Background: Adult acquired flatfoot deformity (AAFD) and hallux valgus (HV) are common foot and ankle deformities. Few studies have reported the changes in radiographic parameters of HV after reconstructive surgery for AAFD. This study aimed to evaluate the changes in radiographic parameters of HV and analyze the risk factors for increased HV after correction of AAFD. Methods: Adult patients with flexible AAFD who underwent similar bony procedures including medializing calcaneal osteotomy and Cotton osteotomy were included. Radiographic parameters were measured on weightbearing radiographs preoperatively, postoperatively, and at the final follow-up. Patients were divided into hallux valgus angle (HVA) increased and HVA nonincreased groups; logistic regression analysis was performed to identify risk factors affecting increased HV. Results: Forty-six feet of 43 patients were included. After AAFD reconstructive surgery, the tibial sesamoid position improved by 1 grade, but the HVA increased 4 degrees in average. Further, 21 of 46 feet (46%) showed an HVA increase ≥5 degrees immediately after AAFD correction surgery. Preoperative talonavicular coverage angle <21.6 degrees was a risk factor associated with HV increase immediately after the surgery. Conclusion: In this case series, using plain radiographs to measure standard parameters of foot alignment, we found the association between AAFD correction and HV deformity measures somewhat paradoxical. Correction of overpronation of the hindfoot and midfoot appears to improve the first metatarsal rotational deformity but may also increase HVA. A lower preoperative talonavicular coverage angle was associated with an increase of the HVA after surgery. Level of Evidence: Level IV, case series study.
Cardiac signals are frequently used in disease and emotion analyses. However, current measurement methods mostly require direct contact. Remote photoplethysmography (rPPG) has been proposed in recent years which measures minute variations in color on the face due to blood volume changes as the heart pumps, using a consumer grade camera. In this study, we proposed a deep learning framework based on a light-weight and task-adapted version of U-Net to extract rPPG. The face video was converted into multiscale spatio-temporal map (MSTmap) as input to the network. Two types of attention mechanisms were added, namely variations of the squeeze-and-excitation block (SE block), which compresses global information to enhance the channel and ROI signals, and the multihead attention block with position encoding, which extracts information from different parts of the signal. We further propose using virtual PPG (vPPG) as a replacement for PPG ground-truth so that the model focuses on learning the peak information instead of morphological details. Extensive experiments were conducted using the UBFC-rPPG dataset for heart rate (HR) and heart rate variability (HRV) estimations. The model achieved a root-mean-square error of 0.78 bpm and correlation coefficient of 0.99 in heart rate estimation, which is comparable to state-of-the-art while being more light-weight.INDEX TERMS Attention, remote photoplethysmography, remote heart rate estimation, spatio-temporal map.
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