AI Helps Escom Improve Kidney Cancer Diagnosis

Reporter: Enrique Soto / Photographer: Jorge Aguilar – August 28, 2026

Students and professors develop a deep learning-based tool to detect suspicious kidney masses

Students and professors at the IPN are developing an Artificial Intelligence (AI) tool based on deep learning and artificial neural networks to detect suspicious kidney masses that may be cancerous, helping streamline the diagnostic process and reduce the time required for evaluation by medical specialists.

María de Jesús Ángeles Escamilla, a Data Science undergraduate student, and Diana Valeria Martínez Bribiesca and Ethan Axel Yáñez Torres, undergraduate students in Artificial Intelligence Engineering, all from Escom, are developing the project “Identification of Renal Masses in Computed Tomography Scans Using Deep Learning,” under the guidance of professors Benjamín Luna Benoso and Alberto Jesús Alcántara Méndez.

María de Jesús Ángeles explained that the project was inspired by a personal experience involving her mother: “When I started college, my mother was diagnosed with kidney cancer. Although we all know about this disease, we do not fully understand what it means until someone close to us is affected by it.”

From her field of study, the young Polytechnic student conducted research on the disease. “That is when I came up with the idea of developing a project that could help people affected by the disease. I found that certain AI techniques are already being used to diagnose skin and eye cancer.”

She explained that, unlike other types of cancer, kidney cancer is not always treated with chemotherapy or radiotherapy because these treatments have an effectiveness rate of less than five percent. “Therefore, surgery is the only viable option, which makes it extremely important to identify the kidney mass accurately,” she noted.

“Using abdominal computed tomography scans, which include the kidneys, and with the renal masses identified by specialists, we set out to train a neural network—an Artificial Intelligence tool—to learn how to identify these masses more quickly, which will help optimize diagnostic times,” she said.

She added that the CT scans contain different intensity levels and that the neural network learns to identify these patterns, distinguishing normal from abnormal tissue and determining the size, location, and shape of the mass.

Diana Valeria Martínez explained that this technological tool can generate a three-dimensional model of the organ and the tissue comprising the mass inside the kidney. She reported that in 2022, Mexico recorded 6,427 new cases of kidney cancer and 3,379 deaths from the disease.

“For my capstone project,” she said, “I wanted to develop something with a social impact. Supporting the diagnosis of health problems through new technologies gave us a new meaning to what we are studying.”

Meanwhile, Ethan Axel Yáñez emphasized that symptoms of kidney cancer tend to appear later than those of other types of cancer, which means that in many cases the disease is already at an advanced stage when it is detected.

He stressed the importance of using Artificial Intelligence resources where they can make a meaningful difference, noting that in many cases the technology is used primarily for entertainment, representing an underused potential. “We have to harness the power of computing and AI for projects that have an impact on people’s lives,” he said.

The young Polytechnic students emphasized that the tool developed through this project does not replace medical judgment. Instead, it provides objective delineations that can serve as a basis for specialized clinical analysis.

Escom professors Benjamín Luna and Alberto Jesús Alcántara agreed that young people developing innovative projects are an example of how students can bring fresh perspectives to their field of study while contributing to the reputation and prestige of the institution.