Dr. Benoit Rizk Profile

Dr. Benoit Rizk

at Groupe 3R

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Publications (2)

Proceedings Article | 1 April 2024 Poster + Paper

Enhancing personalized contrast injection in computed tomography: clinical validation of a machine learning algorithm for accurate fat-free mass estimation

Natalie Heracleous, Hugues Brat, Benoit Dufour, Benoit Rizk, Cyril Thouly, Federica Zanca

Proceedings Volume 12925, 129251Y (2024) https://doi.org/10.1117/12.3005481

KEYWORDS: Data modeling, Performance modeling, Computed tomography, Liver, Cross validation, Algorithm development, Machine learning, Education and training, Linear regression, Algorithms

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To develop a Machine Learning (ML) model that accurately estimates the patient fat-free mass (FFM) in order to personalize intravenous (i.v.) contrast volume injection and ensure reproducible target liver enhancement. A dataset of previously collected abdominal CT data from 689 adult patients referred for liver lesion characterization or cancer follow-up was utilized. The data was obtained from eleven different radiology centers, utilizing various CT vendors, spanning the period from 2018 to 2022. The dataset encompasses diverse patient characteristics and measurements, including age, gender, weight, height, Body Mass Index (BMI), Size Specific Dose Estimate (SSDE), and FFM measured with a Bioelectrical Impedance meter. A multivariate linear regression model was developed to estimate FFM. The relationships between the investigated variables and the measured FFM were examined, and the most highly correlated variables were selected for inclusion in the final model. The dataset was divided into training and test sets according to the 80/20 rule and validated using the K-fold technique. We built several multivariate linear regression models and evaluated the performance of the trained model using the testing dataset against metrics such as the GT using Mean Absolute Percentage Error (MAPE), Root Mean Squared Error (RMSE), and R-squared. The cross-validation results for the best model reveal the model's robustness in typical patient profiles within our clinical settings. Specifically, the model exhibits low MAPE (0.033+/-0.003), high R2 (0.91+/-0.02), and relatively low standard deviation of residuals RMSE (2.13+/-0.23). External validation is being performed and preliminary results confirm the validity of the implementation of the theoretical FFM estimation in our personalized algorithm for contrast injection. Our model provides reliable and efficient estimation of patient FFM, facilitating the personalization of i.v. contrast volume in adult abdominal CT examinations while eliminating the need for expensive equipment.

Proceedings Article | 29 March 2024 Poster + Paper

Structured radiology report text analysis using natural language processing for automatic billing

Nicolas Devanthery, Natalie Heracleous, Benoit Dufour, Jean-Daniel Fardel, Benoit Rizk, Hugues Brat, Cyril Thouly, Henning Müller, Federica Zanca, Lluis Borràs Ferrís

Proceedings Volume 12929, 129290Y (2024) https://doi.org/10.1117/12.3005518

KEYWORDS: Ultrasonography, Abdomen, Algorithm development, Radiology, Doppler effect, Quality control, Medical imaging, Data modeling, Data mining, Tissues

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Purpose: The aim of this study was to develop an algorithm for automated quality control of structured radiology reports and to automatically obtain the correct invoicing codes for the performed exam. Ultrasound (US) exams of the abdomen were selected as use case, including Doppler exams. Method: To build a correct algorithm for automatic billing, the billing tree for the Ultrasound exams was studied. In Switzerland, TARMED, is the tariff structure used for billing outpatient medical services. The 4600 services listed in TARMED are divided into chapters that group together all services with a well-defined common characteristic. For example, Chapter 39 covers all medical imaging services. These chapters are further subdivided into subchapters for greater precision. Using this information a modular Natural Language Processing algorithm based on the Natural Language Toolkit (NLTK) library was developed. A second NLP algorithm based on SPACY was also developed, with the objective of a double validation of the first developed NLP algorithm. To train and test the algorithm a dataset of 170 exams corresponding to US abdominal examinations along with their radiology report were extracted from our RIS. The results of the algorithm were validated by an experienced technologists which identified possible discrepancy between the algorithm results and the correct billing. This check was carried out on a batch of data containing 95 samples. A confusion matrix was used to analyze the results. Results: In all 95 data samples, the NLTK algorithm was able to detect the billing codes correctly 100% of the time. In all our 95 data samples, the Spacy algorithm was able to detect the billing codes correctly in 86.3% of cases. This algorithm tends to overestimate the type of abdominal examination present in the report. Indeed, the 13 cases in which the algorithm made an error were cases where it detected a full abdominal ultrasound when the examination was a simple lower or upper abdomen. Conclusion: The NLTK model provides reliable and efficient estimation of billing codes for abdominal ultrasound, facilitating the task of the technologies who saves time and avoids possible human errors.

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