Comparison of cone beam computed tomography measurements obtained after manual and AI-assisted head positioning and reconstruction
Year: 2026
Type: article
Abstract: Objectives To compare measurements obtained from cone beam computed tomography (CBCT) exams by radiologists following manual head positioning and panoramic curve tracing with those obtained after artificial intelligence (AI)-assisted positioning adjustments performed prior to measurement. Materials and methods Twenty-five CBCT exams presenting edentulous sites corresponding to missing mandibular first molars were selected. Five examiners performed measurements of bone height and width after manual head positioning adjustments and panoramic curve tracing (manual method). Subsequently, the same exams were processed using TomoNav software, which automatically performed volume alignment and generated the panoramic curve and cross-sectional images. The examiners then repeated the measurements (AI-assisted method). Measurements obtained from both approaches were summarized using descriptive statistics and compared using the intraclass correlation coefficient (ICC), paired t-test, and Bland–Altman analysis (p˂0.05). Results Excellent inter-examiner reproducibility was observed for both height and width measurements, with ICC values of 0.94 for height and 0.81 and 0.85 for width using the manual and AI-assisted methods, respectively. Significant differences were identified for left-side height, total height, right-side width, and total width ( p < 0.05). However, the mean biases were low and overall agreement between methods was excellent, with ICC values equal or greater than 0.89. Conclusion Measurements obtained with the AI-assisted method for volume repositioning and generation of panoramic and cross-sectional images were comparable to those obtained using a conventional method. Clinical relevance AI facilitates CBCT implant-planning preprocessing without compromising measurement reproducibility.
Source: Clinical Oral Investigations
Language: English
Subfield: Oral Surgery
Field: Dentistry
Domain: Health Sciences
Open Access status: hybrid
Estimated APC paid: $4,790
OpenAlex ID: W7225792433