Standardized evaluation of algorithms for computer-aided diagnosis of dementia based on structural MRI: The CADDementia challenge

Algorithms for computer-aided diagnosis of dementia based on structural MRI have demonstrated high performance in the literature, but are difficult to compare as different data sets and methodology were used for evaluation. In addition, it is unclear how the algorithms would perform on previously unseen data, and thus, how they would perform in clinical practice when there is no real opportunity to adapt the algorithm to the data at hand. To address these comparability, generalizability and clinical applicability issues, we organized a grand challenge that aimed to objectively compare algorithms based on a clinically representative multi-center data set. Using clinical practice as the starting point, the goal was to reproduce the clinical diagnosis. Therefore, we evaluated algorithms for multi-class classification of three diagnostic groups: patients with probable Alzheimer's disease, patients with mild cognitive impairment and healthy controls. The diagnosis based on clinical criteria was used as reference standard, as it was the best available reference despite its known limitations. For evaluation, a previously unseen test set was used consisting of 354 T1-weighted MRI scans with the diagnoses blinded. Fifteen research teams participated with a total of 29 algorithms. The algorithms were trained on a small training set (n = 30) and optionally on data from other sources (e.g., the Alzheimer's Disease Neuroimaging Initiative, the Australian Imaging Biomarkers and Lifestyle flagship study of aging). The best performing algorithm yielded an accuracy of 63.0% and an area under the receiver-operating-characteristic curve (AUC) of 78.8%. In general, the best performances were achieved using feature extraction based on voxel-based morphometry or a combination of features that included volume, cortical thickness, shape and intensity. The challenge is open for new submissions via the web-based framework: http://caddementia.grand-challenge.org.

Computer-aided Structure Elucidation of Neolignans

This paper describes a new module of the expert system SISTEMAT used for the prediction of the skeletons of neolignans by (13)C NMR, (1)H NMR and botanical data obtained from the literature. SISTEMAT is composed of MACRONO, SISCONST, C13MACH, H1MACH and SISOCBOT programs, each analyzing data of the neolignan in question to predict the carbon skeleton of the compound. From these results, the global probability is computed and the most probable skeleton predicted. SISTEMAT predicted the skeletons of 75% of the 20 neolignans tested, in a rapid and simple procedure demonstrating its advantage for the structural elucidation of new compounds.

Computer-aided design decision support: interfacing knowledge and information

Computer-aided design decision support has proved to be an elusive and intangible project for many researchers as they seek to encapsulate information and knowledge-based systems as useful multifunctional data structures. Definitions of ‘knowledge', ‘information', ‘facts', and ‘data' become semantic footballs in the struggle to identify what designers actually do, and what level of support would suit them best, and how that support might be offered. The Construction Primer is a database-drivable interactive multimedia environment that provides readily updated access to many levels of information aimed to suit students and practitioners alike. This is hardly a novelty in itself. The innovative interface and metadata structures, however, combine with the willingness of national building control legislators, standards authorities, materials producers, building research organisations, and specification services to make the Construction Primer a versatile design decision support vehicle. It is both compatible with most working methodologies while remaining reasonably future-proof. This paper describes the structure of the project and highlights the importance of sound planning and strict adhesion to library-standard metadata protocols as a means to avoid the support becoming too specific or too paradigmatic.

Computer aided ultrasonic thickness strain analysis of automotive sheet metal stampings using FEA information

This paper discusses a computer-aided methodology for thickness strain analysis (TSA) of sheet metal stampings using geometry and strain information that is extracted from finite element analysis (FEA) results. The system utilises both FEA results and an ultrasonic gauge capability expert system to assist press shop personnel, providing them with capabilities such as optimum measurement point location and an estimate of gauge error. Key advantages of this enhanced TSA methodology are related to overall efficiency and accuracy gains.

Computer aided analysis of dental radiographic images

This paper is a result of a fruitful cooperation between the computer science and the dental diagnosis experiences. The study presents a new approach of applying computer algorithms to radiographic images of dental implantation used for bone regeneration. We focus here only on the contribution of the computer assistance to the clinical research as the periodontal therapy is beyond the scope of this paper. The proposed system is based on a pattern recognition approach, directed to recognize density changes in the intra-bony affected areas of patients. It comprises different modules with new algorithms specially designed to treat the patients’ radiographic images more accurately. The system includes digitizing, detecting the complicated region of interest (ROI), defining reference area to correct any projection discrepancy of the follow up images, and finally to extract the distinguishing features of the ROI as a basis for determining the rate of new bone density accumulation. This study is applied to two typical dental cases for a patient who received two different operations. The results are very encouraging and more accurate than traditional techniques reported before.

