@article{RigamontiEstelGehlenetal.2021,
  author    = {Rigamonti, Lia and Estel, Katharina and Gehlen, Tobias and Wolfarth, Bernd and Lawrence, James B. and Back, David A.},
  title     = {Use of artificial intelligence in sports medicine},
  series = {BMC Sports Science, Medicine \& Rehabilitation},
  volume    = {13},
  journal   = {BMC Sports Science, Medicine \& Rehabilitation},
  publisher = {BioMed Central},
  address   = {London},
  issn      = {2052-1847},
  doi       = {10.1186/s13102-021-00243-x},
  pages     = {17},
  year      = {2021},
  abstract  = {Background Artificial intelligence (AI) is one of the most promising areas in medicine with many possibilities for improving health and wellness. Already today, diagnostic decision support systems may help patients to estimate the severity of their complaints. This fictional case study aimed to test the diagnostic potential of an AI algorithm for common sports injuries and pathologies. Methods Based on a literature review and clinical expert experience, five fictional "common" cases of acute, and subacute injuries or chronic sport-related pathologies were created: Concussion, ankle sprain, muscle pain, chronic knee instability (after ACL rupture) and tennis elbow. The symptoms of these cases were entered into a freely available chatbot-guided AI app and its diagnoses were compared to the pre-defined injuries and pathologies. Results A mean of 25-36 questions were asked by the app per patient, with optional explanations of certain questions or illustrative photos on demand. It was stressed, that the symptom analysis would not replace a doctor's consultation. A 23-yr-old male patient case with a mild concussion was correctly diagnosed. An ankle sprain of a 27-yr-old female without ligament or bony lesions was also detected and an ER visit was suggested. Muscle pain in the thigh of a 19-yr-old male was correctly diagnosed. In the case of a 26-yr-old male with chronic ACL instability, the algorithm did not sufficiently cover the chronic aspect of the pathology, but the given recommendation of seeing a doctor would have helped the patient. Finally, the condition of the chronic epicondylitis in a 41-yr-old male was correctly detected. Conclusions All chosen injuries and pathologies were either correctly diagnosed or at least tagged with the right advice of when it is urgent for seeking a medical specialist. However, the quality of AI-based results could presumably depend on the data-driven experience of these programs as well as on the understanding of their users. Further studies should compare existing AI programs and their diagnostic accuracy for medical injuries and pathologies.},
  language  = {en}
}
@misc{RigamontiEstelGehlenetal.2021,
  author    = {Rigamonti, Lia and Estel, Katharina and Gehlen, Tobias and Wolfarth, Bernd and Lawrence, James B. and Back, David A.},
  title     = {Use of artificial intelligence in sports medicine},
  series = {Postprints der Universit{\"a}t Potsdam : Humanwissenschaftliche Reihe},
  journal   = {Postprints der Universit{\"a}t Potsdam : Humanwissenschaftliche Reihe},
  issn      = {1866-8364},
  doi       = {10.25932/publishup-51552},
  url       = {http://nbn-resolving.de/urn:nbn:de:kobv:517-opus4-515528},
  pages     = {19},
  year      = {2021},
  abstract  = {Background Artificial intelligence (AI) is one of the most promising areas in medicine with many possibilities for improving health and wellness. Already today, diagnostic decision support systems may help patients to estimate the severity of their complaints. This fictional case study aimed to test the diagnostic potential of an AI algorithm for common sports injuries and pathologies. Methods Based on a literature review and clinical expert experience, five fictional "common" cases of acute, and subacute injuries or chronic sport-related pathologies were created: Concussion, ankle sprain, muscle pain, chronic knee instability (after ACL rupture) and tennis elbow. The symptoms of these cases were entered into a freely available chatbot-guided AI app and its diagnoses were compared to the pre-defined injuries and pathologies. Results A mean of 25-36 questions were asked by the app per patient, with optional explanations of certain questions or illustrative photos on demand. It was stressed, that the symptom analysis would not replace a doctor's consultation. A 23-yr-old male patient case with a mild concussion was correctly diagnosed. An ankle sprain of a 27-yr-old female without ligament or bony lesions was also detected and an ER visit was suggested. Muscle pain in the thigh of a 19-yr-old male was correctly diagnosed. In the case of a 26-yr-old male with chronic ACL instability, the algorithm did not sufficiently cover the chronic aspect of the pathology, but the given recommendation of seeing a doctor would have helped the patient. Finally, the condition of the chronic epicondylitis in a 41-yr-old male was correctly detected. Conclusions All chosen injuries and pathologies were either correctly diagnosed or at least tagged with the right advice of when it is urgent for seeking a medical specialist. However, the quality of AI-based results could presumably depend on the data-driven experience of these programs as well as on the understanding of their users. Further studies should compare existing AI programs and their diagnostic accuracy for medical injuries and pathologies.},
  language  = {en}
}
@phdthesis{Rigamonti2023,
  author    = {Rigamonti, Lia},
  title     = {Use of digital media for remote instruction in exercise sciences education and research},
  doi       = {10.25932/publishup-58364},
  url       = {http://nbn-resolving.de/urn:nbn:de:kobv:517-opus4-583642},
  school      = {Universit{\"a}t Potsdam},
  pages     = {129},
  year      = {2023},
  abstract  = {To grant high-quality evidence-based research in the field of exercise sciences, it is often necessary for various institutions to collaborate over longer distances and internationally. Here, not only with regard to the recent COVID-19-pandemic, digital means provide new options for remote scientific exchanges. This thesis is meant to analyse and test digital opportunities to support the dissemination of knowledge and instruction of investigators about defined examination protocols in an international multi-center context. The project consisted of three studies. The first study, a questionnaire-based survey, aimed at learning about the opinions and preferences of digital learning or social media among students of sport science faculties in two universities each in Germany, the UK and Italy. Based on these findings, in a second study, an examination video of an ultrasound determination of the intima-media-thickness and diameter of an artery was distributed by a messenger app to doctors and nursing personnel as simulated investigators and efficacy of the test setting was analysed. Finally, a third study integrated the use of an augmented reality device for direct remote supervision of the same ultrasound examinations in a long-distance international setting with international experts from the fields of engineering and sports science and later remote supervision of augmented reality equipped physicians performing a given task. The first study with 229 participating students revealed a high preference for YouTube to receive video-based knowledge as well as a preference for using WhatsApp and Facebook for peer-to-peer contacts for learning purposes and to exchange and discuss knowledge. In the second study, video-based instructions send by WhatsApp messenger showed high approval of the setup in both study groups, one with doctors familiar with the use of ultrasound technology as well as one with nursing staff who were not familiar with the device, with similar results in overall time of performance and the measurements of the femoral arteries. In the third and final study, experts from different continents were connected remotely to the examination site via an augmented reality device with good transmission quality. The remote supervision to doctors' examination produced a good interrater correlation. Experiences with the augmented reality-based setting were rated as highly positive by the participants. Potential benefits of this technique were seen in the fields of education, movement analysis, and supervision. Concluding, the findings of this thesis were able to suggest modern and addressee- centred digital solutions to enhance the understanding of given examinations techniques of potential investigators in exercise science research projects. Head-mounted augmented reality devices have a special value and may be recommended for collaborative research projects with physical examination-based research questions. While the established setting should be further investigated in prospective clinical studies, digital competencies of future researchers should already be enhanced during the early stages of their education.},
  language  = {en}
}