A set of ultrasonograms of lesions from 200 patients between the ages of 14 and 93 years who underwent mammography followed by ultrasonographic examination and excisional biopsy has been studied with computer vision techniques to improve the ultrasonographic specificity of the diagnosis. Selected features representing the texture of the lesion were calculated and then classified by an artificial neural network. This network was biased toward correctly classifying all the malignant cases at the expense of some misclassification of the benign cases. The network diagnosed the malignant cases with 100% sensitivity and 40% specificity (compared with 0% specificity for the radiologists diagnosing the same set of cases in the breast imaging setting), and tests performed with a leave-one-out technique indicate that the network will generalize well to new cases. This suggests that methods based on neural network classification of texture features show promise for potentially decreasing the number of unnecessary biopsies by a significant amount in patients with sonographically identifiable lesions.

We conclude that the FMB/F relation reflects hemodynamic conductance at the microvascular level.

The proposed method brings forward transformational possibilities in wireless cardiac pacing, and also in powering up the implantable devices.

Radio-frequency (RF)-based wireless power transfer method is highly desirable to power deep-body medical implants, such as cardiac pacemakers. The antenna is one of the essential components of such system; however, it poses significant design challenges for deep-body applications and must be modeled and characterized correctly to achieve the required performance. In this paper, design and validation of a novel wideband numerical model (WBNM) are proposed for deeply implantable antennas and to enable RF-powered leadless pacing. In particular, we acquired a wideband tissue simulating liquid (TSL) and fully characterized it using a dielectric probe. Based on the measured properties of the TSL, the design and numerical characterization of the WBNM were performed using a hybrid simulation method, i.e., by employing the finite-element method and method of moment. The proposed WBNM was validated experimentally as well as analytically using a reference microstrip antenna. Good agreement between the simulated, measured, and analytical results validated the proposed model. Furthermore, the application of this model and the TSL was demonstrated by the design, development, manufacture, and measurement of a novel metamaterial-based conformal antenna at 2.4 GHz. Moreover, good agreement was found between the simulated and measured results of the proposed conformal antenna. It is evident from the results that the proposed numerical model can be used to design deeply implantable antennas for any frequency, ranging from 800 to 5800 MHz. Finally, the proposed miniature conformal antenna and its successful integration with a leadless pacemaker model present a great potential for future RF-powered leadless pacemakers and other deep-body medical implants. INDEX TERMS Tissue simulating model, numerical model, leadless pacemakers, implantable antennas, implantable medical devices, energy harvesting, deep-body implants.