AI 中文总结
针对CBCT报告监督不完整问题，提出证据瓶颈框架EviDent-CBCT，通过离散证据记录和牙科逻辑一致性投影生成报告，在ODIN 2026挑战赛中获自动评估第二、临床比较第三，验证了其有效性与可审计性。
AI 中文摘要
牙颌面锥形束CT（CBCT）报告可能包含来自单次三维扫描的数十项牙齿特异性、解剖学和空间发现。从有限的临床数据中学习生成此类报告具有挑战性，因为常规报告可能无法穷尽记录影像发现，且未提及可能反映缺失或未报告。我们提出EviDent-CBCT，一种专为此类不完整监督设计的证据瓶颈框架。解剖感知网络将每次CBCT扫描映射为离散的牙齿级、全局和牙齿-IAC证据记录。牙科逻辑一致性投影在不兼容证据间进行调和，随后确定性渲染器和图像无关的局部语言模型仅使用该记录生成报告。对于牙齿级证据，可靠性感知训练将符合条件的未提及项作为降低权重的负样本，而未报告的全局和牙齿-IAC标签保持未知。金属敏感输入通道保留牙科材料的强度线索。在三次验证运行中，EviDent-CBCT实现了$0.666\pm0.006$的合并证据集F1分数和$0.402\pm0.003$的RadFact-Lite-Dental逻辑F1分数，而最强受控直接基线的相应分数为$0.371\pm0.018$。在ODIN 2026挑战赛中，它在隐藏测试集上的自动评估中排名第二，在盲法临床Arena比较中排名第三。这些结果支持离散证据记录作为CBCT报告生成的有效且可审计的接口。
英文摘要
Dento-maxillofacial cone-beam CT (CBCT) reports may contain dozens of tooth-specific, anatomical, and spatial findings from a single 3D scan. Learning to generate such reports from limited clinical data is challenging because routine reports may not exhaustively document image findings, and a non-mention may reflect either absence or non-reporting. We present EviDent-CBCT, an evidence-bottlenecked framework designed for this incomplete supervision. An anatomy-aware network maps each CBCT scan to a discrete record of tooth-level, global, and tooth-IAC evidence. A dental-logic consistency projection reconciles incompatible evidence before a deterministic renderer and an image-blind local language model generate the report using only this record. For tooth-level evidence, reliability-aware training uses eligible non-mentions as reduced-weight negatives, while unreported global and tooth-IAC labels remain unknown. A metal-sensitive input channel preserves intensity cues from dental materials. Across three validation runs, EviDent-CBCT achieves $0.666\pm0.006$ merged evidence set-F1 and $0.402\pm0.003$ RadFact-Lite-Dental logical-F1, versus $0.371\pm0.018$ for the strongest controlled direct baseline. In the ODIN 2026 challenge, it ranked second in automated evaluation and third in blinded clinical Arena comparison on the hidden test set. These results support the discrete evidence record as an effective and auditable interface for CBCT report generation.
