Child's nervous system : ChNS : official journal of the International Society for Pediatric Neurosurgery · 2024
Diagnostic support in pediatric craniopharyngioma using deep learning.
СтатьяПоддержка исследования: вне правительства СШАНазвание и аннотация: Перевод пока не подготовлен
Обработка статьи · этапы, ошибки и управление
Загрузка состояния…
Ошибки и ограничения
История · было → стало
Состояние обновляется каждые 5 секунд, пока блок открыт. Процент показывается только там, где известен объём работы. Пауза останавливает продолжение этапа; текущий запрос к источнику может завершиться.
Castiglioni Giovanni, Vallejos Joaquín, Intriago Jhon, Hernández María Isabel, Valenzuela Samuel, Fernández José, Castro Ignacio, Valenzuela Sergio, Estévez Pablo A, Okuma Cecilia
Аннотация
PURPOSE: We studied a pediatric group of patients with sellar-suprasellar tumors, aiming to develop a convolutional deep learning algorithm for radiological assistance to classify them into their respective cohort.
METHODS: T1w and T2w preoperative magnetic resonance images of 226 Chilean patients were collected at the Institute of Neurosurgery Dr. Alfonso Asenjo (INCA), which were divided into three classes: healthy control (68 subjects), craniopharyngioma (58 subjects) and differential sellar/suprasellar tumors (100 subjects).
RESULTS: The PPV among classes was 0.828±0.039, and the NPV was 0.919±0.063. Also explainable artificial intelligence (XAI) was used, finding that structures that are relevant during diagnosis and radiological evaluation highly influence the decision-making process of the machine.
CONCLUSION: This is the first experience of this kind of study in our institution, and it led to promising results on the task of radiological diagnostic support based on explainable artificial intelligence (AI) and deep learning models.
METHODS: T1w and T2w preoperative magnetic resonance images of 226 Chilean patients were collected at the Institute of Neurosurgery Dr. Alfonso Asenjo (INCA), which were divided into three classes: healthy control (68 subjects), craniopharyngioma (58 subjects) and differential sellar/suprasellar tumors (100 subjects).
RESULTS: The PPV among classes was 0.828±0.039, and the NPV was 0.919±0.063. Also explainable artificial intelligence (XAI) was used, finding that structures that are relevant during diagnosis and radiological evaluation highly influence the decision-making process of the machine.
CONCLUSION: This is the first experience of this kind of study in our institution, and it led to promising results on the task of radiological diagnostic support based on explainable artificial intelligence (AI) and deep learning models.