Sampath Kumar

This manuscript investigates a biosensor based on a gadolinium (Gd)-doped hafnium oxide (HfO₂) ferroelectric–dielectric (FE-DE) heterostructure for high-sensitivity, low-power biomolecule detection. The sensing cavity consists of a dielectric layer and a biomolecule-filled region, where different filling conditions (0%, 50%, 75%, and 100%) emulate realistic sensing environments. The dielectric properties of the biomolecule changes, which results in changes of the dielectric capacitance, thus improving capacitance matching with the ferroelectric layer and stabilizing the negative-capacitance effect. This intrinsic voltage amplification enhances the electrical response of the biosensor. Electrical characteristics, including charge–voltage, energy–charge, capacitance–voltage, and voltage amplification, were analyzed for biomolecules with dielectric constants ranging from 1 to 12. Higher-dielectric-constant biomolecules exhibited superior sensing performance; for example, gelatin (K = 12) achieved the highest sensitivity of 1.9632 at 100% cavity filling, along with the maximum voltage amplification. To evaluate the impact of amplification, the amplified dielectric voltage was applied to the gate of a 50 nm NMOS transistor. The enhanced gate voltage improved the drain current and transconductance and enabled a subthreshold swing below the conventional thermal limit of 60 mV/dec. A feasible fabrication plan is demonstrated to show the possible realization of the sensor system. These outcomes indicate that the proposed Gd-doped HfO₂-based FE-DE heterostructure is capable of efficiently magnifying the dielectric changes caused by biomolecules, thus offering an encouraging framework for future low-power and high-sensitivity biosensing applications.

Spine radiographs in the standing position are the recommended standard for diagnosing idiopathic scoliosis. Though the deformity exists in 3D, its diagnosis is currently carried out with the help of 2D radiographs due to the unavailability of an efficient, low-cost 3D alternative. Computed tomography (CT) and magnetic resonance imaging (MRI) are not suitable in this case, as they are obtained in the supine position. Research on 3D modelling of scoliotic spine began with multiplanar radiographs and later moved on to biplanar radiographs and finally a single radiograph. Nonetheless, modern advances in diagnostic imaging have the potential to preserve image quality and decrease radiation exposure. They include the DIERS formetric scanner system, the EOS imaging system, and ultrasonography. This review article briefly explains the technology behind each of these methods. They are compared with the standard imaging techniques. The DIERS system and ultrasonography are radiation free but have limitations with respect to the quality of the 3D model obtained. There is a need for 3D imaging technology with less or zero radiation exposure and that can produce a quality 3D model for diseases like adolescent idiopathic scoliosis. Accurate 3D models are crucial in clinical practice for diagnosis, planning surgery, patient follow-up examinations, biomechanical applications, and computer-assisted surgery.

Three out of every 100 people in this world have some form of scoliosis. A doctor would suggest surgery if scoliosis is severe in certain conditions to prevent it from getting worse. The deformity of spine can be visualized well in 3D rather than in 2D as it is time-consuming to evaluate the degree of deformity. CATIA V5 is used to develop feature-based modeling. The angles of vertebrae orientation from biplanar X-rays are fed into the CATIA interface which forms the orientation of the 3D spine model. The feature-based model is modified using the morpho-realistic model for increasing the accuracy. The validation procedure for the feature-based model is divided into quantitative and qualitative analysis. In quantitative analysis, the One Sided Hausdorff Distance (OSHD), Average Surface Distance (ASD), Cobb angle and Axial Vertebral Rotation (AVR) metrics are obtained for inter-observer variability and intra-observer variability. In qualitative analysis, the model is projected along the frontal and lateral radiographs and compared with reference radiographs. The accuracy of the model can be estimated by the uncertainty in these parameters. The mean surface model reconstruction errors were found to be smaller than 1.5 mm in comparing cadaver Computed Tomography (CT) scan as well as for the 10 cases including inter-observer and intra-observer variability. The average differences for AVR and Cobb angle were less than 2 ̊. The modified feature-based 3D modeling allows for true 3D pre-operative planning which helps the doctor for better treatment with much less time.

3D deformity of the spine-like scoliosis is commonly assessed using stereo-radiographic technique. The Magnetic Resonance Imaging (MRI) and Computed Tomography (CT) modelling fail in this case, as they are obtained with the patient in sleeping position. Also, they are associated with heavy cost and radiation hazards. We have developed two models namely, feature-based model and a Combined Stereo-Corresponding Point And Geometric (CSCPG) model for 3D spine reconstruction using biplanar x-rays. The spinal disorder is quantified using two parameters, namely axial vertebral rotation and spinal curvature using both these methods. Their performance is compared by considering Non-Stereo-Corresponding Point (NSCP) model as the gold standard method. The feature-based model is less accurate compared to the CSCPG model. The performance of the feature-based model is also poor in the case of deformity quantification. A statistical significance test is conducted in order to verify its suitability in diagnosis of the spinal disorders. The accuracy of feature-based model can be improved by incorporating additional landmark features from the biplanar radiographs. The proposed CSCPG model has accuracy closer to the gold standard technique and can be used a diagnostic tool for the spinal disorders.

Essential predictions are to be made by the parties distributed at multiple locations. However, in the process of building a model, perceptive data is not to be revealed. Maintaining the privacy of such data is a foremost concern. Earlier approaches developed for classification and prediction are proven not to be secure enough and the performance is affected. This chapter focuses on the secure construction of commonly used classifiers. The computations performed during model building are proved to be semantically secure. The homomorphism and probabilistic property of Paillier is used to perform secure product, mean, and variance calculations. The secure computations are performed without any intermediate data or the sensitive data at multiple sites being revealed. It is observed that the accuracy of the classifiers modeled is almost equivalent to the non-privacy preserving classifiers. Secure protocols require reduced computation time and communication cost. It is also proved that proposed privacy preserving classifiers perform significantly better than the base classifiers.

Scoliosis is a 3D deformation of the human spine. It is evaluated using 3D stereo-radiographic reconstruction from the biplanar x-rays. The combined stereo-corresponding point and geometric (CSCPG) reconstruction algorithm is used for this purpose. The CSCPG reconstruction requires six stereo-corresponding landmarks per vertebra to be identified on the biplanar x-rays. Currently, these landmarks are semi automatically identified. In this paper, the landmark identification procedure is automated and the effect of automated procedures on the accuracy of 3D reconstruction is analyzed. The statistical significance test is performed to compare the accuracies of these reconstructions. The benefits of automated procedure are twofold. It is able to give better reconstruction accuracy and at the same time it also reduces observer variability.