Performance of two different artificial intelligence models in dental implant planning among four different implant planning software: a comparative study.
Background: The integration of artificial intelligence (AI) in dental implant planning has emerged as a transformative approach to enhance diagnostic accuracy and efficiency. This study aimed to evaluate the performance of two object detection models, Faster R-CNN and YOLOv7 in analyzing cross-secti...
| Publicado en: | BMC Oral Health Vol. 25; no. 1; pp. 1 - 15 |
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| Autores principales: | , , |
| Formato: | diagnostic images equations & formulas pictorial research tables/charts Journal Article |
| Publicado: |
BioMed Central
7/2/2025
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| Acceso en línea: | Ver este registro en EBSCOhost |
| fields | @attributes: recordID: 1 pdfLink: plink: https://search.ebscohost.com/login.aspx?direct=true&db=ccm&AN=186339239&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 186339239 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 14726831 1CIC jtl: BMC Oral Health issn: 14726831 maglogo: N pubinfo: dt: 7/2/2025 vid: 25 iid: 1 pid: 24147 pub: BioMed Central artinfo: ui: 186339239 186339239 186339239 10.1186/s12903-025-06336-0 186339239 ppf: 1 ppct: 14 formats: tig: atl: Performance of two different artificial intelligence models in dental implant planning among four different implant planning software: a comparative study. aug: au: Roongruangsilp, Pathompong Narkbuakaew, Walita Khongkhunthian, Pathawee affil: https://ror.org/05m2fqn25 Center of Excellence for Dental Implantology, Faculty of Dentistry, Chiang Mai University, Chiang Mai, Thailand sug: subj: Artificial Intelligence Dental Implants Dental Implantation Software Design Image Processing, Computer Assisted Methods Planning Techniques Deep Learning Methods Human Machine Learning Methods Comparative Studies Descriptive Statistics Funding Source ab: Background: The integration of artificial intelligence (AI) in dental implant planning has emerged as a transformative approach to enhance diagnostic accuracy and efficiency. This study aimed to evaluate the performance of two object detection models, Faster R-CNN and YOLOv7 in analyzing cross-sectional and panoramic images derived from DICOM files processed by four distinct dental imaging software platforms. Methods: The dataset consisted of 332 implant position images derived from DICOM files of 184 CBCT scans. Three hundred images were processed using DentiPlan Pro 3.7 software (NECTEC, NSTDA, Thailand) for the development of Faster R-CNN and YOLOv7 models for dental implant planning. For model testing, 32 additional implant position images, which were not included in the training set, were processed using four different software programs: DentiPlan Pro 3.7, DentiPlan Pro Plus 5.0 (DTP; NECTEC, NSTDA, Thailand), Implastation (ProDigiDent USA, USA), and Romexis 6.0 (Planmeca, Finland). The performance of the models was evaluated using detection rate, accuracy, precision, recall, F1 score, and the Jaccard Index (JI). Results: Faster R-CNN achieved superior accuracy across imaging modalities, while YOLOv7 demonstrated higher detection rates, albeit with lower precision. The impact of image rendering algorithms on model performance underscores the need for standardized preprocessing pipelines. Although Faster R-CNN demonstrated relatively higher performance metrics, statistical analysis revealed no significant differences between the models (p-value > 0.05). Conclusions: This study emphasizes the potential of AI-driven solutions in dental implant planning and advocates the need for further research in this area. The absence of statistically significant differences between Faster R-CNN and YOLOv7 suggests that both models can be effectively utilized, depending on the specific requirements for accuracy or detection. Furthermore, the variations in imaging rendering algorithms across different software platforms significantly influenced the model outcomes. AI models for DICOM analysis should rely on standardized image rendering to ensure consistent performance. pubtype: Academic Journal doctype: diagnostic images equations & formulas pictorial research tables/charts Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
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