Cephalometric analysis has undergone a progressive evolution since Broadbent’s introduction of 2D lateral cephalograms, which provided the foundation for orthodontic diagnosis and treatment planning [1]. Artificial intelligence (AI) has revolutionized numerous fields, including medicine and dentistry, by enhancing precision, efficiency, and reproducibility in clinical and research workflows [[1], [2], [3]]. In craniofacial analysis, AI-assisted semi-automated tools integrated into imaging software enable automated identification and assessment of anatomical landmarks, significantly reducing manual effort and potential errors [[4], [5], [6]].

Cone-beam computed tomography (CBCT), a key imaging modality in orthodontics and craniofacial surgery, has greatly benefited from such technological advances [[7], [8], [9]]. The InVivo7 software (Anatomage, San Jose, CA, USA) applies a rule-based automated algorithm to standardize the identification and analysis of craniofacial landmarks in CBCT scans [3,4,[10], [11], [12], [13]]. This system extends traditional cephalometric frameworks into three dimensions, allowing a more comprehensive evaluation of craniofacial morphology [4,5]. This software employs advanced AI algorithms that, when combined with the expertise of experienced orthodontists, can recognize key points with high precision and efficiency [14,15].

Several approaches for performing 3D cephalometric measurements have since been proposed, including direct landmarking on volumetric reconstructions, surface-based analyses, and voxel-based registration techniques [[16], [17], [18], [19]]. While traditional 2D cephalometry remains widely adopted because of its simplicity, reduced radiation exposure, and well-established normative references, 3D cephalometry offers superior anatomical accuracy, the elimination of superimposition errors, and improved visualization of asymmetries [4,16,18,31]. Despite these advantages, important challenges persist, such as the variability of datasets, the reliability of soft-tissue landmark identification, and the absence of universally standardized protocols and normative databases [6,18,20,23].

Three-dimensional cephalometric analysis has emerged as an important advancement in modern orthodontics, offering notable advantages over conventional 2D methods. Unlike lateral cephalograms, which are limited by magnification errors, distortions, and superimpositions, CBCT-based 3D cephalometry enables the simultaneous evaluation of skeletal, dental, and soft-tissue structures in all spatial planes [4,16,18,23]. Several studies have demonstrated that 3D measurements provide improved reliability and reproducibility, particularly for complex cases involving asymmetries or high morphological variability [7,9,18,23,31]. By integrating automated landmark detection with 3D cephalometric principles, InVivo7 represents a clinically relevant tool for standardized assessment of craniofacial morphology, enhancing diagnostic accuracy and streamlining treatment planning [12,31].

This article evaluates the functionality of the InVivo7 3D imaging software as a standardized semi-automated tool for identifying craniofacial landmarks in CBCT scans.