-1.
J. Shaw, F. Rudzicz, T. Jamieson, and A. Goldfarb, “Artificial Intelligence and the Implementation Challenge,” May 31, 2019, JMIR Publications. doi: 10.2196/13659.
T. H. Davenport and J. Glaser, “Factors governing the adoption of artificial intelligence in healthcare providers,” Oct. 31, 2022, Springer Science+Business Media. doi: 10.1007/s44250-022-00004-8.
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Derechos de autor 2024 Carlos Zepeda Lugo, Andrea Insfran-Rivarola, Ana Arévalos
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