Odontomas are the most common odontogenic tumors (1–4). Odontomas had a marked predilection for the mandible and for the anterior region of the jaws, particularly for the anterior maxilla (1). These benign lesions often impede permanent tooth eruption and cause malocclusion, particularly in pediatric and adolescent patients (5). Conventional surgical enucleation—while effective—carries risks of iatrogenic damage to adjacent dental structures, neurosensory impairment, and prolonged healing in anatomically complex regions like the anterior mandible (6–8).

Recent advancements in robotic-assisted surgery have demonstrated significant potential for improving procedural accuracy in oral and maxillofacial procedures (9, 10). Contemporary autonomous robotic systems achieve submillimeter precision in osteotomy execution, provide real-time three-dimensional navigation, and facilitate minimally invasive approaches, thereby potentially minimizing iatrogenic tissue damage (11, 12). However, the current evidence base remains insufficient to fully evaluate the efficacy of robotic systems for complex dentoalveolar pathology management, particularly in cases requiring odontoma resection. This evidence gap is most pronounced regarding longitudinal outcomes and pediatric applications (13, 14).

This report details a case involving a pediatric patient diagnosed with a compound odontoma situated adjacent to the unerupted mandibular permanent incisor (#31). The lesion was managed via autonomous robotic enucleation employing an autonomous robotic guidance system developed in-house by our research group. The procedure integrated cone-beam computed tomography (CBCT)-based preoperative planning, dynamic surgical pathway mapping, and tissue-sparing osteotomy. Primary emphases encompassed: (1) optimization of minimal bone removal to facilitate eruption potential, (2) validation of robotic precision through postoperative CBCT assessment, and (3) evaluation of early-stage healing responses. Our objectives were to demonstrate the feasibility of robotic surgery for complex dentoalveolar pathologies and to catalyze discourse on technological synergies in precision-based oral healthcare.

The patient requested enucleation of the odontoma in the anterior mandibular region. 1.2

Medical History:

31 teeth was completely bone-impacted, with compound odontoma adjacent to tooth 31. 3

Intervention-Robot-assisted surgical planning design: 3.1

CBCT data segmentation (Figures 2A,B);

For robotic surgical planning, intraoral scans of the labial vestibule, teeth #36–46, and palatal mucosa were acquired using an intraoral scanner (CS 3600; Carestream Dental) and exported in STL format. Concurrently, CBCT data captured in DICOM format were processed through HiRes3D software (LargeV Instrument Corp.) to delineate the odontoma's spatial relationship with adjacent anatomy. These datasets underwent fusion to generate a 3D surgical field model for robotic navigation design (Figures 2C,D). 3.3

Minimally invasive bone removal and odontoma segmentation pathway planning.

The robotic arm was manually positioned near the surgical field. A custom 3D-printed accessory, equipped with optical markers and registration holes, was secured intraorally using dentition support. Spatial registration was achieved by probing five fiducial markers, establishing real-time navigation via infrared tracking between the robotic instrument and the surgical site (Figure 3B). 4.3

Robot-assisted precise alveolar bone removal.

Surgical management of compound odontomas in the anterior mandibular region remains clinically challenging due to intricate anatomical constraints (1–4). Conventional enucleation techniques, reliant on surgeon dexterity and spatial perception, frequently encounter difficulties in achieving complete lesion removal while preserving adjacent critical structures. Risks of iatrogenic injury to developing tooth follicles, neurovascular bundles, and thin buccal cortices are well-documented concerns (6–8). In contrast, robotic-assisted systems overcome these limitations through three-dimensional visualization and sub-millimeter motion control. This case demonstrates how preoperative CBCT data integration enables precise osteotomy pathway planning around the odontoma's periphery—effectively minimizing collateral tissue disruption while ensuring complete encapsulation removal—a critical advantage in anatomically complex pediatric cases (9, 10).

The paradigm of minimally invasive surgery is fundamentally redefined through robotic precision (15, 16). Traditional approaches often necessitate extensive bone removal and mucoperiosteal reflection to gain adequate operational visibility, resulting in prolonged healing and compromised functional outcomes. Robotic systems, however, allow for lesion-specific targeted osteotomy without compromising structural integrity. This case exemplifies significant soft tissue preservation with minimal incision lengths and negligible periosteal elevation. Postoperative assessment revealed accelerated mucosal healing compared to conventional benchmarks, with no neurosensory complications. Such tissue-sparing capabilities align optimally with the pathological characteristics of encapsulated odontomas, establishing a new standard for conservative dentoalveolar surgery.

