Potential use of deep learning techniques for postmortem imaging

The use of postmortem computed tomography in forensic medicine, in addition to conventional autopsy, is now a standard procedure in several countries. However, the large number of cases, the large amount of data, and the lack of postmortem radiology experts have pushed researchers to develop solutions that are able to automate diagnosis by applying deep learning techniques to postmortem computed tomography images. While deep learning techniques require a good understanding of image analysis and mathematical optimization, the goal of this review was to provide to the community of postmortem radiology experts the key concepts needed to assess the potential of such techniques and how they could impact their work.

Automated Diagnosis through Ontologies and Logical Descriptions

The combination of the burgeoning interest in efficient and reliable Health Systems and the advent of the Information Age represent both a challenge and an opportunity for new paradigms and cutting-edge technologies reaching a certain degree of maturity. Hence, the use of Semantic Technologies for Automated Diagnosis could leverage the potential of current solutions by providing inference-based knowledge and support on decision-making. This paper presents the ADONIS approach, which harnesses the use of ontologies and the underlying logical mechanisms to automate diagnosis and provide significant quality results in its evaluation on real-world data scenarios.

A Comprehensive Algorithm for the Analysis of ECG Waveforms

This paper presents a comprehensive approach for the detailed analysis of ECG waveforms including various morphologies to aid clinical diagnosis. Clinical judgment is often based on observing various features which may occur simultaneously on the ECG. Thus, to automate diagnosis, a comprehensive tool capable of detecting all these features is required. Parabolic curve fitting, adaptive thresholds and synchronicity across leads are utilized to detect the various waves of the QRS complex namely Q,R,S,R’ and S’. Onset of the QRS complex and the J point are detected using a ‘modified second derivative’ approach. The isoelectric level is detected using linearity and slope conditions. P and T waves are detected using ‘area under curve’ approach. Measurements such as peak-to-peak intervals and ST elevation/depression are numerically calculated from the points obtained. Curve fitting and change in slope are utilized for obtaining morphology of the ST segment. Presence of significant Q waves and abnormal T waves are inferred using clinical guidelines and numerical calculations. The performance of the algorithm is validated on 40 sample patient data — 20 healthy and 20 with Myocardial Infarction. Average accuracy shown in detecting all points of interest is 98.5%. All measurements are successfully calculated from these points. Along with this reliable performance, the approach proves to be simple and computationally fast.

Automated Diagnosis Through Ontologies and Logical Descriptions

The combination of the burgeoning interest in efficient and reliable Health Systems and the advent of the Information Age represent both a challenge and an opportunity for new paradigms and cutting-edge technologies reaching a certain degree of maturity. Hence, the use of Semantic Technologies for Automated Diagnosis could leverage the potential of current solutions by providing inference-based knowledge and support on decision-making. This paper presents the ADONIS approach, which harnesses the use of ontologies and the underlying logical mechanisms to automate diagnosis and provide significant quality results in its evaluation on real-world data scenarios.

A Virtual Reality-Based System for Hand Diagnosis and Rehabilitation

This article describes a new and unified approach to computerized hand diagnosis and rehabilitation. The system uses a workstation to automate diagnosis data collection and analysis and to assess the rehabilitation progress. A new diagnosis glove was developed and tested. This device measures grasping forces applied to 16 regions of the hand. A physician using this system can also utilize modern diagnosis devices such as electronic dynamometer, pinchmeter, and goniometer. Additionally, three VR rehabilitation exercises were created using WorldToolKit graphics library and run on the same workstation. These exercises were modeled after standard hand rehabilitation procedures and involve manipulation of virtual objects and transparent real-time data gathering. Grasping forces were modeled and fed back using the Rutgers Master worn on the patient's hand. An Oracle database was used to store, analyze, and integrate the patient's diagnosis and rehabilitation data. The system is presently undergoing clinical trials.