{"id":170712,"date":"2025-08-18T07:30:10","date_gmt":"2025-08-18T13:30:10","guid":{"rendered":"https:\/\/tecscience.tec.mx\/en\/?post_type=sciencecommunication&p=170712"},"modified":"2025-08-25T19:01:44","modified_gmt":"2025-08-26T01:01:44","slug":"predictive-growth-model","status":"publish","type":"sciencecommunication","link":"https:\/\/tecscience.tec.mx\/en\/science-communication\/predictive-growth-model\/","title":{"rendered":"AI Detects Childhood Growth Problems Early"},"content":{"rendered":"\n

By <\/em>Mauro Rodr\u00edguez-Mar\u00edn<\/em><\/a> and <\/em>Luis Gustavo Orozco-Alatorre<\/em><\/a><\/p>\n\n\n\n

When Camila was seven years old, her parents noticed she was still the shortest in her class. \u201cIt\u2019s normal, she\u2019ll catch up,\u201d<\/em> people told them. But something didn\u2019t seem right. After months of waiting to see a specialist, they finally received a diagnosis of growth delay\u2014one that could have been detected much earlier.<\/p>\n\n\n\n

This story\u2014common in many households every day\u2014inspired the development of a tool that combines artificial intelligence, open clinical data, and pediatric care.<\/p>\n\n\n\n

Through the study Advances in Pediatric Growth Assessment with Machine Learning: Overcoming the Challenges of Early Diagnosis and Monitoring<\/em>, researchers analyzed a set of biometric and demographic data from public institutions to develop a logistic regression model capable of identifying growth deviations in children with precision, speed, and clarity.<\/p>\n\n\n\n

In Camila\u2019s hypothetical case, where a growth disorder was suspected from age seven but took much longer to confirm, the tool described in the study would have enabled a far quicker diagnosis.<\/p>\n\n\n\n

Predictive Growth Model<\/h2>\n\n\n\n

Logistic regression tools are based on machine learning, and their strength lies in delivering interpretable categorical results\u2014such as the presence or absence of a risk. This makes them easier to integrate into clinical and educational settings and supports earlier detection of potential developmental disorders.<\/p>\n\n\n\n

Because of its ability to identify risk factors at an early stage, this tool is already widely used in medical research.<\/p>\n\n\n\n

In our project, we built on this approach to develop a model that predicts and helps healthcare professionals detect early warning signs of growth-related issues.<\/p>\n\n\n\n

The proposed model can identify deviations in children\u2019s height with 94.65% accuracy<\/strong>. Unlike other methods that predict numerical values, this one produces categorical outcomes (for example, \u201cyes\/no\u201d<\/em> or \u201cpositive\/negative\u201d<\/em>) using variables such as age, weight, height, and patient history.<\/p>\n\n\n\n

It is also explanatory, meaning it can show which of these variables has the strongest influence on the likelihood of a growth deviation.<\/p>\n\n\n\n

Importantly, while the model does not diagnose a specific condition, it generates an alert that is both understandable and clinically useful. It flags cases where a child may fall outside the expected growth range, guiding doctors toward a more detailed evaluation.<\/p>\n\n\n\n

This explanatory approach marks a key difference from other, more complex algorithms. Here, data does not replace medical judgment\u2014it complements it.<\/p>\n\n\n\n

For instance, the tool might detect that a child\u2019s height is below the fifth percentile, considering their age, sex, and population context. That triggers an early alert, which can lead to timely medical consultation.<\/p>\n\n\n\n

Clinical Results<\/h2>\n\n\n\n

Our logistic regression model was trained on a biometric and demographic dataset of over 2,400 children\u2019s records, obtained from the Pediatric Growth Data Set<\/strong> at Stanford Medicine Children\u2019s Health.<\/p>\n\n\n\n

The algorithm achieved a sensitivity of 91.03%<\/strong> (its ability to detect positive cases). The dataset is also available on GitHub, making it easier to apply in different hospitals and educational settings, and ensuring the research can be reproduced.<\/p>\n\n\n\n

The goal of this research project is to support earlier diagnoses, especially in resource-limited settings or in systems where long waiting lists delay access to specialized pediatric care.<\/p>\n\n\n\n

