Comparative assessment of orthodontic and aesthetic outcomes after orthognathic surgery with clear aligner or fixed appliance therapy.

BACKGROUND: Clear aligner therapy has an aesthetic advantage over fixed appliance therapy. However, to our knowledge, no study has objectively compared patient orthodontic and aesthetic outcomes between clear aligner and fixed appliance therapies administered after orthognathic surgery (OGS). METHODS: This study included patients with no history of congenital craniofacial deformities who underwent surgery-first OGS and received clear aligner or fixed appliance therapy. The patients' grades on the Dental Health Component (DHC) and Aesthetic Component (AC) of the Index of Orthodontic Treatment Need and scores on the Peer Assessment Rating (PAR) index were calculated before OGS (T0), after OGS (T1), and after orthodontic therapy (T2). RESULTS: This study included 33 patients (clear aligner therapy, 19; fixed appliance therapy, 14). No considerable between-group differences were noted in the DHC and AC grades at T0, T1, or T2. Furthermore, %reduction in the PAR index score was more significant in the clear aligner group (74.4%) than in the fixed appliance group (63.2%) from T0 to T1 (p = .035); however, no between-group differences were noted from T1 to T2 or from T0 to T2. Both groups exhibited substantially improved DHC grades, AC grades, and PAR index scores at T1 and T2. CONCLUSIONS: Patient outcomes were similar between the clear aligner and fixed appliance groups after orthodontic therapy. However, the former group exhibited more favorable immediate results after OGS than did the latter group. Thus, as an adjunct therapy for patients with malocclusion, clear aligner therapy may be more effective than fixed appliance therapy.

Quantization of extraoral free flap monitoring for venous congestion with deep learning integrated iOS applications on smartphones: a diagnostic study.

BACKGROUND: Free flap monitoring is essential for postmicrosurgical management and outcomes but traditionally relies on human observers; the process is subjective and qualitative and imposes a heavy burden on staffing. To scientifically monitor and quantify the condition of free flaps in a clinical scenario, we developed and validated a successful clinical transitional deep learning (DL) model integrated application. MATERIAL AND METHODS: Patients from a single microsurgical intensive care unit between 1 April 2021 and 31 March 2022, were retrospectively analyzed for DL model development, validation, clinical transition, and quantification of free flap monitoring. An iOS application that predicted the probability of flap congestion based on computer vision was developed. The application calculated probability distribution that indicates the flap congestion risks. Accuracy, discrimination, and calibration tests were assessed for model performance evaluations. RESULTS: From a total of 1761 photographs of 642 patients, 122 patients were included during the clinical application period. Development (photographs =328), external validation (photographs =512), and clinical application (photographs =921) cohorts were assigned to corresponding time periods. The performance measurements of the DL model indicate a 92.2% training and a 92.3% validation accuracy. The discrimination (area under the receiver operating characteristic curve) was 0.99 (95% CI: 0.98-1.0) during internal validation and 0.98 (95% CI: 0.97-0.99) under external validation. Among clinical application periods, the application demonstrates 95.3% accuracy, 95.2% sensitivity, and 95.3% specificity. The probabilities of flap congestion were significantly higher in the congested group than in the normal group (78.3 (17.1)% versus 13.2 (18.1)%; 0.8%; 95% CI, P <0.001). CONCLUSION: The DL integrated smartphone application can accurately reflect and quantify flap condition; it is a convenient, accurate, and economical device that can improve patient safety and management and assist in monitoring flap physiology.

Reliability of Postoperative Free Flap Monitoring with a Novel Prediction Model Based on Supervised Machine Learning.

BACKGROUND: Postoperative free flap monitoring is a critical part of reconstructive microsurgery. Postoperative clinical assessments rely heavily on specialty-trained staff. Therefore, in regions with limited specialist availability, the feasibility of performing microsurgery is restricted. This study aimed to apply artificial intelligence in postoperative free flap monitoring and validate the ability of machine learning in predicting and differentiating types of postoperative free flap circulation. METHODS: Postoperative data from 176 patients who received free flap surgery were prospectively collected, including free flap photographs and clinical evaluation measures. Flap circulation outcome variables included normal, arterial insufficiency, and venous insufficiency. The Synthetic Minority Oversampling Technique plus Tomek Links (SMOTE-Tomek) was applied for data balance. Data were divided into 80%:20% for model training and validation. Shapley Additive Explanations were used for prediction interpretations of the model. RESULTS: Of 805 total included flaps, 555 (69%) were normal, 97 (12%) had arterial insufficiency, and 153 (19%) had venous insufficiency. The most effective prediction model was developed based on random forest, with an accuracy of 98.4%. Temperature and color differences between the flap and the surrounding skin were the most significant contributing factors to predict a vascular compromised flap. CONCLUSIONS: This study demonstrated the reliability of a machine-learning model in differentiating various types of postoperative flap circulation. This novel technique may reduce the burden of free flap monitoring and encourage the broader use of reconstructive microsurgery in regions with a limited number of staff specialists.