NINM Safety Parameter Explorer: a safety-reference tool for non-invasive neuromodulation
zana, y.; Fernandes, I. R.; Lopes, T. d. S.
Show abstract
Introduction: Safety parameters for non-invasive neuromodulation (NINM) remain scattered across consensus documents that differ in format, units, and scope, so pre-session parameter screening stays error-prone and difficult to audit. Objectives: To describe the NINM Safety Parameter Explorer, a browser-based tool that consolidates safety thresholds for six NINM modalities (e.g., TMS, tDCS, peripheral magnetic stimulation, TENS, transcranial ultrasound, and transcutaneous vagus nerve stimulation) into a single rule engine in which every numeric boundary carries an explicit, auditable provenance classification as literature-derived, literature-anchored, or operational default. Methods and Materials: We encoded the tool's rule corpus from published international consensus guidelines and peer-reviewed literature (82 modality threshold rules: 29 literature-derived, 36 literature-anchored, 17 operational defaults; complemented by 5 target-site caps for 87 total evaluative rules, alongside 15 informational reference values), then evaluated it in a simulated, automated internal verification suite of 38 example protocols spanning all six modalities and three expected verdicts, with automated static integrity assertions, regression guards, and behavioural and accessibility unit checks, all under a single pinned software version (v1.1.0-beta). Results: All 38 examples agreed with their declared verdicts (19 safe, 8 caution, 11 unsafe), and all static guards and supporting checks passed. Conclusion: The Explorer shows complete internal self-consistency between its rule corpus, engine, and interface, and its per-rule provenance labelling renders safety reasoning transparent and auditable for research and educational use. The tool is an in-silico protocol planning aid; it is not a medical device and is not intended for clinical decision-making or use in humans.
Matching journals
The top 10 journals account for 50% of the predicted probability mass.
Similar papers in this journal
Similar papers in this journal
- A Framework to Assess Clinical Safety and Hallucination Rates of LLMs for Medical Text Summarisation 90%
- How AI is used in FDA-authorized medical devices: a taxonomy across 1,016 authorizations 90%
- The clinician-AI interface: intended use and explainability in FDA-cleared AI devices for medical image interpretation 90%
Similar papers in this journal
- Analysis of clinical trial registry entry histories using the novel R package cthist 91%
- Comparison of TENS electrodes and textile electrodes for electrocutaneous warning 91%
- Exploring scalable assessment methods for terminated trials in ClinicalTrials.gov: A cohort analysis of German and Californian trials 91%
Similar papers in this journal
"Similar papers" are the closest papers from that journal in the model's embedding space. They show what the match is built on, but the ranking comes mostly from a classifier over the whole training set, not from these examples alone.