Early breast cancer identification : Which way to go? Microarray or image based computer aided diagnosis!

The goal of this research is to develop a computer aided diagnostic (CAD) system that can detect breast cancer in the early stage by using microarray and image data. We verified the performance of six well known classification algorithms with various performance matrices. Although we do not suggest a unique classifier algorithm for a CAD system, we do identify a number of algorithms whose performance is very promising. The algorithms performance was validated by 3 images dataset; two have been used for the first time in this experiment. Multidimensional image filtering is adopted for the final data extraction. The image data classification performance is compared with microarray data. Results suggest the most effective means of breast cancer identification in the early stage is a hybrid approach.

Zebra fingerprints : towards a computer-aided identification system for individual zebra

Information regarding wildlife behavioural patterns provides researchers with clearer understanding of species. Social interactions, breeding habits, feeding and migration trends are key factors influencing decisions in game management and ecological impact assessment. In order to obtain such information researchers must be able to track individual animals over a period of time. One of the commonest methods of identifying individual animals is to tag them with radio, satellite, or GPS transmitters (Mech & Barber, 2002). While these systems are very effective, they are often costly and can be traumatic for tagged animals.

Lab-on-a-Chip turns soft : computer-aided, software-enabled microfluidics design

The current practice of designing microfluidic Lab-on-a-Chip (LoCs) limits reusing designs and makes sharing tasks among researchers difficult. One way to achieve that objective is to borrow best practices from engineering. Also it takes a lot of skills to design LoCs. Design-by-assembly in which a LoC can be designed by configuring, laying out subsystems can help new researchers to develop custom chips. Flexible, reusable, and rapid-prototyping-feasible LoC designs can be achieved by fabricated modular microfluidic blocks. However, challenging problems still persist, which limit the usefulness of prefabricated blocks. We propose software microfluidic modules (SoftMABs) based design technique to solve issues fabricated modules face. By configuring SoftMABs, integrating them, the new assembly of SoftMABs can form a 3D LoC design ready to be prototyped. The proposed method can make designing a complex LoC less challenging, and collaborating among laboratories easier. We created SoftMABs and designed a custom microfluidic chip by assembling SoftMABs like LEGOs, dragging-and-dropping them. Later we reconfigured them - by replacing a SoftMAB with another module - to make a new LoC. We believe this computeraided method is an interesting and useful LoC design technique.

Computer-aided design of a novel ligand for retinoic acid receptor in cancer chemotherapy

The isotypes of RAR and RXR are retinoic acid and retinoid X acid receptors, respectively, whose ligand-binding domain contains the ligand-dependent activation function, with distinct pharmacological targets for retinoids, involved in the treatment of various cancers and skin diseases. Due to the major challenge which cancer treatment and cure still imposes after many decades to the international scientific community, there is actually considerable interest in new ligands with increased bioactivity. We have focused on the retinoid acid receptor, which is considered an interesting target for drug design. In this work, we carried out density functional geometry optimizations, and different docking procedures. We performed screening in a large database (hundreds of thousands of molecules which we optimized at the AM1 level) yielding a set of potential bioactive ligands. A new ligand was selected and optimized at the B3LYP/6-31G* level. A flexible docking program was used to investigate the interactions between the receptor and the new ligand. The result of this work is compared with several crystallographic ligands of RAR. Our theoretically more bioactive new-ligand indicates stronger and more hydrogen bonds as well as hydrophobic interactions with the receptor. (c) 2005 Wiley Periodicals, Inc.

The cross-entropy method and its application to minimize the ripple of magnetic levitation forces of a maglev system

This paper explores firstly the potential of a new evolutionary method - the Cross-Entropy (CE) method in solving continuous inverse electromagnetic problems. For this purpose, an adaptive updating formula for the smoothing parameter, some mutation operation, and a new termination criterion are proposed. The proposed CE based metaheuristics is applied to reduce the ripple of the magnetic levitation forces of a prototype Maglev system. The numerical results have shown that the ripple of the magnetic levitation forces of the prototype system is reduced significantly after the design optimization using the proposed algorithm.