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Treatment Model for Young Patients with Psychogenic Erectile Dysfunction and Resultant Infertility

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Presenting a conceptual model for decision support systems in infertility: A developmental study.

Hasan Sajjadi¹, Hamid Choobineh^2,3, Reza Safdari¹

¹Department of Health Information Management and Medical Informatics, School of Allied Medical Sciences, Tehran University of Medical Sciences, Tehran, Iran.

International Journal of Reproductive Biomedicine

|December 24, 2025

Summary

This study developed a conceptual model for infertility decision support systems. The model, based on expert input and data analysis, aims to improve diagnosis and treatment accuracy for infertility patients.

Keywords:

Artificial intelligence.Infertility Intelligent systems Expert systems

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Area of Science:

Reproductive Medicine
Medical Informatics
Health Systems Engineering

Background:

Infertility affects individuals trying to conceive for over a year, leading to unintentional childlessness.
Clinical decision-support systems (CDSS) offer potential to improve infertility diagnosis, reduce costs, and enhance treatment accuracy.

Purpose of the Study:

To present a conceptual model for developing decision support systems specifically for infertility care.
To integrate data from literature, existing systems, and expert opinions into a cohesive model.

Main Methods:

A three-step developmental study involving literature review (PubMed, Scopus, Web of Science), identification of existing systems (Google, Yahoo, Bing, AI tools), and expert surveys (32 specialists).
Expert opinions were gathered using a researcher-designed questionnaire, with reliability (Cronbach's alpha=0.78) and validity (CVR=0.60) established.
Data analysis included systematic literature searches, competitor analysis of similar systems, and statistical analysis of expert consensus.

Main Results:

Literature and system searches identified 10 and 13 relevant systems, respectively.
Expert consensus reached high agreement (82.5%-94%) on key data requirements for infertility management, including monitoring, sperm analysis, and abortion data.
The conceptual model, encompassing main features, patient information management, fertility prediction, and secondary features, achieved 85% overall expert agreement.

Conclusions:

A decision support system for infertility holds promise for advancing clinical care and patient outcomes.
Key challenges for implementation include standardizing validation methodologies and ensuring consideration of ethnic diversity in system design.