An automated method for assessing condyle head changes in patients with skeletal class II malocclusion based on CBCT images.

OBJECTIVES: Currently, there is no reliable automated measurement method to study the changes in the condylar process after orthognathic surgery. Therefore, this study proposes an automated method to measure condylar changes in patients with skeletal class II malocclusion following surgical-orthodontic treatment. METHODS: Cone-beam computed tomography (CBCT) scans from 48 patients were segmented using the nnU-Net network for automated maxillary and mandibular delineation. Regions unaffected by orthognathic surgery were selectively cropped. Automated registration yielded condylar displacement and volume calculations, each repeated three times for precision. Logistic regression and Linear regression were used to analyze the correlation between condylar position changes at different time points. RESULTS: The Dice score for the automated segmentation of the condyle was 0.971. The Intraclass correlation coefficients (ICCs) for all repeated measurements ranged from 0.93 to 1.00. The results of the automated measurement showed that 83.33% of patients exhibited condylar resorption occurring six months or more after surgery. Logistic regression and Linear regression indicated a positive correlation between counterclockwise rotation in the Pitch plane and condylar resorption(p < 0.01). And a positive correlation between the rotational angles in both three planes and changes in the condylar volume at six months after surgery(p ≤ 0.04). CONCLUSIONS: This study's automated method for measuring condylar changes shows excellent repeatability. Skeletal class II malocclusion patients may experience condylar resorption after bimaxillary orthognathic surgery, and this is correlated with counterclockwise rotation in the sagittal plane. ADVANCES IN KNOWLEDGE: This study proposes an innovative multi-step registration method based on CBCT, and establishes an automated approach for quantitatively measuring condyle changes post-orthognathic surgery. This method opens up new possibilities for studying condylar morphology.

3D Printing Model of a Patient's Specific Lumbar Vertebra.

Selective dorsal rhizotomy (SDR) is a difficult, risky, and sophisticated operation, in which a laminectomy should not only expose an adequate surgical field of view but also protect the patient's spinal nerves from injury. Digital models play an important role in the pre-and intra-operation of SDR, because they can not only make doctors more familiar with the anatomical structure of the surgical site, but also provide precise surgical navigation coordinates for the manipulator. This study aims to create a 3D digital model of a patient-specific lumbar vertebra that can be used for planning, surgical navigation, and training of the SDR operation. The 3D printing model is also manufactured for more effective work during these processes. Traditional orthopedic digital models rely almost entirely on computed tomography (CT) data, which is less sensitive to soft tissues. Fusion of the bone structure from CT and the neural structure from magnetic resonance imaging (MRI) is the key element for the model reconstruction in this study. The patient's specific 3D digital model is reconstructed for the real appearance of the surgical area and shows the accurate measurement of inter-structural distances and regional segmentation, which can effectively help in the preoperative planning and training of SDR. The transparent bone structure material of the 3D-printed model allows surgeons to clearly distinguish the relative relationship between the spinal nerve and the vertebral plate of the operated segment, enhancing their anatomical understanding and spatial sense of the structure. The advantages of the individualized 3D digital model and its accurate relationship between spinal nerve and bone structures make this method a good choice for preoperative planning of SDR surgery.

Retrospective analysis of the risk factors associated with failure in obtaining effective noninvasive prenatal test results and pregnancy outcomes: a case-control study.

OBJECTIVE: To explore the pregnancy outcomes of women who couldn't obtain effective results from noninvasive prenatal testing (NIPT) and examine the factors leading to test failure. METHODS: From April 2017 to December 2019, 120,041 pregnant women enrolled for voluntary NIPT. The case group comprised of 274 (274/120,041) women who failed to obtain effective NIPT results, and the control group (n = 540) was from the same population who obtained effective NIPT results and matched by age at a 1:2 ratio. Abnormal pregnancy rates between the two groups were analyzed using Chi-square analysis. NIPT failure risk factors were analyzed using logistic regression analysis. RESULTS: Logistic regression analysis showed that increased maternal age (OR = 0.988; 95% CI = 0.982-0.994), increased pregnancy age (OR = 0.989; 95%CI = 0.988-0.991), and decreased cell-free fetal DNA concentration (OR = 1.050; 95%CI = 1.043-1.058) were independent risk factors for NIPT failure. Fifteen cases showed fetus loss in cases of NIPT failure. There was a significant difference in abnormal pregnancy rate between the NIPT success and failure groups (χ2 = 50.943, P < 0.05). EXPERT COMMENTARY: The specific interventions, guidance, and precautions are needed for pregnant women who have no effective NIPT results.