Examining feature extraction and classification modules in machine learning for diagnosis of low-dose computed tomographic screening-detected in vivo lesions.

Purpose: Medical imaging-based machine learning (ML) for computer-aided diagnosis of in vivo lesions consists of two basic components or modules of (i) feature extraction from non-invasively acquired medical images and (ii) feature classification for prediction of malignancy of lesions detected or localized in the medical images. This study investigates their individual performances for diagnosis of low-dose computed tomography (CT) screening-detected lesions of pulmonary nodules and colorectal polyps. Approach: Three feature extraction methods were investigated. One uses the mathematical descriptor of gray-level co-occurrence image texture measure to extract the Haralick image texture features (HFs). One uses the convolutional neural network (CNN) architecture to extract deep learning (DL) image abstractive features (DFs). The third one uses the interactions between lesion tissues and X-ray energy of CT to extract tissue-energy specific characteristic features (TFs). All the above three categories of extracted features were classified by the random forest (RF) classifier with comparison to the DL-CNN method, which reads the images, extracts the DFs, and classifies the DFs in an end-to-end manner. The ML diagnosis of lesions or prediction of lesion malignancy was measured by the area under the receiver operating characteristic curve (AUC). Three lesion image datasets were used. The lesions' tissue pathological reports were used as the learning labels. Results: Experiments on the three datasets produced AUC values of 0.724 to 0.878 for the HFs, 0.652 to 0.965 for the DFs, and 0.985 to 0.996 for the TFs, compared to the DL-CNN of 0.694 to 0.964. These experimental outcomes indicate that the RF classifier performed comparably to the DL-CNN classification module and the extraction of tissue-energy specific characteristic features dramatically improved AUC value. Conclusions: The feature extraction module is more important than the feature classification module. Extraction of tissue-energy specific characteristic features is more important than extraction of image abstractive and characteristic features.

Decoding articulatory and phonetic components of naturalistic continuous speech from the distributed language network.

Objective.The speech production network relies on a widely distributed brain network. However, research and development of speech brain-computer interfaces (speech-BCIs) has typically focused on decoding speech only from superficial subregions readily accessible by subdural grid arrays-typically placed over the sensorimotor cortex. Alternatively, the technique of stereo-electroencephalography (sEEG) enables access to distributed brain regions using multiple depth electrodes with lower surgical risks, especially in patients with brain injuries resulting in aphasia and other speech disorders.Approach.To investigate the decoding potential of widespread electrode coverage in multiple cortical sites, we used a naturalistic continuous speech production task. We obtained neural recordings using sEEG from eight participants while they read aloud sentences. We trained linear classifiers to decode distinct speech components (articulatory components and phonemes) solely based on broadband gamma activity and evaluated the decoding performance using nested five-fold cross-validation.Main Results.We achieved an average classification accuracy of 18.7% across 9 places of articulation (e.g. bilabials, palatals), 26.5% across 5 manner of articulation (MOA) labels (e.g. affricates, fricatives), and 4.81% across 38 phonemes. The highest classification accuracies achieved with a single large dataset were 26.3% for place of articulation, 35.7% for MOA, and 9.88% for phonemes. Electrodes that contributed high decoding power were distributed across multiple sulcal and gyral sites in both dominant and non-dominant hemispheres, including ventral sensorimotor, inferior frontal, superior temporal, and fusiform cortices. Rather than finding a distinct cortical locus for each speech component, we observed neural correlates of both articulatory and phonetic components in multiple hubs of a widespread language production network.Significance.These results reveal the distributed cortical representations whose activity can enable decoding speech components during continuous speech through the use of this minimally invasive recording method, elucidating language neurobiology and neural targets for future speech-BCIs.

Artificial intelligence automates the characterization of reversibly actuating planar-flow-casted NiTi shape memory alloy foil.

Nickel-Titanium (NiTi) shape memory alloys (SMAs) are smart materials able to recover their original shape under thermal stimulus. Near-net-shape NiTi SMA foils of 2 meters in length and width of 30 mm have been successfully produced by a planar flow casting facility at CSIRO, opening possibilities of wider applications of SMA foils. The study also focuses on establishing a fully automated experimental system for the characterisation of their reversible actuation, significantly improving SMA foils adaptation into real applications. Artificial Intelligence involving Computer Vision and Machine Learning based methods were successfully employed in the development of the automation SMA characterization process. The study finds that an Extreme Gradient Boosting (XGBoost) Regression model based predictive system experimented with over 175,000 video samples could achieve 99% overall prediction accuracy. Generalisation capability of the proposed system makes a significant contribution towards the efficient optimisation of the material design to produce high quality 30 mm SMA foils.

