Advancing epilepsy diagnosis: A meta-analysis of artificial intelligence approaches for interictal epileptiform discharge detection.

INTRODUCTION: Interictal epileptiform discharges (IEDs) in electroencephalograms (EEGs) are an important biomarker for epilepsy. Currently, the gold standard for IED detection is the visual analysis performed by experts. However, this process is expert-biased, and time-consuming. Developing fast, accurate, and robust detection methods for IEDs based on EEG may facilitate epilepsy diagnosis. We aim to assess the performance of deep learning (DL) and classic machine learning (ML) algorithms in classifying EEG segments into IED and non-IED categories, as well as distinguishing whether the entire EEG contains IED or not. METHODS: We systematically searched PubMed, Embase, and Web of Science following PRISMA guidelines. We excluded studies that only performed the detection of IEDs instead of binary segment classification. Risk of Bias was evaluated with Quality Assessment of Diagnostic Accuracy Studies (QUADAS-2). Meta-analysis with the overall area under the Summary Receiver Operating Characteristic (SROC), sensitivity, and specificity as effect measures, was performed with R software. RESULTS: A total of 23 studies, comprising 3,629 patients, were eligible for synthesis. Eighteen models performed discharge-level classification, and 6 whole-EEG classification. For the IED-level classification, 3 models were validated in an external dataset with more than 50 patients and achieved a sensitivity of 84.9 % (95 % CI: 82.3-87.2) and a specificity of 68.7 % (95 % CI: 7.9-98.2). Five studies reported model performance using both internal validation (cross-validation) and external datasets. The meta-analysis revealed higher performance for internal validation, with 90.4 % sensitivity and 99.6 % specificity, compared to external validation, which showed 78.1 % sensitivity and 80.1 % specificity. CONCLUSION: Meta-analysis showed higher performance for models validated with resampling methods compared to those using external datasets. Only a minority of models use more robust validation techniques, which often leads to overfitting.

Herramienta para la detección automática de nódulos pulmonares solitarios en series de imágenes de tomografía computarizada multicorte / A tool for automated detection of solitary pulmonary nodules in series of multicut computerized tomography images

Introducción: el nódulo pulmonar solitario es uno de los problemas más frecuentes en la práctica del radiólogo, que constituye un hallazgo incidental habitual en los estudios torácicos realizados durante el ejercicio clínico diario. Objetivo: implementar un sistema de diagnóstico asistido por computadora que facilite la detección del nódulo pulmonar solitario en las series de imágenes de tomografía computarizada multicorte. Métodos: se utilizó Matlab para el desarrollo y evaluación de un conjunto de algoritmos que constituyen elementos necesarios de un sistema de diagnóstico asistido por computadora. En orden: un algoritmo para la extracción de las regiones de interés, algoritmo para la extracción de características y un algoritmo de detección de nódulo pulmonar solitario para el cual se probaron varios clasificadores. La evaluación de los algoritmos fue efectuada en base a las anotaciones realizada por especialistas a la colección de imágenes LIDC-IDRI (Lung Image Database Consortium). Resultados: el método de segmentación empleado para extracción de las regiones de interés permitió generar la adecuada división de las imágenes originales en regiones significativas. El algoritmo utilizado en la detección mostró para el conjunto de prueba además de buena exactitud (de 96,4 por ciento), un buen balance de sensibilidad (91,5 por ciento) para una tasa de 0,84 falsos positivos por imagen. Conclusiones: el trabajo de investigación y la implementación realizada se reflejan en la construcción de una interfaz gráfica en Matlab como prototipo del sistema de diagnóstico asistido por computadora, con el que se puede contribuir a detectar más fácilmente el NPS(AU)

Introduction: solitary pulmonary nodules are one of the most frequent problems in radiographic practice. They are a common incidental finding in chest studies conducted during routine clinical work. Objective: implement a computer-assisted diagnostic system facilitating detection of solitary pulmonary nodules in multicut computerized tomography image series. Methods: Matlab was used to develop and evaluate a set of algorithms constituting necessary components of a computer-assisted diagnostic system. The order was the following: an algorithm to extract regions of interest, another to extract characteristics, and another to detect solitary pulmonary nodules, for which several classifiers were tested. Evaluation of the algorithms was based on notes taken by specialists on the LIDC-IDRI (Lung Image Database Consortium) image collection. Results: the segmentation method used for extraction of regions of interest made it possible to create a suitable division of the original images into significant regions. The algorithm used for detection found that the test set exhibited good accuracy (96.4%), a good sensitivity balance (91.5%), and a 0.84 rate of false positives per image. Conclusions: the research and implementation work done is reflected in the construction of a Matlab graphic interface serving as a prototype for a computer-assisted diagnostic system which may facilitate detection of SPNs.

Objective detection of apoptosis in rat renal tissue sections using light microscopy and free image analysis software with subsequent machine learning: Detection of apoptosis in renal tissue.

OBJECTIVE: The current study proposes an automated machine learning approach for the quantification of cells in cell death pathways according to DNA fragmentation. METHODS: A total of 17 images of kidney histological slide samples from male Wistar rats were used. The slides were photographed using an Axio Zeiss Vert.A1 microscope with a 40x objective lens coupled with an Axio Cam MRC Zeiss camera and Zen 2012 software. The images were analyzed using CellProfiler (version 2.1.1) and CellProfiler Analyst open-source software. RESULTS: Out of the 10,378 objects, 4970 (47,9%) were identified as TUNEL positive, and 5408 (52,1%) were identified as TUNEL negative. On average, the sensitivity and specificity values of the machine learning approach were 0.80 and 0.77, respectively. CONCLUSION: Image cytometry provides a quantitative analytical alternative to the more traditional qualitative methods more commonly used in studies.