Recently progressive resistance training (PRT) has been recognised as an effective tool in the rehabilitation of persons with multiple sclerosis (MS). The objective of this study was to systematically review the literature of PRT studies for persons with MS. A comprehensive literature search (PubMed, SveMed+, Embase, Cochrane, PEDro, SPORTDiscus and Bibliotek.dk) was conducted. Identified papers were rated according to the PEDro-scale. Sixteen studies were included and scored between 3 and 8 of 11 total points on the PEDro-scale, showing a general lack of blinding. Strong evidence regarding the beneficial effect of PRT on muscle strength was observed. Regarding functional capacity, balance and self-reported measures (fatigue, quality of life and mood) evidence is less strong, but the tendency is overall positive. Indications of an effect on underlying mechanisms such as muscle morphological changes, neural adaptations and cytokines also exist, but the studies investigating these aspects are few and inconclusive. PRT has a positive effect on muscle strength for persons with MS. Heterogeneous results exist regarding the effect on functional capacity and self-reported measures probably because of differences in training protocols, samples sizes, type and severity of MS. The area of underlying mechanisms deserves more attention in future research.

Fascicle angle (FA) is suggested to increase as a result of fiber hypertrophy and furthermore to serve as the explanatory link in the discrepancy in the relative adaptations in the anatomical cross-sectional area (CSA) and fiber CSA after resistance training (RT). In contrast to RT, the effects of endurance training on FA are unclear. The purpose of this study was therefore to investigate and compare the longitudinal effects of either progressive endurance training (END, n = 7) or RT (n = 7) in young untrained men on FA, anatomical CSA, and fiber CSA. Muscle morphological measures included the assessment of vastus lateralis FA obtained by ultrasonography and anatomical CSA by magnetic resonance imaging of the thigh and fiber CSA deduced from histochemical analyses of biopsy samples from m. vastus lateralis. Functional performance measures included VO2max and maximal voluntary contraction (MVC). The RT produced increases in FA by 23 ± 8% (p < 0.01), anatomical CSA of the knee extensor muscles by 9 ± 3% (p = 0.001), and fiber CSA by 19 ± 7% (p < 0.05). RT increased knee extensor MVC by 20 ± 5% (p < 0.001). END increased VO2max by 10 ± 2% but did not evoke changes in FA, anatomical CSA, or in fiber CSA. In conclusion, the morphological changes induced by 10 weeks of RT support that FA does indeed serve as the explanatory link in the observed discrepancy between the changes in anatomical and fiber CSA. Contrarily, 10 weeks of endurance training did not induce changes in FA, but the lack of morphological changes from END indirectly support the fact that fiber hypertrophy and FA are interrelated.

Background: Artificial intelligence (AI) is seen as one of the major disrupting forces in the future healthcare system. However, the assessment of the value of these new technologies is still unclear, and no agreed international health technology assessment-based guideline exists. This study provides an overview of the available literature in the value assessment of AI in the field of medical imaging.Methods: We performed a systematic scoping review of published studies between January 2016 and September 2020 using 10 databases (Medline, Scopus, ProQuest, Google Scholar, and six related databases of grey literature). Information about the context (country, clinical area, and type of study) and mentioned domains with specific outcomes and items were extracted. An existing domain classification, from a European assessment framework, was used as a point of departure, and extracted data were grouped into domains and content analysis of data was performed covering predetermined themes.Results: Seventy-nine studies were included out of 5890 identified articles. An additional seven studies were identified by searching reference lists, and the analysis was performed on 86 included studies. Eleven domains were identified: (1) health problem and current use of technology, (2) technology aspects, (3) safety assessment, (4) clinical effectiveness, (5) economics, (6) ethical analysis, (7) organisational aspects, (8) patients and social aspects, (9) legal aspects, (10) development of AI algorithm, performance metrics and validation, and (11) other aspects. The frequency of mentioning a domain varied from 20 to 78% within the included papers. Only 15/86 studies were actual assessments of AI technologies. The majority of data were statements from reviews or papers voicing future needs or challenges of AI research, i.e. not actual outcomes of evaluations.Conclusions: This review regarding value assessment of AI in medical imaging yielded 86 studies including 11 identified domains. The domain classification based on European assessment framework proved useful and current analysis added one new domain. Included studies had a broad range of essential domains about addressing AI technologies highlighting the importance of domains related to legal and ethical aspects.