**Author Listing - Lead/ Submitting Author (we will be communicating with this individual): Name, Institution, City and State where the work was done. Co-authors will be added in a separate section.**
Nicolas Hugo Quiroz; Maria Victoria Anconetani; Marcelina Carretero; Delfina Cirelli; Erika Bárbara Brulc; María Adela Aguirre; Gabriela Alejandra Blugerman; Elsa Mercedes Nucifora; Marcelo Raul Risk; María Lourdes Posadas Martínez.
Submitting Author: Delfina Cirelli. Hospital Italiano de Buenos Aires. Ciudad Autónoma de Buenos Aires. Argentina.

**Main Body of the Abstract**
Background: Early detection of systemic amyloidosis is crucial for prognosis and quality of life. Artificial intelligence (AI) predictive models offer the potential for timely identification. Studies using medical records for diagnostic algorithms are lacking.
Objective: To develop an AI-driven predictive model for systemic amyloidosis identification.
Materials & methods: Dynamic retrospective cohort study. All adult patients affiliated to the Medical Care Program at the Hospital Italiano de Buenos Aires were included. For each case of amyloidosis, up to four controls without amyloidosis of the same age, gender, and affiliation period were matched. A retrospective collection of all available information was conducted for each case and their respective controls spanned from six months before diagnosis. The collected data included demographic, clinical, laboratory, and imaging information. Seven machine-learning models, employing resampling techniques, were applied to training and testing cohorts with internal validation through cross-validation.
Results: Among 961 patients, 218 cases and 743 controls met the selection criteria. The mean age was 65 years, with 63% male. The predictive variables that best identify amyloidosis included carpal tunnel syndrome history, arrhythmia, abnormalities in free light chains, and imaging. When assessing performance details, the random forest and decision tree models exhibited the best AUC (over 90%) consistently in both cohorts.
Summary & conclusion: Medical record features demonstrated strong performance in early amyloidosis identification using the developed AI-driven model, particularly with the random forest and decision tree algorithms.



**Author Listing - Lead/ Submitting Author (we will be communicating with this individual): Name, Institution, City and State where the work was done. Co-authors will be added in a separate section.**
Nicolas Hugo Quiroz; Maria Victoria Anconetani; Marcelina Carretero; Delfina Cirelli; Erika Bárbara Brulc; María Adela Aguirre; Gabriela Alejandra Blugerman; Elsa Mercedes Nucifora; Marcelo Raul Risk; María Lourdes Posadas Martínez.
Submitting Author: Delfina Cirelli. Hospital Italiano de Buenos Aires. Ciudad Autónoma de Buenos Aires. Argentina.

**Main Body of the Abstract**
**Background:** Early detection of systemic amyloidosis is crucial for prognosis and quality of life. Artificial intelligence (AI) predictive models offer the potential for timely identification. Studies using medical records for diagnostic algorithms are lacking.
**Objective:** To develop an AI-driven predictive model for systemic amyloidosis identification.
**Materials & methods:** Dynamic retrospective cohort study. All adult patients affiliated to the Medical Care Program at the Hospital Italiano de Buenos Aires were included. For each case of amyloidosis, up to four controls without amyloidosis of the same age, gender, and affiliation period were matched. A retrospective collection of all available information was conducted for each case and their respective controls spanned from six months before diagnosis. The collected data included demographic, clinical, laboratory, and imaging information. Seven machine-learning models, employing resampling techniques, were applied to training and testing cohorts with internal validation through cross-validation.
**Results:** Among 961 patients, 218 cases and 743 controls met the selection criteria. The mean age was 65 years, with 63% male. The predictive variables that best identify amyloidosis included carpal tunnel syndrome history, arrhythmia, abnormalities in free light chains, and imaging. When assessing performance details, the random forest and decision tree models exhibited the best AUC (over 90%) consistently in both cohorts.
**Summary & conclusion:** Medical record features demonstrated strong performance in early amyloidosis identification using the developed AI-driven model, particularly with the random forest and decision tree algorithms.



Artificial intelligence for the detection of systemic amyloidosis PA3 (#105)

We invite you to attend the XIX International Symposium on Amyloidosis to be held in Rochester, MN.

This symposium is organized as a hybrid meeting (with the focus on in-person) held from Sunday, May 26 through Thursday, May 30, 2024.

Together with the ISA Board and members of Mayo Clinic, a very attractive scientific hybrid program is being planned. The program will cover a variety of types of systemic amyloidosis: ATTR (hereditary and wild-type), AL (light-chain), AA and less frequent forms. There will be key lectures, specific symposia including those company sponsored, oral presentations and poster sessions of accepted abstracts.

COURSE DESCRIPTION
This program will provide a comprehensive review of state-of-the-art education in systemic amyloidosis: basic science, pathophysiology, diagnosis, current and emerging treatments. Scientific presentations will include competitive oral and poster abstracts. The rapid evolution of scientific discovery and expanding treatment options has created tremendous interest in the field and this meeting will fill and important gap in educating scientists and clinicians.