Abstract Purpose Stroke patients may need to undergo rehabilitation therapy to improve their mobility. Electromyography (EMG) can be used to improve the effectiveness of at-home therapy programs, as it can assess recovery progress in the absence of a health professional. In particular, EMG armbands have the advantage of being easy to use compared to other EMG technologies, which could allow patients to complete therapy programs without external assistance. However, it is unclear whether there are drawbacks associated with the fixed electrode placement imposed by current armband designs. This study compared the hand gesture prediction capabilities of an off-the-shelf EMG armband with fixed electrode placement and an EMG setup with flexible electrode positioning. Methods Ten able-bodied participants performed a series of hand and finger gestures with their dominant hand, once with an EMG armband (Untargeted condition) and once with electrodes deliberately placed on specific muscles (Targeted condition). EMG features were extracted from overlapping sliding windows and were used to (1) classify the gestures and (2) predict finger joint positions as measured by a robotic hand exoskeleton. Results For the classification task, a logistic regression model performed significantly better ($$p < 0.001$$ p < 0.001 ) for the Targeted condition ($$55.8\% \pm 10.1\%$$ 55.8 % ± 10.1 % ) compared to the Untargeted condition ($$47.9\% \pm 11.6\%$$ 47.9 % ± 11.6 % ). For the regression task, a k-nearest neighbours model obtained significantly lower ($$p = 0.007$$ p = 0.007 ) mean RMSE values for the Targeted condition ($$0.260 \pm 0.037$$ 0.260 ± 0.037 ) compared to the Untargeted condition ($$0.270 \pm 0.043$$ 0.270 ± 0.043 ). Conclusion We observed a trade-off between predictive accuracy and ease-of-use of the EMG devices used in this study. It is important to consider such a trade-off when developing clinical applications such as at-home stroke rehabilitation therapy programs.

Abstract Purpose Medical technology has gone a long way in diagnosis and characterization of breast tumors. Diffusion-weighted MR imaging is the state of the art for breast screening and diagnosing. The aim of this meta-analysis is to evaluate the diagnostic performances of diffusion-weighted MR imaging in characterization of breast lesions with different b value in 1.5 T MRI. Method An extensive search on Scopus, Embase, and PubMed databases were performed on studies published between January 2000 and 2020. The systematic seek initially yielded 2467 studies, out of which 27 research were covered on this meta-evaluation. The included studies for meta-analysis utilized different b value and noted that the ADC value was highly influenced by the b value, for differential diagnosis of breast tumors. Results The current meta-analysis has shown the ADC values was lower for malignant breast lesions as compared with benign lesions. The recommended mean threshold ADC was 1.25 ± 0.17 × 10–3 mm2/s range from 0.93 to 1.60 × 10–3 mm2/s for differential diagnosis of breast tumors. Sub-group analysis on the bases of b value showed statistically significant differences in the ADC value of benign and malignant breast tumors. Conclusion In conclusion, we noted that b value has a significant effect in calculating the ADC value of the breast lesions as well as ADC threshold value but lacks standardization. The ADC value measurement has a potential for differentiation between benign and malignant breast lesions.

Abstract Purpose Head and neck squamous cell carcinoma (HNSCC) is one of the most common cancer types globally. Due to the complex anatomy of the region, diagnosis and treatment is challenging. Early diagnosis and treatment are important, because advanced and recurrent HNSCC have a poor prognosis. Robust and precise tools are needed to help diagnose HNSCC reliably in its early stages. The aim of this study was to assess the applicability of a convolutional neural network in detecting and auto-delineating HNSCC from PET-MRI data. Methods 2D U-net models were trained and tested on PET, MRI, PET-MRI and augmented PET-MRI data from 44 patients diagnosed with HNSCC. The scans were taken 12 weeks after chemoradiation therapy with a curative intention. A proportion of the patients had follow-up scans which were included in this study as well, giving a total of 62 PET-MRI scans. The scans yielded a total of 178 PET-MRI slices with cancer. A corresponding number of negative slices were chosen randomly yielding a total of 356 slices. The data was divided into training, validation and test sets (n = 247, n = 43 and n = 66 respectively). Dice score was used to evaluate the segmentation accuracy. In addition, the classification capabilities of the models were assessed. Results When true positive segmentations were considered, the mean Dice scores for the test set were 0.79, 0.84 and 0.87 for PET, PET-MRI and augmented PET-MRI, respectively. Classification accuracies were 0.62, 0.71 and 0.65 for PET, PET-MRI and augmented PET-MRI, respectively. The MRI based model did not yield segmentation results. A statistically significant difference was found between the PET-MRI and PET models (p = 0.008). Conclusion Automatic segmentation of HNSCC from the PET-MRI data with 2D U-nets was shown to give sufficiently accurate segmentations.