A study on the prediction of male semen quality and fertility assessment based on a composite machine learning algorithm and a self-developed sperm image recognition system (Johnsen-FAST) in a complex factor environment

A study on the prediction of male semen based on a composite machine learning algorithm and a self-developed sperm image recognition system (Johnsen-FAST) in a complex factor environment

Status

Recruiting

Phases

Early Phase 1

Study type

Observational

Source

ChiCTR

Registry ID

ChiCTR2500101473

Enrollment

Unknown

Registered

2025-04-25

Start date

2024-07-18

Completion date

Unknown

Last updated

2025-04-28

For informational purposes only — not medical advice. Sourced from public registries and may not reflect the latest updates. Terms

Conditions

Diseases of the male reproductive system

Interventions

Observation group:None

Eligibility

Sex/Gender

Male

Inclusion criteria

Inclusion criteria: (i) semen examination was performed and male fertility assessment was completed; (ii) patients gave informed consent and cooperated with the activities related to this study; and (iii) cooperated in completing the follow-up programme as scheduled.

Exclusion criteria

Exclusion criteria: (i) previous history of genitourinary trauma or surgery; (ii) combination of malignant tumours; (iii) unwillingness to participate in this study or mental consciousness disorder or cognitive dysfunction; (iv) other unpredictable exacerbation of the condition midway through the course of the study or the occurrence of any other serious illness.

Design outcomes

Primary

Measure	Time frame
Routine semen;Spermatozoa morphology;Sperm DNA fragmentation;	—

Countries

China

Contacts

Public ContactDongsheng Ma

The People's Hospital Bozhou

madongsheng_med@163.com+86 181 4077 8864

Outcome results

None listed

Source: ChiCTR (via WHO ICTRP) · Data processed: Feb 4, 2026