Recent digital advances have popularized surgical guides and navigation systems for impacted tooth removal, demonstrating significant advantages in precise localization (17, 18). However, while these technologies markedly reduce localization time, they remain unable to facilitate complete tooth disimpaction (19). Substantial surgical effort is still required to overcome osseous and dental resistance along the extraction path. Our conventional surgery cohort data revealed that this disimpaction phase exceeded localization time and predominated in overall procedure duration. This persistent limitation may increase surgical trauma and prolong operative time even with navigational assistance, ultimately compromising procedural precision.

Our self-developed robotic system fundamentally advances compound odontoma management by resolving the critical limitation of current digital approaches (20)—while traditional surgical guides and navigation systems excel at lesion localization, they fail to achieve the aim of conservative osteotomy which constitutes the most time-consuming and technically challenging aspect of conventional surgery. The robotic solution integrates three paradigm-shifting capabilities: (1) Preoperative virtual resistance modeling that algorithmically calculates all bone interference zones along the optimal enucleation path, enabling ultra-conservative osteotomy planning tailored to the lesion's 3D morphology; (2) Autonomous execution via a specialized high-speed end-effector performing complex bone removal in anatomically confined spaces inaccessible to conventional instruments, dramatically accelerating the bone-resistance elimination process; (3) Quantitative surgical efficacy evaluation through osteotomy volume-to-odontoma volume ratios, eliminating subjective surgical field assessments that overlook lesion size variability.

Contrary to navigated surgery, which primarily offers spatial guidance without direct intervention, our autonomous robotic system achieves a high degree of procedural autonomy during critical phases. Specifically, the robotic arm autonomously executes the preplanned osteotomy pathway—including bone milling with a 1.2 mm fissure bur—under real-time optical navigation supervision. This is exemplified by its ability to autonomously halt drilling at predefined endpoints and retract upon command, as demonstrated in the bone removal phase where it completed osteotomy within 5 min without manual instrument manipulation. Such autonomy reduces human error and tremor, while quantitative postoperative CBCT analysis confirmed submillimeter accuracy, distinguishing it from passive navigation tools that still require surgeon-controlled execution.

This integrated approach significantly reduces iatrogenic risks to developing follicles and neurovascular bundles while preserving maximal bone stock—advantages objectively verified by postoperative volumetric analyses showing substantially minimized tissue sacrifice. Crucially, the system allows surgeons to oversee continuous, tremor-free milling along prevalidated paths through simple foot-pedal control rather than manual instrument manipulation. This transforms odontoma enucleation from a technique-sensitive, experience-dependent procedure into a reproducible precision intervention where the robot executes the physiologically taxing bone-removal phase with submillimeter fidelity. The resultant reduction in operative duration, operator fatigue, and anatomical disruption establishes a new gold standard for managing deeply impacted odontomas in pediatric patients. Our autonomous robotic system has undergone rigorous validation through the registered clinical trial where we established a standardized clinical pathway encompassing: (1) CBCT/intraoral scan fusion for virtual planning, (2) optical-navigated robotic execution demonstrated in our surgical workflow, and (3) quantitative outcome assessment protocols. Based on current regulatory review progres, the robotic surgical system is projected for expanded clinical deployment, with phased implementation across multiple medical institutions.

This case substantiates three core advantages of robotic-assisted enucleation: (1) Critical Structure Safeguarding through tremor filtration and motion constraint algorithms preventing incidental contact with follicles and nerves; (2) Programmatic Precision Execution translating virtual plans into physical operations with sub-millimeter fidelity; and (3) Predictability Enhancement verified by postoperative CBCT confirmation of complete lesion removal and bone preservation. The integration of artificial intelligence heralds transformative potential—next-generation systems could utilize real-time optical navigation feedback to dynamically adjust osteotomy paths during drilling, while predictive algorithms may forecast eruption probabilities based on follicular biomechanics. Such synergistic “precision intelligence” platforms promise to transition odontoma management from reactive intervention to biologically optimized preservation therapy.