Related Experiment Video For early detection of cancer
Updated: May 2, 2026
Artificial Intelligence Approaches to Assessing Primary Cilia
Published on: May 1, 2021
Use of artificial intelligence in the diagnosis of alterations in cervical cytology: A university population-based
Jose Said Manzano-Chaya1, Tania Mendoza-Herrera1, Ernesto García-Ayala1
1Grupo de Investigación en Patología Estructural, Funcional y Clínica, Departamento de Patología, Universidad Industrial de Santander, Bucaramanga, Colombia.
Introduction:
Conventional cervical cytology (Pap smear) remains a primary method for cervical cancer screening in Colombia, despite limitations in diagnostic yield and heavy workload. The potential of artificial intelligence to address these challenges is yet to be evaluated in our population.
Objective:
To evaluate and compare the discriminative ability of four artificial intelligence-based models for the detection of abnormalities in Pap smears. Materials and methods. A total of 650 images of Pap smear cells were obtained from a university cohort in northeastern Colombia. These images were subjected to diagnostic evaluation by an expert pathologist. Four artificial intelligence models (DenseNet, InceptionV3, MobileNet, and VGG19) were trained using data from a publicly available Pap smear database with digital image analysis and deep learning. The discriminative ability of the models was determined by calculating their sensitivity, specificity, and area under the curve.
Materials And Methods:
MobileNet tuvo la mejor capacidad discriminativa [área bajo la curva (AUC) de 0,97) con una especificidad del 0,99 y sensibilidad de 0,78 para la detección de alteraciones en la citología cervicouterina. Por otro lado, InceptionV3 tuvo un mejor desempeño en el tamizaje, con sensibilidad del 0,93, especificidad de 0,82 y área bajo la curva de 0,947.
Results:
MobileNet showed the highest discriminative ability (AUC = 0.97), with a specificity of 0.99 and sensitivity of 0.78 for the detection of altered cells in Pap smears. On the other hand, InceptionV3 had the best performance capabilities for screening, with a sensitivity of 0.93, specificity of 0.82, and AUC of 0.947.
Conclusions:
The results of this study illustrate the advantages and disadvantages of different artificial intelligence models and how their application could help improve the diagnostic performance of manual reading in cervical cancer screening or even serve as a primary screening method to rule out negative cases, by achieving a diagnostic performance comparable to that of manual reading.
