Artificial intelligence is rapidly transforming pediatric craniofacial surgery by enhancing diagnostic accuracy, improving surgical precision, and optimizing postoperative care. Machine learning and deep learning models are increasingly used to analyze complex craniofacial imaging, enabling early detection of congenital anomalies such as craniosynostosis, and cleft lip and palate. AI-driven algorithms assist in preoperative planning by identifying anatomical abnormalities, predicting surgical outcomes, and guiding personalized treatment strategies. In cleft lip and palate care, AI enhances prenatal detection, severity classification, and the design of custom therapeutic devices, while also refining speech evaluation. For craniosynostosis, AI supports automated morphology classification, severity scoring, and the assessment of surgical indications, thereby promoting diagnostic consistency and predictive outcome modeling. In orthognathic surgery, AI-driven analyses, including skeletal maturity evaluation and cephalometric assessment, inform optimal timing and diagnosis. Furthermore, in cases of craniofacial microsomia and microtia, AI improves phenotypic classification and surgical planning through precise intraoperative navigation. These advancements underscore AI’s transformative role in diagnostic accuracy, and clinical decision-making, highlighting its potential to significantly enhance evidence-based pediatric craniofacial care.

Background/Objectives: The evolving capabilities of large language models, such as generative pre-trained transformers (ChatGPT), offer new avenues for disseminating health information online. These models, trained on extensive datasets, are designed to deliver customized responses to user queries. However, as these outputs are unsupervised, understanding their quality and accuracy is essential to gauge their reliability for potential applications in healthcare. This study evaluates responses generated by ChatGPT addressing common patient concerns and questions about cleft lip repair. Methods: Ten commonly asked questions about cleft lip repair procedures were selected from the American Society of Plastic Surgeons’ patient information resources. These questions were input as ChatGPT prompts and five board-certified plastic surgeons assessed the generated responses on quality of content, clarity, relevance, and trustworthiness, using a 4-point Likert scale. Readability was evaluated using the Flesch reading ease score (FRES) and the Flesch–Kincaid grade level (FKGL). Results: ChatGPT responses scored an aggregated mean rating of 2.9 out of 4 across all evaluation criteria. Clarity and content quality received the highest ratings (3.1 ± 0.6), while trustworthiness had the lowest rating (2.7 ± 0.6). Readability metrics revealed a mean FRES of 44.35 and a FKGL of 10.87, corresponding to approximately a 10th-grade literacy standard. None of the responses contained grossly inaccurate or potentially harmful medical information but lacked citations. Conclusions: ChatGPT demonstrates potential as a supplementary tool for patient education in cleft lip management by delivering generally accurate, relevant, and understandable information. Despite the value that AI-powered tools can provide to clinicians and patients, the lack of human oversight underscores the importance of user awareness regarding its limitations.

Objective: Post-surgical lip symmetry assessment is a key indicator of cleft repair success. Traditional methods rely on distances between anatomical landmarks, which are impractical for video analysis and overlook texture and appearance. We propose an artificial intelligence (AI) approach to automate this process, analyzing lateral lip morphology for a quantitative symmetry evaluation. Design: We utilize contrastive learning to quantify lip symmetry by measuring the similarity between the representations of the sides, which is subsequently used to classify the severity of asymmetry. Our model does not require patient images for training. Instead, we introduce dissimilarities in face images from open datasets using two methods: temporal misalignment for video frames and face transformations to simulate lip asymmetry observed in the target population. The model differentiates the left and right image representations to assess asymmetry. We evaluated our model on 146 images of patients with repaired cleft lip. Results: The deep learning model trained with face transformations categorized patient images into five asymmetry levels, achieving a weighted accuracy of 75% and a Pearson correlation of 0.31 with medical expert human evaluations. The model utilizing temporal misalignment achieved a weighted accuracy of 69% and a Pearson correlation of 0.27 for the same classification task. Conclusions: We propose an automated approach for assessing lip asymmetry in patients with repaired cleft lip by transforming facial images of control subjects to train a deep learning model, eliminating manual anatomical landmarks. Our promising results provide a more efficient and objective tool for evaluating surgical outcomes.

Alveolar bone grafting (ABG) in bilateral cleft lip and palate (BCLP) patients provides a reconstructive challenge. We present a novel technique of combining autologous iliac crest bone graft (ICBG) with recombinant human bone morphogenic protein 2 (rhBMP-2) and cellular bone matrix (CBM) for ABG in BCLP patients. Complete bone fill occurred in 90% of patients, with 100% having bilateral canine eruption. No patients required repeat ABG, and no significant complications were reported. The alveolar cleft gap volume significantly decreased with an improvement of 75.87%. ABG with autologous ICBG with rhBMP-2 and CBM is an effective technique for patients with BCLP.

Complete congenital arhinia is a rare defect of embryogenesis leading to the absence of the external nose and airway. We report our novel multistaged reconstructive approach and literature review. Nasal methyl methacrylate prosthesis was created from a stereolithographic model for use as a temporary prosthesis and tissue expander. Lefort 1 with cannulization was utilized for midface advancement and airway formation. External framework was reconstructed with bilateral conchal bowl cartilage and rib osteocartilagenous grafts. Patient was pleased with the aesthetics and had safe decannulation with the ability to breathe through the nose and airway.