EXPERT-SYSTEM SUPPORT USING BAYESIAN BELIEF NETWORKS IN THE DIAGNOSIS OF FINE-NEEDLE ASPIRATION BIOPSY SPECIMENS OF THE BREAST

Aim-To develop an expert system model for the diagnosis of fine needle aspiration cytology (FNAC) of the breast.

Methods-Knowledge and uncertainty were represented in the form of a Bayesian belief network which permitted the combination of diagnostic evidence in a cumulative manner and provided a final probability for the possible diagnostic outcomes. The network comprised 10 cytological features (evidence nodes), each independently linked to the diagnosis (decision node) by a conditional probability matrix. The system was designed to be interactive in that the cytopathologist entered evidence into the network in the form of likelihood ratios for the outcomes at each evidence node.

Results-The efficiency of the network was tested on a series of 40 breast FNAC specimens. The highest diagnostic probability provided by the network agreed with the cytopathologists' diagnosis in 100% of cases for the assessment of discrete, benign, and malignant aspirates. A typical probably benign cases were given probabilities in favour of a benign diagnosis. Suspicious cases tended to have similar probabilities for both diagnostic outcomes and so, correctly, could not be assigned as benign or malignant. A closer examination of cumulative belief graphs for the diagnostic sequence of each case provided insight into the diagnostic process, and quantitative data which improved the identification of suspicious cases.

Conclusion-The further development of such a system will have three important roles in breast cytodiagnosis: (1) to aid the cytologist in making a more consistent and objective diagnosis; (2) to provide a teaching tool on breast cytological diagnosis for the non-expert; and (3) it is the first stage in the development of a system capable of automated diagnosis through the use of expert system machine vision.

Wearable antennas for medical monitoring systems

A wearable silver nano particle inkjet printed antenna suitable for wireless biomedical sensing is presented. The performance is evaluated on a synthetic variable layered phantom test-bed, representative of human tissue for operation in the 868/915 MHz, and 2400 MHz industrial, scientific and medical frequency bands. Antenna radiation efficiency measurements on the phantom were compared with antennas prototyped with copper. Total radiation efficiencies up to ???6.5 dB are reported, with less than 0.5 dB difference in performance between copper and silver nano particle variants, showing promising application for low-cost disposable wireless sensing.

The Efficient Use of Multiple Sources of a Nonrenewable Resource under Supply Cost Uncertainty

Uncertainties as to future supply costs of nonrenewable natural resources, such as oil and gas, raise the issue of the choice of supply sources. In a perfectly deterministic world, an efficient use of multiple sources of supply requires that any given market exhausts the supply it can draw from a low cost source before moving on to a higher cost one; supply sources should be exploited in strict sequence of increasing marginal cost, with a high cost source being left untouched as long as a less costly source is available. We find that this may not be the efficient thing to do in a stochastic world. We show that there exist conditions under which it can be efficient to use a risky supply source in order to conserve a cheaper non risky source. The benefit of doing this comes from the fact that it leaves open the possibility of using it instead of the risky source in the event the latter’s future cost conditions suddenly deteriorate. There are also conditions under which it will be efficient to use a more costly non risky source while a less costly risky source is still available. The reason is that this conserves the less costly risky source in order to use it in the event of a possible future drop in its cost.