Objective: This study investigated the inter- and intra-rater reliability of the Australian Spasticity Assessment Scale (ASAS) in adults with unilateral hypertonia following acquired brain injury. The ASAS has been sh...Objective: This study investigated the inter- and intra-rater reliability of the Australian Spasticity Assessment Scale (ASAS) in adults with unilateral hypertonia following acquired brain injury. The ASAS has been shown to be superior to other clinical tools for the assessment of spasticity in children with cerebral palsy but reliability has not been previously examined in adults. Method: Four muscle groups were rated on one occasion by four assessors using the ASAS in sixteen adults with unilateral hypertonia following acquired brain injury. Twelve participants returned one week later for reassessment by the same assessors. Results: Overall inter-rater reliability of the ASAS using a quadratic weighted Kappa was moderate (Kqw 0.58) with ranges from moderate to good (Kqw 0.42 - 0.70). Agreement between raters was greatest for soleus muscle and least for wrist flexors. Overall intra-rater reliability of each of the four raters was moderate to good (Kqw 0.48 - 0.79). Agreement within raters was greatest for soleus muscle and least for biceps muscle. Conclusions: The ASAS may represent an appropriate alternative to the clinical scales currently used to assess spasticity;however inter and intra-rater reliability data from this investigation are lower than those which have previously been reported by experienced users of the ASAS in children with cerebral palsy. Further investigation with a larger sample size is warranted before any firm conclusions may be drawn about the reliability and validity of this tool to assess spasticity in adults with acquired brain injury.展开更多
In medical image segmentation,it is often necessary to collect opinions from multiple experts to make the final decision.This clinical routine helps to mitigate individual bias.However,when data is annotated by multip...In medical image segmentation,it is often necessary to collect opinions from multiple experts to make the final decision.This clinical routine helps to mitigate individual bias.However,when data is annotated by multiple experts,standard deep learning models are often not applicable.In this paper,we propose a novel neural network framework called Multi-rater Prism(MrPrism)to learn medical image segmentation from multiple labels.Inspired by iterative half-quadratic optimization,MrPrism combines the task of assigning multi-rater confidences and calibrated segmentation in a recurrent manner.During this process,MrPrism learns inter-observer variability while taking into account the image's semantic properties and finally converges to a self-calibrated segmentation result reflecting inter-observer agreement.Specifically,we propose Converging Prism(ConP)and Diverging Prism(DivP)to iteratively process the two tasks.ConP learns calibrated segmentation based on multi-rater confidence maps estimated by DivP,and DivP generates multi-rater confidence maps based on segmentation masks estimated by ConP.Experimental results show that the two tasks can mutually improve each other through this recurrent process.The final converged segmentation result of MrPrism outperforms state-of-the-art(SOTA)methods for a wide range of medical image segmentation tasks.The code is available at https://github.-com/WuJunde/MrPrism.展开更多
文摘Objective: This study investigated the inter- and intra-rater reliability of the Australian Spasticity Assessment Scale (ASAS) in adults with unilateral hypertonia following acquired brain injury. The ASAS has been shown to be superior to other clinical tools for the assessment of spasticity in children with cerebral palsy but reliability has not been previously examined in adults. Method: Four muscle groups were rated on one occasion by four assessors using the ASAS in sixteen adults with unilateral hypertonia following acquired brain injury. Twelve participants returned one week later for reassessment by the same assessors. Results: Overall inter-rater reliability of the ASAS using a quadratic weighted Kappa was moderate (Kqw 0.58) with ranges from moderate to good (Kqw 0.42 - 0.70). Agreement between raters was greatest for soleus muscle and least for wrist flexors. Overall intra-rater reliability of each of the four raters was moderate to good (Kqw 0.48 - 0.79). Agreement within raters was greatest for soleus muscle and least for biceps muscle. Conclusions: The ASAS may represent an appropriate alternative to the clinical scales currently used to assess spasticity;however inter and intra-rater reliability data from this investigation are lower than those which have previously been reported by experienced users of the ASAS in children with cerebral palsy. Further investigation with a larger sample size is warranted before any firm conclusions may be drawn about the reliability and validity of this tool to assess spasticity in adults with acquired brain injury.
基金supported by the Excellent Young Science and Technology Talent Cultivation Special Project of China Academy of Chinese Medical Sciences(CI2023D006)the National Natural Science Foundation of China(82121003 and 82022076)+2 种基金Beijing Natural Science Foundation(2190023)Shenzhen Fundamental Research Program(JCYJ20220818103207015)Guangdong Provincial Key Laboratory of Human Digital Twin(2022B1212010004)。
文摘In medical image segmentation,it is often necessary to collect opinions from multiple experts to make the final decision.This clinical routine helps to mitigate individual bias.However,when data is annotated by multiple experts,standard deep learning models are often not applicable.In this paper,we propose a novel neural network framework called Multi-rater Prism(MrPrism)to learn medical image segmentation from multiple labels.Inspired by iterative half-quadratic optimization,MrPrism combines the task of assigning multi-rater confidences and calibrated segmentation in a recurrent manner.During this process,MrPrism learns inter-observer variability while taking into account the image's semantic properties and finally converges to a self-calibrated segmentation result reflecting inter-observer agreement.Specifically,we propose Converging Prism(ConP)and Diverging Prism(DivP)to iteratively process the two tasks.ConP learns calibrated segmentation based on multi-rater confidence maps estimated by DivP,and DivP generates multi-rater confidence maps based on segmentation masks estimated by ConP.Experimental results show that the two tasks can mutually improve each other through this recurrent process.The final converged segmentation result of MrPrism outperforms state-of-the-art(SOTA)methods for a wide range of medical image segmentation tasks.The code is available at https://github.-com/WuJunde/MrPrism.