This progress carries both clinical and social implications, since detecting a growth disorder early can mean the difference between effective treatment and a chronic condition.<\/p>\n\n\n\n

Early Diagnosis <\/h2>\n\n\n\n

Child growth follows a predictable sequence, with developmental milestones that generally occur at similar ages across populations. However, many factors can contribute to growth problems in children and adolescents, often stemming from genetic, nutritional, environmental, psychosocial, or hormonal influences.<\/p>\n\n\n\n

For example, short stature in families is usually genetic, while delayed growth and puberty often catch up later. Malnutrition and psychosocial stress can also disrupt the process.<\/p>\n\n\n\n

Other contributors to growth disorders include endocrine imbalances\u2014such as growth hormone deficiency or hypothyroidism\u2014which can cause growth delays, highlighting the need for a holistic diagnostic approach.<\/p>\n\n\n\n

Identifying these causes is essential to implementing effective treatment strategies. Regular follow-ups and adherence to prescribed therapies are crucial for long-term progress (growth phases such as adolescence are critical).<\/p>\n\n\n\n

Reference<\/h5>\n\n\n\n

Rodr\u00edguez-Mar\u00edn, M., y Orozco-Alatorre, LG (2025). <\/em>Avances en la evaluaci\u00f3n del crecimiento pedi\u00e1trico con aprendizaje autom\u00e1tico: Superando los desaf\u00edos del diagn\u00f3stico y la monitorizaci\u00f3n temprana<\/em><\/a>. Children, 12 (3), 317<\/em><\/p>\n\n\n\n

Authors<\/strong><\/h5>\n\n\n\n

Mauro Rodr\u00edguez Mar\u00edn.<\/strong> Professor at the Business School of Tecnol\u00f3gico de Monterrey, Guadalajara campus, where he serves as a faculty member, researcher, and consultant in the Department of Marketing and Analytics. He is a Level C member of the National System of Researchers (SNII) of SECIHTI and he holds a postdoctoral degree in Data Analytics from a joint program between Tec and the University of Texas at San Antonio (UTSA). His research areas include demand planning, artificial intelligence applied to business, healthcare, data analytics, and tourism. He has also been a visiting professor at universities such as Yale (USA) and Jean Moulin Lyon III (France).<\/p>\n\n\n\n

Luis Gustavo Orozco Alatorre.<\/strong> Professor of Pediatrics at the University Center for Health Sciences of the University of Guadalajara and a Level 1 member of the National System of Researchers (SNI). He served as head of the Pediatric Specialty Program at the New Civil Hospital of Guadalajara, \u201cDr. Juan I. Menchaca,\u201d accredited by SECIHTI\u2019s PNPC. With over 33 years of private practice, he is certified by the Mexican Board of Pediatrics. He is a former president of the Jalisco Pediatrics Association, A.C., and a member of the Mexican Academy of Pediatrics, A.C. His research areas include child development and growth, pediatric pathologies, infectious diseases, and perinatal medicine<\/p>\n","protected":false},"excerpt":{"rendered":"

