Objective Diagnosis of Fibromyalgia Using Neuroretinal Evaluation and Artificial Intelligence / Diagnóstico objetivo de fibromialgia analizando la neurorretina con inteligencia artificial

ResumenAntecedentes/Objetivo: Identificar biomarcadores objetivos de fibromialgia (FM) apli-cando inteligencia artificial a datos estructurales de retina obtenidos mediante tomogra-fía de coherencia óptica Swept Source (TCO-SS). Método: Se evaluó una cohorte de 29 pacientes con FM y otra de 32 sujetos control, registrando los espesores de la retina completa, de varias capas de la retina [capa de células ganglionares (CCG+), CCG amplia-da (CCG++, entre la membrana limitante interna y los límites de la capa nuclear interna) y capa de fibras nerviosas (CFNR)] y de la coroides, mediante TCO-SS. La capacidad dis-criminante se evaluó mediante el área bajo la curva ROC (AROC) y el algoritmo Relief. Se implementó un sistema de ayuda al diagnóstico con clasificador automático. Resultados: No se observó diferencia significativa (p ≥ 0,660) en la coroides, pero sí en el sector in-ferior del anillo interno de la CFNR (p = 0,010) y en los cuatro sectores del anillo interno en las capas CCG+, CCG++ y retina completa. Utilizando un árbol de decisión ensemble RUSBoosted como clasificador de las características con mayor capacidad discriminante, se obtuvo una predicción alta (AROC = 0,820). Conclusiones: Se identifica un potencial biomarcador objetivo y no invasivo para el diagnóstico de FM basado en el análisis de la neurorretina mediante TCO-SS.

AbstractBackground/Objective: This study aims to identify objective biomarkers of fibromyalgia (FM) by applying artificial intelligence algorithms to structural data on the neuroretina obtained using swept-source optical coherence tomography (SS-OCT). Method: The study cohort comprised 29 FM patients and 32 control subjects. The thicknesses of complete retina, 3 retinal layers [ganglion cell layer (GCL+), GCL++ (between the inner limiting membrane and the inner nuclear layer boundaries) and retinal nerve fiber layer (RNFL)] and choroid in 9 areas around the macula were obtained using SS-OCT. Discriminant capacity was evaluated using the area under the curve (AUC) and the Relief algorithm. A diagnostic aid system with an automatic classifier was implemented. Results: No significant difference (p ≥ .660) was found anywhere in the choroid. In the RNFL, a significant difference was found in the inner inferior region (p = .010). In the GCL+, GCL++ layers and complete retina, a significant difference was found in the 4 regions defining the inner ring: temporal, superior, nasal and inferior. Applying an ensemble RUSBoosted tree classifier to the features with greatest discriminant capacity achieved accuracy = .82 and AUC = .82. Conclusions: This study identifies a potential novel objective and non-invasive biomarker of FM based on retina analysis using SS-OCT.

Objective Diagnosis of Fibromyalgia Using Neuroretinal Evaluation and Artificial Intelligence.

Background/Objective: This study aims to identify objective biomarkers of fibromyalgia (FM) by applying artificial intelligence algorithms to structural data on the neuroretina obtained using swept-source optical coherence tomography (SS-OCT). Method: The study cohort comprised 29 FM patients and 32 control subjects. The thicknesses of complete retina, 3 retinal layers [ganglion cell layer (GCL+), GCL++ (between the inner limiting membrane and the inner nuclear layer boundaries) and retinal nerve fiber layer (RNFL)] and choroid in 9 areas around the macula were obtained using SS-OCT. Discriminant capacity was evaluated using the area under the curve (AUC) and the Relief algorithm. A diagnostic aid system with an automatic classifier was implemented. Results: No significant difference (p ≥ .660) was found anywhere in the choroid. In the RNFL, a significant difference was found in the inner inferior region (p = .010). In the GCL+, GCL++ layers and complete retina, a significant difference was found in the 4 regions defining the inner ring: temporal, superior, nasal and inferior. Applying an ensemble RUSBoosted tree classifier to the features with greatest discriminant capacity achieved accuracy = .82 and AUC = .82. Conclusions: This study identifies a potential novel objective and non-invasive biomarker of FM based on retina analysis using SS-OCT.

Antecedentes/Objetivo: Identificar biomarcadores objetivos de fibromialgia (FM) aplicando inteligencia artificial a datos estructurales de retina obtenidos mediante tomografía de coherencia óptica Swept Source (TCO-SS). Método: Se evaluó una cohorte de 29 pacientes con FM y otra de 32 sujetos control, registrando los espesores de la retina completa, de varias capas de la retina [capa de células ganglionares (CCG+), CCG ampliada (CCG++, entre la membrana limitante interna y los límites de la capa nuclear interna) y capa de fibras nerviosas (CFNR)] y de la coroides, mediante TCO-SS. La capacidad discriminante se evaluó mediante el área bajo la curva ROC (AROC) y el algoritmo Relief. Se implementó un sistema de ayuda al diagnóstico con clasificador automático. Resultados: No se observó diferencia significativa (p ≥ .660) en la coroides, pero sí en el sector inferior del anillo interno de la CFNR (p = .010) y en los cuatro sectores del anillo interno en las capas CCG+, CCG++ y retina completa. Utilizando un árbol de decisión ensemble RUSBoosted como clasificador de las características con mayor capacidad discriminante, se obtuvo una predicción alta (AROC=.820). Conclusiones: Se identifica un potencial biomarcador objetivo y no invasivo para el diagnóstico de FM basado en el análisis de la neurorretina mediante TCO-SS.

Automated detection of breast cancer in thermal infrared images, based on independent component analysis.

Breast cancer, among women, is the second-most common cancer and the leading cause of cancer death. It has become a major health issue in the world over the past decades and its incidence has increased in recent years mostly due to increased awareness of the importance of screening and population ageing. Early detection is crucial in the effective treatment of breast cancer. Current mammogram screening may turn up many tiny abnormalities that are either not cancerous or are slow-growing cancers that would never progress to the point of killing a woman and might never even become known to her. Ideally a better screening method should find a way of distinguishing the dangerous, aggressive tumors that need to be excised from the more languorous ones that do not. This paper therefore proposes a new method of thermographic image analysis for automated detection of high tumor risk areas, based on independent component analysis (ICA) and on post-processing of the images resulting from this algorithm. Tests carried out on a database enable tumor areas of 4 × 4 pixels on an original thermographic image to be detected. The proposed method has shown that the appearance of a heat anomaly indicating a potentially cancerous zone is reflected as an independent source by ICA analysis of the YCrCb components; the set of available images in our small series is giving us a sensitivity of 100% and a specificity of 94.7%.

Data acquisition, analysis and transmission platform for a Pay-As-You-Drive system.

This paper presents a platform used to acquire, analyse and transmit data from a vehicle to a Control Centre as part of a Pay-As-You-Drive system. The aim is to monitor vehicle usage (how much, when, where and how) and, based on this information, assess the associated risk and set an appropriate insurance premium. To determine vehicle usage, the system analyses the driver's respect for speed limits, driving style (aggressive or non-aggressive), mobile telephone use and the number of vehicle passengers. An electronic system on board the vehicle acquires these data, processes them and transmits them by mobile telephone (GPRS/UMTS) to a Control Centre, at which the insurance company assesses the risk associated with vehicles monitored by the system. The system provides insurance companies and their customers with an enhanced service and could potentially increase responsible driving habits and reduce the number of road accidents.