Predicting the Prognosis of MCI Patients Using Longitudinal MRI Data.

The aim of this study is to develop a computer-aided diagnosis system with a deep-learning approach for distinguishing "Mild Cognitive Impairment (MCI) due to Alzheimer's Disease (AD)" patients among a list of MCI patients. In this system we are using the power of longitudinal data extracted from magnetic resonance (MR). For this work, a total of 294 MCI patients were selected from the ADNI database. Among them, 125 patients developed AD during their follow-up and the rest remained stable. The proposed computer-aided diagnosis system (CAD) attempts to identify brain regions that are significant for the prediction of developing AD. The longitudinal data were constructed using a 3D Jacobian-based method aiming to track the brain differences between two consecutive follow-ups. The proposed CAD system distinguishes MCI patients who developed AD from those who remained stable with an accuracy of 87.2 percent. Moreover, it does not depend on data acquired by invasive methods or cognitive tests. This work demonstrates that the use of data in different time periods contains information that is beneficial for prognosis prediction purposes that outperform similar methods and are slightly inferior only to those systems that use invasive methods or neuropsychological tests.

Distinguishing age-related cognitive decline from dementias: A study based on machine learning algorithms.

BACKGROUND AND AIM: This study aims to examine the distinguishability of age-related cognitive decline (ARCD) from dementias based on some neurocognitive tests using machine learning. MATERIALS AND METHODS: 106 subjects were divided into four groups: ARCD (n=30), probable Alzheimer's disease (AD) (n=20), vascular dementia (VD) (n=21) and amnestic mild cognitive impairment (MCI) (n=35). The following tests were applied to all subjects: The Wechsler memory scale-revised, a clock-drawing, the dual similarities, interpretation of proverbs, word fluency, the Stroop, the Boston naming (BNT), the Benton face recognition, a copying-drawings and Öktem verbal memory processes (Ö-VMPT) tests. A multilayer perceptron, a support vector machine and a classification via regression with M5-model trees were employed for classification. RESULTS: The pairwise classification results show that ARCD is completely separable from AD with a success rate of 100% and highly separable from MCI and VD with success rates of 95.4% and 86.30%, respectively. The neurocognitive tests with the higher merit values were Ö-VMPT recognition (ARCD vs. AD), Ö-VMPT total learning (ARCD vs. MCI) and semantic fluency, proverbs, Stroop interference and naming BNT (ARCD vs. VD). CONCLUSION: The findings show that machine learning can be successfully utilized for distinguishing ARCD from dementias based on neurocognitive tests.

Status and potential clinical value of a transthoracic evaluation of the coronary arteries.

The growing need for coronary evaluation has raised interest in non-radioactive, non-invasive monitoring systems. In particular, radiation exposure during coronary investigations has been shown to be a possible cause of an enhanced risk of secondary tumors. Literature search has indicated that transthoracic echocardiography (TTE) has been widely applied to coronary arteries up to 2003, following which the lack of adequate equipment and the increased availability of invasive diagnostics, has reduced interest in this low cost, low-risk technology. The more recent availability of newer, more sensitive machines, allows evaluation of a larger number of arterial trees, including the aorta in newborns, the prenatal aortic intima-media thickness, as well as the detection of coronary artery anomalies in the adult. Improved technology for this highly operator sensitive technique may thus predict a possible evolution toward the clinical diagnostics of coronary disease and, eventually, also of the progression/regression of disease. We sought to evaluate the present status of this seldom quoted non-invasive technology.

A new reconstruction of Sts 14 pelvis (Australopithecus africanus) from computed tomography and three-dimensional modeling techniques.

The purpose of this study is to propose a new reconstruction of the australopithecine Sts 14 pelvis from original fossils. Digital models created from CT images allow us to perform mirroring operations, select valid regions after digital interposition, and reassemble parts. The key-element of the reconstruction is the sacroiliac joint, restored from right and left articular surfaces, which places of the pubic symphysis close to the sagittal plane. The complete pelvis is obtained by 3D model mirroring of hip-bone and sacrum. The present reconstruction of the Sts 14 pelvis is consistent with Schmid's (1983) [Folia Primatol. 40, 283-306, 1983] and Häusler and Schmid's A.L. 288-1 [J. Hum. Evol. 29, 363-383, 1995] pelvic reconstructions by illustrating a relatively platypelloid shape of the pelvic cavity and laterally inclined iliac blades. The pelvic morphology suggests that australopithecines had a less posteriorly tilted sacrum in erect posture than modern humans. As compared with Lovejoy's [Am. J. Phys. Anthropol. Suppl. 50, 460, 1979] A.L. 288-1 pelvic reconstruction, the less transversely flattened shape of the Sts 14 pelvic cavity led to obstetrical mechanics characterized as in humans by ante-ischiatic birth and a curved trajectory. We deduce a human-like movement of rotation and flexion of the fetal skull in the Sts 14 pelvic cavity.