Using biometric and demographic data, researchers have developed a model that can identify growth disorders in children.<\/p>\n","protected":false},"author":18,"featured_media":170781,"menu_order":0,"comment_status":"closed","ping_status":"closed","template":"","format":"standard","meta":{"_acf_changed":false,"_eb_attr":"","footnotes":""},"categories":[86],"tags":[566,224],"class_list":["post-170712","sciencecommunication","type-sciencecommunication","status-publish","format-standard","has-post-thumbnail","hentry","category-health","tag-egade-business-school","tag-health"],"acf":[],"yoast_head":"\nPredictive Growth Model with AI | TecScience<\/title>\n<meta name=\"description\" content=\"Using biometric and demographic data, researchers have developed a model that can identify growth disorders in children.\" \/>\n<meta name=\"robots\" content=\"index, follow, max-snippet:-1, max-image-preview:large, max-video-preview:-1\" \/>\n<link rel=\"canonical\" href=\"https:\/\/tecscience.tec.mx\/en\/science-communication\/predictive-growth-model\/\" \/>\n<meta property=\"og:locale\" content=\"en_US\" \/>\n<meta property=\"og:type\" content=\"article\" \/>\n<meta property=\"og:title\" content=\"AI Detects Childhood Growth Problems Early\" \/>\n<meta property=\"og:description\" content=\"Using biometric and demographic data, researchers have developed a model that can identify growth disorders in children.\" \/>\n<meta property=\"og:url\" content=\"https:\/\/tecscience.tec.mx\/en\/science-communication\/predictive-growth-model\/\" \/>\n<meta property=\"og:site_name\" content=\"TecScience\" \/>\n<meta property=\"article:modified_time\" content=\"2025-08-26T01:01:44+00:00\" \/>\n<meta property=\"og:image\" content=\"https:\/\/tecscience.tec.mx\/en\/wp-content\/uploads\/sites\/9\/2025\/08\/predictive-growth-model-1.jpg\" \/>\n\t<meta property=\"og:image:width\" content=\"750\" \/>\n\t<meta property=\"og:image:height\" content=\"500\" \/>\n\t<meta property=\"og:image:type\" content=\"image\/jpeg\" \/>\n<meta name=\"twitter:card\" content=\"summary_large_image\" \/>\n<meta name=\"twitter:label1\" content=\"Est. reading time\" \/>\n\t<meta name=\"twitter:data1\" content=\"5 minutes\" \/>\n<script type=\"application\/ld+json\" class=\"yoast-schema-graph\">{\"@context\":\"https:\\\/\\\/schema.org\",\"@graph\":[{\"@type\":\"WebPage\",\"@id\":\"https:\\\/\\\/tecscience.tec.mx\\\/en\\\/science-communication\\\/predictive-growth-model\\\/\",\"url\":\"https:\\\/\\\/tecscience.tec.mx\\\/en\\\/science-communication\\\/predictive-growth-model\\\/\",\"name\":\"Predictive Growth Model with AI | TecScience\",\"isPartOf\":{\"@id\":\"https:\\\/\\\/tecscience.tec.mx\\\/en\\\/#website\"},\"primaryImageOfPage\":{\"@id\":\"https:\\\/\\\/tecscience.tec.mx\\\/en\\\/science-communication\\\/predictive-growth-model\\\/#primaryimage\"},\"image\":{\"@id\":\"https:\\\/\\\/tecscience.tec.mx\\\/en\\\/science-communication\\\/predictive-growth-model\\\/#primaryimage\"},\"thumbnailUrl\":\"https:\\\/\\\/tecscience.tec.mx\\\/en\\\/wp-content\\\/uploads\\\/sites\\\/9\\\/2025\\\/08\\\/predictive-growth-model-1.jpg\",\"datePublished\":\"2025-08-18T13:30:10+00:00\",\"dateModified\":\"2025-08-26T01:01:44+00:00\",\"description\":\"Using biometric and demographic data, researchers have developed a model that can identify growth disorders in children.\",\"breadcrumb\":{\"@id\":\"https:\\\/\\\/tecscience.tec.mx\\\/en\\\/science-communication\\\/predictive-growth-model\\\/#breadcrumb\"},\"inLanguage\":\"en-US\",\"potentialAction\":[{\"@type\":\"ReadAction\",\"target\":[\"https:\\\/\\\/tecscience.tec.mx\\\/en\\\/science-communication\\\/predictive-growth-model\\\/\"]}]},{\"@type\":\"ImageObject\",\"inLanguage\":\"en-US\",\"@id\":\"https:\\\/\\\/tecscience.tec.mx\\\/en\\\/science-communication\\\/predictive-growth-model\\\/#primaryimage\",\"url\":\"https:\\\/\\\/tecscience.tec.mx\\\/en\\\/wp-content\\\/uploads\\\/sites\\\/9\\\/2025\\\/08\\\/predictive-growth-model-1.jpg\",\"contentUrl\":\"https:\\\/\\\/tecscience.tec.mx\\\/en\\\/wp-content\\\/uploads\\\/sites\\\/9\\\/2025\\\/08\\\/predictive-growth-model-1.jpg\",\"width\":750,\"height\":500,\"caption\":\"The proposed model can detect deviations in children\u2019s height with 94.65% accuracy. 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