Dachs, Nicky

Abstract Body surface gastric mapping (BSGM) enables non-invasive measurement of gastric electrophysiology, but the signals are approximately 100 times weaker than cardiac potentials and overlap spectrally with motion artifacts, necessitating labor-intensive manual review that limits clinical scalability. We present an uncertainty-aware deep learning framework combining a signal reconstruction network with a parallel uncertainty estimation network to automate artifact correction in high-resolution BSGM. Models were trained on 2,398 multihour, 64-channel recordings from 27 international clinical sites using weak supervision, a physiology-aware loss function, and uncertainty-gated quality control. In an independent cohort of 127 patients, the system achieved relative reductions of 39% in signal reconstruction error, 9% in total data removed, and 23% in amplitude–movement correlation compared with the industry-standard Wiener filter. Improved signal fidelity altered automated clinical phenotyping in 7% of patients by recovering previously obscured gastric rhythms. Uncertainty-aware deep learning enables reliable automated artifact correction in body-surface gastric mapping, improving signal fidelity and enabling scalable clinical interpretation. The system is FDA-cleared (510(k) K252504) and deployed in clinical practice, demonstrating that data-driven artifact correction can meet regulatory requirements for medical devices and reduce dependence on specialist manual review.

ABSTRACT Introduction Chronic gastroduodenal disorders remain challenging to manage, and new diagnostic approaches are needed to better delineate underlying causes and guide therapeutic decisions. Body Surface Gastric Mapping (BSGM) technologies combine high‐resolution gastric myoelectrical activity measurements with symptom and psychological profiling to provide mechanistic insights into gastric motor and sensory dysfunction. An International Working Group convened to derive the first consensus classification of BSGM phenotypes (the “Auckland Classification”). Methods A Technical Group conducted a systematic literature and clinical database review to identify objective test biomarkers and candidate disease mechanisms. Evidence was synthesized across 50 studies (primarily in gastroparesis, chronic nausea and vomiting, and functional dyspepsia), and BSGM phenotypes were mapped to existing treatment guidelines. Subsequently, iterative review and development of consensus was performed by a Consensus Group composed of international clinical experts familiar with BSGM. Eleven statements underlying the classification were then derived and circulated as a final survey to establish agreement. Results Six BSGM phenotypes were endorsed: three defined by myoelectrical abnormalities (Dysrhythmic, High Frequency, and Low Meal Response) and three by characteristic symptom associations (Sensorimotor, Continuous, and Delayed Onset Symptoms). Published studies plausibly linked these phenotypes to mechanisms including interstitial cell of Cajal depletion, vagal impairment, hypomotility, visceral hypersensitivity, gut‐brain dysregulation, and small bowel dysfunction. Phenotypes were also mapped to existing mechanism‐based treatment guidelines. Ten out of the eleven statements had > 80% agreement. Conclusions The Auckland Classification, derived by international consensus, presents a structured framework for BSGM‐defined patient phenotypes. Evidence and mechanism‐based treatment options are suggested for each phenotype to provide a foundation for research, further validation, and a pathway for integrating BSGM into clinical care.

ABSTRACT Background Body surface gastric mapping (BSGM) non‐invasively assesses gastric myoelectrical activity along with real‐time symptom reporting. In adults, the development of normative intervals has underpinned new explanatory phenotypes, aiding clinical decision‐making. This study established normative reference intervals for adolescent BSGM metrics. Methods Healthy adolescents aged 12–17 years with a BMI ≤ 35 kg/m 2 were recruited from New Zealand, Australia, and the United States. BSGM using Gastric Alimetry (Alimetry, New Zealand) involved a 30‐min fast, followed by a 480‐kcal meal, and a 4‐h postprandial recording. Reference intervals were calculated for four validated metrics: Principal Gastric Frequency (PGF), body mass index (BMI)‐adjusted amplitude, Gastric Alimetry Rhythm Index (GA‐RI, indicating rhythm stability), and the fed: fasted amplitude ratio (ff‐AR). Results were reported at the median and 5th and/or 95th percentiles as appropriate. Key Results A total of 107 participants (52.8% female, median age 14 [IQR 13–16], median BMI 20.1 [IQR 18.75–22.40]) with mixed ethnicities were included. No substantive correlations were observed between BSGM metrics and demographics or anthropometric data. Therefore, a single set of normative reference intervals was established. Median PGF was 3.06 cycles per minute; reference interval 2.72–3.37. Median BMI‐adjusted amplitude was 37.80 μV; reference interval 20.0–72.0. Median GA‐RI was 0.51; reference interval ≥ 0.22. Median ff‐AR was 2.12; reference interval ≥ 1.0. Conclusions and Inferences This study presents normative reference intervals for BSGM spectral metrics in adolescent populations, informing the interpretation of tests in research and clinical practice.

ABSTRACT Background Chronic neurogastroduodenal disorders are challenging to manage, with therapy often initiated on a trial and error basis. Prokinetics play a significant role in management, but responses are variable and have been associated with adverse events, impacting widespread use. We investigated whether body surface gastric mapping (BSGM) biomarkers (using Gastric Alimetry) could inform patient selection for prokinetic therapy. Methods Patients with chronic gastroduodenal symptoms taking oral prokinetic agents, regardless of gastric emptying status, were prospectively recruited and underwent BSGM (30 m baseline, 482 kcal standardized meal, 4 h postprandial recording) while off‐prokinetic agents. Patients were followed up with daily symptom diaries. A subset was compared to matched patients not taking prokinetic agents. Prokinetic responders were defined based on symptom improvement greater than a minimum clinically important difference methodology. Key Results Forty‐two patients (88% female; median age 36; median BMI 26) taking prokinetics were analyzed. Prokinetic prescribing, compared to matched patients, was independent of BSGM metrics ( p > 0.15). In patients on existing prokinetics (withheld for BSGM), lower amplitudes predicted reduced symptom burden, whereas low rhythm stability predicted a worse symptom burden ( p < 0.05). In prokinetic‐naive patients (i.e., started on a prokinetic during the study), a lower postprandial amplitude predicted responders (mean 37.5 ± 10.6 uV in responders [ n = 5] vs. mean 54.8 ± 6.6 uV among nonresponders [ n = 3], p = 0.047). Conclusions Gastric Alimetry biomarkers may help in the prediction of prokinetic response in patients with chronic gastroduodenal symptoms. Lower postprandial amplitudes, indicating a reduced meal response, appear to predict benefit, while impaired rhythm stability predicted poorer therapeutic response.