In vivo performance of a rare earth free Mg-Zn-Ca alloy manufactured using twin roll casting for potential applications in the cranial and maxillofacial fixation devices.

A magnesium alloy containing essential, non-toxic, biodegradable elements such as Ca and Zn has been fabricated using a novel twin-roll casting process (TRC). Microstructure, mechanical properties, in vivo corrosion and biocompatibility have been assessed and compared to the properties of the rare earth (RE) element containing WE43 alloy. TRC Mg-0.5 wt% Zn- 0.5 wt% Ca exhibited fine grains with an average grain size ranging from 70 to 150 µm. Mechanical properties of a TRC Mg-0.5Zn-0.5Ca alloy showed an ultimate tensile strength of 220 MPa and ductility of 9.3%. The TRC Mg-0.5Zn-0.5Ca alloy showed a degradation rate of 0.51 ± 0.07 mm/y similar to that of the WE43 alloy (0.47 ± 0.09 mm/y) in the rat model after 1 week of implantation. By week 4 the biodegradation rates of both alloys studied were lowered and stabilized with fewer gas pockets around the implant. The histological analysis shows that both WE43 and TRC Mg-0.5Zn-0.5Ca alloy triggered comparable tissue healing responses at respective times of implantation. The presence of more organized scarring tissue around the TRC Mg-0.5Zn-0.5Ca alloys suggests that the biodegradation of the RE-free alloy may be more conducive to the tissue proliferation and remodelling process.

Vision based supervised restricted Boltzmann machine helps to actuate novel shape memory alloy accurately.

Extraordinary shape recovery capabilities of shape memory alloys (SMAs) have made them a crucial building block for the development of next-generation soft robotic systems and associated cognitive robotic controllers. In this study we desired to determine whether combining video data analysis techniques with machine learning techniques could develop a computer vision based predictive system to accurately predict force generated by the movement of a SMA body that is capable of a multi-point actuation performance. We identified that rapid video capture of the bending movements of a SMA body while undergoing external electrical excitements and adapting that characterisation using computer vision approach into a machine learning model, can accurately predict the amount of actuation force generated by the body. This is a fundamental area for achieving a superior control of the actuation of SMA bodies. We demonstrate that a supervised machine learning framework trained with Restricted Boltzmann Machine (RBM) inspired features extracted from 45,000 digital thermal infrared video frames captured during excitement of various SMA shapes, is capable to estimate and predict force and stress with 93% global accuracy with very low false negatives and high level of predictive generalisation.

Comparative study of the methods of extracting mesenchymal stem cells from cryopreserved Wharton's Jelly.

The Wharton's Jelly (WJ) is an established source of mesenchymal stem cells (MSC). We compared 3 methods of extracting WJ-MSC from cryopreserved tissue and determined that enzymatic digestion of the WJ yielded the most viable MSC, compared to the explant and mechanical digestion methods. The enzymatically-released WJ-MSC conformed to the International Society for Cellular Therapy (ISCT) criteria: displayed plastic-adherence, co-expressed CD73, CD90, CD105 and were negative for hematopoietic lineage cell markers.

The evolution of intramedullary spinal cord tumor surgery.

OBJECTIVE: Resections of intramedullary spinal cord tumors were attempted as early as 1890. More than a century after these primitive efforts, profound advancements in imaging, instrumentation, and operative techniques have greatly improved the modern surgeon's ability to treat such lesions successfully, often with curative results. METHODS: We review the history of intramedullary spinal cord tumor surgery, as well as the evolution and advancement of technologies and surgical techniques that have defined the procedure over the past 100 years. RESULTS: Surgery to remove intramedullary spinal cord tumors has evolved to include sophisticated imaging equipment to pinpoint tumor location, laser scalpel systems to provide precise incisions with minimal damage to surrounding tissue, and physiological monitoring to detect and prevent intraoperative motor deficits. CONCLUSION: Modern surgical devices and techniques have developed dramatically with the availability of new technologies. As a result, continual advancements have been achieved in intramedullary spinal cord tumor surgery, thus increasing the safety and effectiveness of tumor resection, and progressively improving the overall outcomes in patients undergoing such procedures.