METHOD: This systematic review was conducted using four recognised academic and scientific databases (Scopus, Web of Science, PubMed and Cochrane) to identify articles that met the inclusion criteria. The secondary search used the Google Scholar and the Science Direct search engines. The search for articles for this review began in July 06, 2023 and was concluded on February 01, 2024. The search process for this study was documented using the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA 2020). The PEDro scale was used to assess the internal validity and data statistics of the studies included in this systematic review and to evaluate the quality of the studies.
RESULTS: The systematic review included nine studies that met the inclusion criteria from the 651 studies retrieved, involving a total of 643 participants. The PEDro scale scores of the studies included in this systematic review ranged from 3 to 8. The intervention was in the form of Pilates or Pilates combined exercises. The studies included in this review used outcome measures of Cobb angle, angle of trunk rotation (ATR), range of motion (ROM), chest expansion, Scoliosis Research Society Questionnaire (SRS-22r) and postural assessment. Research has shown that Pilates is effective in correcting spinal deformities and posture, as well as improving quality of life, pain relief, function and fitness.
CONCLUSIONS: This systematic review provide substantial evidence that Pilates has a positive impact on improving spinal deformity and posture. However, more research is needed to validate whether Pilates can be used effectively as a physical therapy for spinal deformity rehabilitation. Pilates has considerable potential for public health interventions.
APPROACH: In this paper, we propose a novel model called radiomics-reporting network (Radioport), which incorporates text attention. This model aims to improve the interpretability of deep learning radiomics in mammographic calcification diagnosis. Firstly, it employs convolutional neural networks (CNN) to extract visual features as radiomics for multi-category classification based on Breast Imaging Reporting and Data System (BI-RADS). Then, it builds a mapping between these visual features and textual features to generate diagnostic reports, incorporating an attention module for improved clarity.
MAIN RESULTS: To demonstrate the effectiveness of our proposed model, we conducted experiments on a breast calcification dataset comprising mammograms and diagnostic reports. The results demonstrate that our model can: (i) semantically enhance the interpretability of deep learning radiomics; and, (ii) improve the readability of generated medical reports.
SIGNIFICANCE: Our interpretable textual model can explicitly simulate the mammographic calcification diagnosis process.