Data · dataset · 2025
Artificial Intelligence in Onco-Andrology: Applications in Fertility Preservation, Sperm Analysis and Post-Treatment Recovery
Listed in Teesside University Research Data Repository
The convergence of artificial intelligence (AI) and onco-andrology constitutes an emerging domain with significant potential to enhance reproductive outcomes in male cancer patients.
Description
Chemotherapy and radiation therapy frequently impair male fertility. Consequently, there is a critical demand for developing effective strategies aimed at the preservation and restoration of reproductive function.
AI-driven tools are being integrated into clinical practice to improve decision-making in fertility preservation, automate and enhance sperm analysis post-treatment and forecast personalised recovery trajectories. This review explores the latest applications of AI in onco-andrology, highlighting advances in machine learning algorithms for personalised fertility preservation protocols, deep learning models for automated sperm quality assessment and predictive analytics to guide post-treatment fertility restoration.
Read the rest (1 more)
By harnessing the capabilities of AI, the field of onco-andrology is poised to enhance patient counselling, optimise therapeutic approaches and markedly elevate the quality of life for cancer survivors facing fertility-related challenges.
Links
Where it is published
- DOI doi.org/10.17632/fmxnbygdgk.1 ↗
DOI / persistent id · from researchdata tees ac uk
Catalogue records · 1
- OAI-PMH record data.mendeley.com/oai?verb=GetRecord&metadataPrefix=oai_dc&identifier=oai%3Adata… ↗
metadata API · from researchdata tees ac uk
Topics
- From keywords
- Artificial intelligence · Computer Science & AI · Deep learning · Earth & Environmental Science · Fertility
- Inferred from text
- Cancer 75%
Provenance · 1 source records, 11 field assertions
| Source | Key | Last seen | Raw |
|---|---|---|---|
| Teesside University Research Data Repository | oai:data.mendeley.com/fmxnbygdgk.1 | 15 h ago | JSON v1 |
| Field | Assertion | Extractor | Evidence |
|---|---|---|---|
| access_level | source · researchdata tees ac uk | connector:researchdata_tees_ac_uk@1.0.0 | |
| concepts[disease].local:disease:cancer | enrichment · researchdata tees ac uk | keyword-concept-rules@1.0.0 | title+description (75%) |
| concepts[field].anzsrc:field:440302 | mapping · researchdata tees ac uk | vocabulary-mapper@1.0.0 | keywords['Fertility'] |
| concepts[field].anzsrc:field:461103 | mapping · researchdata tees ac uk | vocabulary-mapper@1.0.0 | keywords['Deep Learning'] |
| concepts[field].anzsrc:group:4602 | mapping · researchdata tees ac uk | vocabulary-mapper@1.0.0 | keywords['Artificial Intelligence'] |
| concepts[field].local:field:computer-science-ai | mapping · researchdata tees ac uk | connector:researchdata_tees_ac_uk@1.0.0 | |
| concepts[field].local:field:earth-environmental | mapping · researchdata tees ac uk | connector:researchdata_tees_ac_uk@1.0.0 | |
| description | source · researchdata tees ac uk | connector:researchdata_tees_ac_uk@1.0.0 | /metadata/dc/description |
| license | source · researchdata tees ac uk | connector:researchdata_tees_ac_uk@1.0.0 | /metadata/dc/rights |
| publication_date | source · researchdata tees ac uk | connector:researchdata_tees_ac_uk@1.0.0 | |
| title | source · researchdata tees ac uk | connector:researchdata_tees_ac_uk@1.0.0 | /metadata/dc/title |