Bauer, Bernhard

Ebigbo, Alanna

Elia, Miriam

Grünherz, Vivien

Nagl, Sandra

Peter, Tobias

Stieler, Fabian

English

OPUS Augsburg

PRECISION MEDICINE FOR ACHALASIA DIAGNOSIS: A MULTI-MODAL ANDINTERDISCIPLINARY APPROACH FOR TRAINING DATA GENERATIONM. Elia, T. Peter, F. Stieler, B. BauerFaculty of Applied Computer ScienceUniversity of Augsburg, GermanyS. Nagl, A. Ebigbo, V. GrünherzGastroenterologyUniversity Hospital Augsburg, GermanyAchalasia (AC) is a rare chronic esophageal motility dis-order, characterized by impaired deglutitive lower esophagealsphincter (ES) relaxation and absent propulsive tubular peri-stalsis. Therapeutic management of AC patients targets re-lieving the obstruction at the esophagogastric junction. How-ever, treatment response can vary depending on factors suchas type of AC, degree of dilatation, kinking of the tubularesophagus and pressure at the lower ES. In line with precisionmedicine (PM) [1], the present work introduces an interdis-ciplinarily developed concept to combine valuable informa-tion from different diagnostic data sources into one coherent3D-representation of the esophagus. In addition to improv-ing diagnostic precision, the long-term objective is to traina machine learning (ML) model that predicts the success ofindividual treatment options. 11. A NOVEL COMPREHENSIVE DATATYPEMultiple data sources are consulted to diagnose the precisecase of AC. These include endoscopy (ED), timed bariumesophagogram (TBE), and esophageal manometry (MA).A first prototype that combines their information in a 3D-representation of the esophagus has already been developed.Interestingly, automated extraction of valuable informationlike the esophagus’ form in a TBE or the automated genera-tion of its 3D-representation proved to be unreliable due to ahigh variance in the data sources’ quality. In order to addressthese challenges, the software was adapted in such a manner,that physicians can control and adjust the automated selectionof relevant information before being utilized to generate the3D-representation. Another positive effect of the presentedhuman-in-the-loop approach is enhanced trust thanks to theseplausibility checks. The process is as follows: First, a TBEand N ED images are loaded to extract the esophagus’ form,and diameter at previously defined positions along its course.The TBE supports mapping all sources to a shared scale.There, the user marks the point zero of the ED images andtwo widely spaced sensor points of the MA, i.e. the upper and1Compliant with the Declaration of Helsinki. The study protocol wasapproved by the medical ethics committee of the Ludwig Maximilians Uni-versity of Munich on June 27, 2022 (registration no. 22-0149).lower ES. The resulting approximation of the patient’s esoph-agus displays color-scaled pressure values whose movementcan be tracked over the complete time span of the MA. Allin all, there is still potential for improvement regarding therepresentation’s accuracy. The course of the MA catheter, forinstance, is only estimated. Furthermore, the approximationof the diameter could be more precise, and its implementa-tion should be integrated within the ED technology, as in [2]for measuring polyp size during live colonoscopies. Also,the human-in-the-loop approach involves some risks, sinceinserting points of reference by hand is prone to some sort ofvariance. Thus, further automating the mapping of all datasources while still including the physician as reality check ap-pears fitting. To enhance diagnostic precision, additional datasources (Endosonography or EndoFLIP) could be integratedto include information about the thickness of the esophagealwall or distensibility.2. OUTLOOK: MACHINE LEARNINGAs of now, the prototype is tested in a clinical environment toassess therapeutic performance. An index consisting of vol-ume and pressure values was added, to evaluate the therapy’ssuccess by comparing 3D-representations from before and af-ter the procedure. The desired outcome is a decrease in sizeabove the ES and a dilation within. Based on that data and in-dex, the long-term objective is to enhance AC treatment withartificial intelligence, including domain experts from ML andmedicine. Generally, disease data is rare and the presentedmulti-modal approach to develop a novel comprehensive datatype for the diagnosis of AC is expected to capture informa-tion more precisely, in line with PM.3. REFERENCES[1] S. J. MacEachern et al., “Machine learning for precisionmedicine,” Genome, vol. 64, no. 4, pp. 416–425, 2021.[2] D. von Renteln et al., “Measuring size of smaller col-orectal polyps using a virtual scale function during en-doscopies,” Gut, p. gutjnl—2022—328654, November2022.

Precision medicine for achalasia diagnosis: a multi-modal and interdisciplinary approach for training data generation [Abstract]

https://opus.bibliothek.uni-augsburg.de/opus4/files/104278/104278.pdf

Precision medicine for achalasia diagnosis: a multi-modal and interdisciplinary approach for training data generation [Abstract]

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