ARTMAP-IC and Medical Diagnosis: Instance Counting and Inconsistent Cases

For complex database prediction problems such as medical diagnosis, the ARTMAP-IC neural network adds distributed prediction and category instance counting to the basic fuzzy ARTMAP system. For the ARTMAP match tracking algorithm, which controls search following a predictive error, a new version facilitates prediction with sparse or inconsistent data. Compared to the original match tracking algorithm (MT+), the new algorithm (MT-) better approximates the real-time network differential equations and further compresses memory without loss of performance. Simulations examine predictive accuracy on four medical databases: Pima Indian diabetes, breast cancer, heart disease, and gall bladder removal. ARTMAP-IC results arc equal to or better than those of logistic regression, K nearest neighbor (KNN), the ADAP perceptron, multisurface pattern separation, CLASSIT, instance-based (IBL), and C4. ARTMAP dynamics are fast, stable, and scalable. A voting strategy improves prediction by training the system several times on different orderings of an input set. Voting, instance counting, and distributed representations combine to form confidence estimates for competing predictions.National Science Foundation (IRI 94-01659); Office of Naval Research (N00014-95-J-0409, N00014-95-0657

Misleading measures in Vitamin D analysis: a novel LC-MS/MS assay to account for epimers and isobars

Background Recently, the accuracies of many commercially available immunoassays for Vitamin D have been questioned. Liquid chromatography tandem mass spectrometry (LC- MS/MS) has been shown to facilitate accurate separation and quantification of the major circulating metabolite 25-hydroxyvitamin-D3 (25OHD3) and 25-hydroxyvitamin-D2 (25OHD2) collectively termed as 25OHD. However, among other interferents, this method may be compromised by overlapping peaks and identical masses of epimers and isobars, resulting in inaccuracies in circulating 25OHD measurements. The aim of this study was to develop a novel LC-MS/MS method that can accurately identify and quantitate 25OHD3 and 25OHD2 through chromatographic separation of 25OHD from its epimers and isobars. Methods A positive ion electrospray ionisation (ESI) LC-MS/MS method was used in the Multiple Reaction Monitoring (MRM) mode for quantification. It involved i) liquid-liquid extraction, ii) tandem columns (a high resolution ZORBAX C18 coupled to an ULTRON chiral, with guard column and inlet filter), iii) Stanozolol-D3 as internal standard, and iv) identification via ESI and monitoring of three fragmentation transitions. To demonstrate the practical usefulness of our method, blood samples were collected from 5 healthy male Caucasian volunteers; age range 22 to 37 years and 25OHD2, 25OHD3 along with co-eluting epimers and analogues were quantified. Results The new method allowed chromatographic separation and quantification of 25OHD2, 25OHD3, along with 25OHD3 epimer 3-epi-25OHD3 and isobars 1-alpha-hydroxyvitamin-D3 (1alphaOHD3), and 7-alpha-hydroxy-4-cholesten-3-one (7alphaC4). The new assay was capable of detecting 0.25 ng/mL of all analytes in serum. Conclusions To our knowledge, this is the first specific, reliable, reproducible and robust LC-MS/MS method developed for the accurate detection of 25OHD (Vitamin D). The method is capable of detecting low levels of 25OHD3 and 25OHD2 together with chromatographic separation from the co-eluting epimers and isobars and circumvents other instrumental/analytical interferences. This analytical method does not require time-consuming derivatisation and complex extraction techniques and could prove very useful in clinical studies

Una revisión sistemática de métodos de aprendizaje profundo aplicados a imágenes oculares

Artificial intelligence is having an important effect on different areas of medicine, and ophthalmology has not been the exception. In particular, deep learning methods have been applied successfully to the detection of clinical signs and the classification of ocular diseases. This represents a great potential to increase the number of people correctly diagnosed. In ophthalmology, deep learning methods have primarily been applied to eye fundus images and optical coherence tomography. On the one hand, these methods have achieved an outstanding performance in the detection of ocular diseases such as: diabetic retinopathy, glaucoma, diabetic macular degeneration and age-related macular degeneration.  On the other hand, several worldwide challenges have shared big eye imaging datasets with segmentation of part of the eyes, clinical signs and the ocular diagnostic performed by experts. In addition, these methods are breaking the stigma of black-box models, with the delivering of interpretable clinically information. This review provides an overview of the state-of-the-art deep learning methods used in ophthalmic images, databases and potential challenges for ocular diagnosisLa inteligencia artificial está teniendo un importante impacto en diversas áreas de la medicina y a la oftalmología no ha sido la excepción. En particular, los métodos de aprendizaje profundo han sido aplicados con éxito en la detección de signos clínicos y la clasificación de enfermedades oculares. Esto representa un potencial impacto en el incremento de pacientes correctamente y oportunamente diagnosticados. En oftalmología, los métodos de aprendizaje profundo se han aplicado principalmente a imágenes de fondo de ojo y tomografía de coherencia óptica. Por un lado, estos métodos han logrado un rendimiento sobresaliente en la detección de enfermedades oculares tales como: retinopatía diabética, glaucoma, degeneración macular diabética y degeneración macular relacionada con la edad. Por otro lado, varios desafíos mundiales han compartido grandes conjuntos de datos con segmentación de parte de los ojos, signos clínicos y el diagnóstico ocular realizado por expertos. Adicionalmente, estos métodos están rompiendo el estigma de los modelos de caja negra, con la entrega de información clínica interpretable. Esta revisión proporciona una visión general de los métodos de aprendizaje profundo de última generación utilizados en imágenes oftálmicas, bases de datos y posibles desafíos para los diagnósticos oculare