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In this work, we propose measuring the magnetic field's variation due to distortion at a witness position to localise the instrument causing distortion based on a pre-trained model and without additional sensors attached to it. <\/jats:p>\n              <\/jats:sec><jats:sec>\n                <jats:title>Methods<\/jats:title>\n                <jats:p>Two experiments were performed to demonstrate possible applications of the technique proposed. In the first case, the distortion introduced by an ultrasound (US) probe was characterised and subsequently used to track the probe position on a line. In the second application, the measurement was used to estimate the distance of an interventional fluoroscopy C-arm machine and apply the correct compensation model. <\/jats:p>\n              <\/jats:sec><jats:sec>\n                <jats:title>Results<\/jats:title>\n                <jats:p>Tracking of the US probe using the proposed method was demonstrated with millimetric accuracy. The distortion created by the C-arm caused errors in the order of centimetres, which were reduced to 1.52\u00a0mm RMS after compensation. <\/jats:p>\n              <\/jats:sec><jats:sec>\n                <jats:title>Conclusions<\/jats:title>\n                <jats:p>The distortion profile associated with medical equipment was pre-characterised and used in applications such as object tracking and error compensation map selection. In the current study, the movement was limited to one degree of freedom (1\u00a0DOF) and simple analytical functions were used to model the magnetic distortion. Future work will explore advanced AI models to extend the method to 6\u00a0DOF tracking using multiple witness sensors.<\/jats:p>\n              <\/jats:sec>","DOI":"10.1007\/s11548-023-02994-z","type":"journal-article","created":{"date-parts":[[2023,7,27]],"date-time":"2023-07-27T17:02:21Z","timestamp":1690477341000},"page":"27-31","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":3,"title":["Enhancing electromagnetic tracking accuracy in medical applications using pre-trained witness sensor distortion models"],"prefix":"10.1007","volume":"19","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-1901-0041","authenticated-orcid":false,"given":"Marco","family":"Cavaliere","sequence":"first","affiliation":[]},{"given":"P\u00e1draig","family":"Cantillon-Murphy","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2023,7,27]]},"reference":[{"issue":"8","key":"2994_CR1","doi-asserted-by":"publisher","first-page":"1702","DOI":"10.1109\/TMI.2014.2321777","volume":"33","author":"AM Franz","year":"2014","unstructured":"Franz AM, Haidegger T, Birkfellner W, Cleary K, Peters TM, Maier-Hein L (2014) Electromagnetic tracking in medicine\u2014a review of technology, validation, and applications. 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