{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,8,13]],"date-time":"2026-08-13T04:30:42Z","timestamp":1786595442818,"version":"build-2736575974"},"reference-count":30,"publisher":"Frontiers Media SA","license":[{"start":{"date-parts":[[2026,8,13]],"date-time":"2026-08-13T00:00:00Z","timestamp":1786579200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":["frontiersin.org"],"crossmark-restriction":true},"short-container-title":["Front. Endocrinol."],"abstract":"<jats:p>Hypothyroidism, most commonly caused by Hashimoto\u2019s thyroiditis, is characterized by insufficient production of thyroid hormones and is typically treated with oral levothyroxine (LT4). However, determining patient-specific LT4 dosage remains challenging due to significant inter-individual variability and the need for repeated clinical adjustments. In this study, a stability-aware, data-driven dosing framework is developed for personalized LT4 therapy. The proposed approach combines a Long Short-Term Memory (LSTM) neural network for predicting free thyroxine (FT4) concentrations with a model predictive control (MPC) strategy for dose optimization. To ensure safe and reliable operation, Input-to-State Stability (ISS) conditions are incorporated within the control framework, ensuring boundedness of the system states and stability of the closed-loop system. The method is evaluated using simulated patients generated with Thyrosim, a validated mathematical simulator of thyroid hormone regulation, under varying biological parameters. Simulation results demonstrate that the proposed framework regulates FT4 concentrations toward patient-specific euthyroid levels during the initial phase of treatment and maintains stable hormone levels throughout the simulation period. Furthermore, consistent performance across a heterogeneous population of simulated patients highlights the robustness of the approach. These findings suggest that the proposed ISS-LSTM-MPC framework provides a promising foundation for automated, personalized LT4 dosing and represents a step toward clinically applicable decision-support systems for thyroid hormone therapy.<\/jats:p>","DOI":"10.3389\/fendo.2026.1894583","type":"journal-article","created":{"date-parts":[[2026,8,13]],"date-time":"2026-08-13T04:24:18Z","timestamp":1786595058000},"update-policy":"https:\/\/doi.org\/10.3389\/crossmark-policy","source":"Crossref","is-referenced-by-count":0,"title":["Stability-aware LSTM\u2013MPC framework for personalized levothyroxine dosing in Hashimoto\u2019s thyroiditis"],"prefix":"10.3389","volume":"17","author":[{"given":"Ravi","family":"Sharma","sequence":"first","affiliation":[{"name":"Institute of Automation and Control, Graz University of Technology","place":["Graz, Austria"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Uwe","family":"Riedmann","sequence":"additional","affiliation":[{"name":"Division of Endocrinology and Diabetology, Department of Internal Medicine, Medical University of Graz","place":["Graz, Austria"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Stefan","family":"Pilz","sequence":"additional","affiliation":[{"name":"Division of Endocrinology and Diabetology, Department of Internal Medicine, Medical University of Graz","place":["Graz, Austria"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Markus","family":"Reichhartinger","sequence":"additional","affiliation":[{"name":"Institute of Automation and Control, Graz University of Technology","place":["Graz, 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