A retrospective analysis of factors affecting speech production in school-aged children with cleft palate (+- cleft lip).

OBJECTIVE: The present investigation examined how factors such as cleft type, age of primary palatal surgery, diagnosed syndromes, hearing problems, and malocclusions could predict persistent speech difficulties and the need for speech services in school-aged children with cleft palate. METHODS: Participants included 100 school-aged children with cleft palate. Americleft speech protocol was used to assess the perceptual aspects of speech production. The logistic regression was performed to evaluate the impact of independent variables (IV) on the dependent variables (DV): intelligibility, posterior oral CSCs, audible nasal emission, hypernasality, anterior oral CSCs, and speech therapy required. RESULTS: Sixty-five percent of the children were enrolled in (or had received) speech therapy. The logistic regression model shows a good fit to the data for the need for speech therapy (Hosmer and Lemeshow's χ2(8)=9.647,p=.291). No IVs were found to have a significant impact on the need for speech therapy. A diagnosed syndrome was associated with poorer intelligibility (Pulkstenis-Robinson's χ2(11)=7.120,p=.789). Children with diagnosed syndromes have about six times the odds of a higher hypernasality rating (Odds Ratio = 5.703) than others. The cleft type was significantly associated with audible nasal emission (Fisher'sexactp=.006). At the same time, malocclusion had a significant association with anterior oral CSCs (Fisher'sexactp=.005). CONCLUSIONS: According to the latest data in the Cleft Registry and Audit Network Annual Report for the UK, the majority of children with cleft palate attain typical speech by age five. However, it is crucial to delve into the factors that may influence the continuation of speech disorders beyond this age. This understanding is vital for formulating intervention strategies aimed at mitigating the long-term effects of speech disorders as individuals grow older.

Improving Proton Dose Calculation Accuracy by Using Deep Learning.

Introduction: Pencil beam (PB) dose calculation is fast but inaccurate due to the approximations when dealing with inhomogeneities. Monte Carlo (MC) dose calculation is the most accurate method but it is time consuming. The aim of this study was to develop a deep learning model that can boost the accuracy of PB dose calculation to the level of MC dose by converting PB dose to MC dose for different tumor sites. Methods: The proposed model uses the PB dose and CT image as inputs to generate the MC dose. We used 290 patients (90 head and neck, 93 liver, 75 prostate and 32 lung) to train, validate, and test the model. For each tumor site, we performed four numerical experiments to explore various combinations of training datasets. Results: Training the model on data from all tumor sites together and using the dose distribution of each individual beam as input yielded the best performance for all four tumor sites. The average gamma passing rate (1mm/1%) between the converted and the MC dose was 92.8%, 92.7%, 89.7% and 99.6% for head and neck, liver, lung, and prostate test patients, respectively. The average dose conversion time for a single field was less than 4 seconds. The trained model can be adapted to new datasets through transfer learning. Conclusions: Our deep learning-based approach can quickly boost the accuracy of PB dose to that of MC dose. The developed model can be added to the clinical workflow of proton treatment planning to improve dose calculation accuracy.

Boosting radiotherapy dose calculation accuracy with deep learning.

In radiotherapy, a trade-off exists between computational workload/speed and dose calculation accuracy. Calculation methods like pencil-beam convolution can be much faster than Monte-Carlo methods, but less accurate. The dose difference, mostly caused by inhomogeneities and electronic disequilibrium, is highly correlated with the dose distribution and the underlying anatomical tissue density. We hypothesize that a conversion scheme can be established to boost low-accuracy doses to high-accuracy, using intensity information obtained from computed tomography (CT) images. A deep learning-driven framework was developed to test the hypothesis by converting between two commercially available dose calculation methods: Anisotropic analytic algorithm (AAA) and Acuros XB (AXB). A hierarchically dense U-Net model was developed to boost the accuracy of AAA dose toward the AXB level. The network contained multiple layers of varying feature sizes to learn their dose differences, in relationship to CT, both locally and globally. Anisotropic analytic algorithm and AXB doses were calculated in pairs for 120 lung radiotherapy plans covering various treatment techniques, beam energies, tumor locations, and dose levels. For each case, the CT and the AAA dose were used as the input and the AXB dose as the "ground-truth" output, to train and test the model. The mean squared errors (MSEs) and gamma passing rates (2 mm/2% & 1 mm/1%) were calculated between the boosted AAA doses and the "ground-truth" AXB doses. The boosted AAA doses demonstrated substantially improved match to the "ground-truth" AXB doses, with average (± s.d.) gamma passing rate (1 mm/1%) 97.6% (±2.4%) compared to 87.8% (±9.0%) of the original AAA doses. The corresponding average MSE was 0.11(±0.05) vs 0.31(±0.21). Deep learning is able to capture the differences between dose calculation algorithms to boost the low-accuracy algorithms. By combining a less accurate dose calculation algorithm with a trained deep learning model, dose calculation can potentially achieve both high accuracy and efficiency.

Technical Note: A feasibility study on deep learning-based radiotherapy dose calculation.

PURPOSE: Various dose calculation algorithms are available for radiation therapy for cancer patients. However, these algorithms are faced with the tradeoff between efficiency and accuracy. The fast algorithms are generally less accurate, while the accurate dose engines are often time consuming. In this work, we try to resolve this dilemma by exploring deep learning (DL) for dose calculation. METHODS: We developed a new radiotherapy dose calculation engine based on a modified Hierarchically Densely Connected U-net (HD U-net) model and tested its feasibility with prostate intensity-modulated radiation therapy (IMRT) cases. Mapping from an IMRT fluence map domain to a three-dimensional (3D) dose domain requires a deep neural network of complicated architecture and a huge training dataset. To solve this problem, we first project the fluence maps to the dose domain using a broad beam ray-tracing (RT) algorithm, and then we use the HD U-net to map the RT dose distribution into an accurate dose distribution calculated using a collapsed cone convolution/superposition (CS) algorithm. The model is trained on 70 patients with fivefold cross validation, and tested on a separate 8 patients. RESULTS: It takes about 1 s to compute a 3D dose distribution for a typical 7-field prostate IMRT plan, which can be further reduced to achieve real-time dose calculation by optimizing the network. The average Gamma passing rate between DL and CS dose distributions for the 8 test patients are 98.5% (±1.6%) at 1 mm/1% and 99.9% (±0.1%) at 2 mm/2%. For comparison of various clinical evaluation criteria (dose-volume points) for IMRT plans between two dose distributions, the average difference for dose criteria is less than 0.25 Gy while for volume criteria is <0.16%, showing that the DL dose distributions are clinically identical to the CS dose distributions. CONCLUSIONS: We have shown the feasibility of using DL for calculating radiotherapy dose distribution with high accuracy and efficiency.