Publicação
Development and design of implant success prediction tool: AI risk assessment for peri-implant disease prevention
| dc.contributor.author | Bornes, Rita | |
| dc.contributor.author | Montero, Javier | |
| dc.contributor.author | Rosa, Nuno | |
| dc.contributor.author | Fonseca, Patrícia | |
| dc.contributor.author | Correia, André | |
| dc.date.accessioned | 2026-07-01T14:57:28Z | |
| dc.date.available | 2026-07-01T14:57:28Z | |
| dc.date.issued | 2026-04-27 | |
| dc.description.abstract | Peri-implant diseases remain a major cause of late implant failure, and current risk assessment tools show limited capacity to integrate prosthetic factors, salivary biomarkers and artificial intelligence-based prediction. This study aimed to develop the Implant Success Prediction Tool (ISPT), a multifactorial peri-implant risk stratification system structurally designed for modular integration with artificial neural networks and salivary omics data. ISPT development followed three main pillars: (1) incorporation of Implant Disease Risk Assessment (IDRA)-validated clinical vectors, including bleeding on probing percentage, number of sites with probing depth ≥ 5 mm, bone loss in relation to age, periodontitis susceptibility, supportive periodontal therapy and hygiene/compliance parameters; (2) qualitative usability testing of IDRA by implantologists, who identified elements to maintain, clarify or expand; and (3) alignment with a precision medicine framework, establishing collaboration with a salivary diagnostics laboratory SalivaTec (https://ciis.ucp.pt/salivatec) to enable systematic saliva collection and future deep phenotyping. The final ISPT structure comprises ten standardized risk vectors displayed in a colour-coded radial traffic-light diagram, integrating six adapted IDRA-derived vectors and four novel vectors: abutment height/angulation; saliva collection/deep phenotyping vector (“salivaomics”); foreign bodies, titanium particles and tribocorrosion; and other for occlusal loading and functional risk. The tool is conceptually prepared to function as a structured input matrix for artificial neural networks, supporting longitudinal training with combined clinical and salivary data to predict implant outcomes (peri-implant health, mucositis, peri-implantitis) over a minimum 5-year monitoring period. ISPT represents the first peri-implant risk assessment tool explicitly designed for modular integration of artificial intelligence and salivary omics data within a precision dentistry framework. Its standardised vectors, traffic-light visualisation and longitudinal validation methodology provide a scalable structure for future externally validated predictive models of implant success and failure. | eng |
| dc.identifier.doi | 10.1007/s41894-026-00177-y | |
| dc.identifier.other | 04b0b578-343c-4b3a-8c25-a567900244cb | |
| dc.identifier.uri | http://hdl.handle.net/10400.14/58406 | |
| dc.language.iso | eng | |
| dc.peerreviewed | yes | |
| dc.publisher | Springer Nature | |
| dc.rights.uri | http://creativecommons.org/licenses/by/4.0/ | |
| dc.subject | Dental implants | eng |
| dc.subject | Peri-implantitis | eng |
| dc.subject | Artificial intelligence | eng |
| dc.subject | Risk assessment | eng |
| dc.subject | Precision dentistry | eng |
| dc.subject | Salivary biomarkers | eng |
| dc.title | Development and design of implant success prediction tool: AI risk assessment for peri-implant disease prevention | |
| dc.type | research article | |
| dspace.entity.type | Publication | |
| oaire.citation.issue | 1 | |
| oaire.citation.volume | 10 | |
| oaire.version | http://purl.org/coar/version/c_970fb48d4fbd8a85 |
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