A Two-Flap Combination for Auricular Elevation in Microtia Reconstruction.

BACKGROUND: The aims of this study were to describe and evaluate the effectiveness of combined flaps, a modification of the Nagata technique, for providing a reasonable projection for reconstructed auricles. METHODS: The authors modified the Nagata method for covering the cartilage block by introducing a new combined flap technique, including the temporoparietal skin flap and retroauricular flap. The authors compared the shape, size, and position of the reconstructed ear to the opposite ear, and we evaluated postoperative complications and patient satisfaction levels. They verified the effectiveness of the combined flap by assessing flap necrosis, skin color, thickness, hair in the auricular area, and scars. RESULTS: A total of 38 consecutive patients (39 ears) with microtia, aged 6 to 34 years, underwent reconstruction using the modified method and were followed up for 33.6 months on average. The reconstructed auricle's shape was well defined, with 41.0% having good and 15.4% having excellent results. Most cases achieved good and acceptable levels in size, position, medial longitudinal axis angle, and auriculocephalic angles, and 79.9% of patients/their families were satisfied. The authors observed no cases of flap necrosis or hypertrophic scarring, and there were low rates of flap complications, such as unmatched skin color (7.7%), unacceptable thickness (5.1%), or hair and stretch marks (10.3%). CONCLUSIONS: The modified method's reconstructed ear achieved stable projection, symmetric appearance, and obvious anatomical landmarks with high patient satisfaction. The combined flap method showed certain advantages: high survival rate, less skin contrast, no hypertrophic scars, and fewer complications. CLINICAL QUESTION/LEVEL OF EVIDENCE: Therapeutic, IV.

End-to-end deep learning model for segmentation and severity staging of anterior cruciate ligament injuries from MRI.

PURPOSE: The purpose of this study was to develop a semi-supervised segmentation and classification deep learning model for the diagnosis of anterior cruciate ligament (ACL) tears on MRI based on a semi-supervised framework, double-linear layers U-Net (DCLU-Net). MATERIALS AND METHODS: A total of 297 participants who underwent of total of 303 MRI examination of the knee with fat-saturated proton density (PD) fast spin-echo (FSE) sequence in the sagittal plane were included. There were 214 men and 83 women, with a mean age of 37.46 ± 1.40 (standard deviation) years (range: 29-44 years). Of these, 107 participants had intact ACL (36%), 98 had partially torn ACL (33%), and 92 had fully ruptured ACL (31%). The DCLU-Net was combined with radiomic features for enhancing performances in the diagnosis of ACL tear. The different evaluation metrics for both classification (accuracy, sensitivity, accuracy) and segmentation (mean Dice similarity coefficient and root mean square error) were compared individually for each image class across the three phases of the model, with each value being compared to its respective value from the previous phase. Findings at arthroscopic knee surgery were used as the standard of reference. RESULTS: With the addition of radiomic features, the final model yielded accuracies of 90% (95% CI: 83-92), 82% (95% CI: 73-86), and 92% (95% CI: 87-94) for classifying ACL as intact, partially torn and fully ruptured, respectively. The DCLU-Net achieved mean Dice similarity coefficient and root mean square error of 0.78 (95% CI: 0.71-0.80) and 0.05 (95% CI: 0.06-0.07), respectively, when segmenting the three ACL conditions with pseudo data (P < 0.001). CONCLUSION: A dual-modules deep learning model with segmentation and classification capabilities was successfully developed. In addition, the use of semi-supervised techniques significantly reduced the amount of manual segmentation data without compromising performance.

Machine Learning-Based Prediction of Drug-Drug Interactions for Histamine Antagonist Using Hybrid Chemical Features.

The requesting of detailed information on new drugs including drug-drug interactions or targets is often unavailable and resource-intensive in assessing adverse drug events. To shorten the common evaluation process of drug-drug interactions, we present a machine learning framework-HAINI to predict DDI types for histamine antagonist drugs using simplified molecular-input line-entry systems (SMILES) combined with interaction features based on CYP450 group as inputs. The data used in our research consisted of approved drugs of histamine antagonists that are connected to 26,344 DDI pairs from the DrugBank database. Various classification algorithms such as Naive Bayes, Decision Tree, Random Forest, Logistic Regression, and XGBoost were used with 5-fold cross-validation to approach a large-scale DDIs prediction among histamine antagonist drugs. The prediction performance shows that our model outperformed previously published works on DDI prediction with the best precision of 0.788, a recall of 0.921, and an F1-score of 0.838 among 19 given DDIs types. An important finding of the study is that our prediction is based solely on the SMILES and CYP450 and thus can be applied at the early stage of drug development.