The first edition (1981) of this comprehensive work weighed 25 lb and had 3300 pages. This entirely new third edition, published 14 years later, has grown to 45 lb and 4850 pages. This growth is not due to inflation but is the direct result of the information explosion. The multiplication of techniques for imaging and studying the bone and soft tissues has made choosing the best method for examination of the patient more difficult and, in an environment in which cost-effectiveness has become a byword, more important. An underlying principle of this work is to guide the physician in the choice of the technology to be employed to get essential information quickly and efficiently. Dr Resnick has called on a group of 40 contributors for special information, but he himself has played a major role in the writing and has been more than an editor, thereby ensuring a consistent and

Diagnosis of Bone and Joint Disorders

Over the past century, in most countries, there has been a continual increase in life expectancy primarily due to improvements in public health, nutrition, personal hygiene and medicine. However, these improvements are now coupled with aging population demographics and falling birth rates, which, when combined, are expected to significantly burden the socioeconomic well-being of many of these countries. In fact, never before in human history have we been confronted with such a large aging population, nor have we developed solid, cost-effective solutions for the well-being, healthcare and social needs of the elderly. One efficient and cost-effective solution to the problem of elderly/patient care is remote healthcare monitoring so they can continue to live at home rather than in nursing homes or hospitals that are very expensive and with limited spaces. These remote monitoring systems will allow medical personnel to keep track of important physiological signs with reduced human resources, at less cost and in real time. This paper introduces several low-cost, noninvasive, user-friendly sensing and actuating systems using information and communication technologies. Such systems can be used to create engineering solutions to some of the pressing healthcare problems in our society, especially as it pertains to the elderly. One example is the integration of sensors, wireless communications, low-power electronics and intelligent computing to determine health-related information using signals from walking patterns. Such a sensing system will be suitable for prolonged use in a home environment. It will be wearable, noninvasive and non-intrusive, similar to smart socks, smart wrist-bands or smart belts. Other examples such as a smart joint monitor and a smart sleeping environment will be discussed, and future perspectives and research challenges in smart home technologies will be described.

Information and communications technologies for elderly ubiquitous healthcare in a smart home

Vibroarthrographic (VAG) signals emitted by human knee joints are nonstationary and multicomponent in nature; time-frequency distributions (TFD's) provide powerful means to analyze such signals. The objective of this paper is to construct adaptive TFD's of VAG signals suitable for feature extraction. An adaptive TFD was constructed by minimum cross-entropy optimization of the TFD obtained by the matching pursuit decomposition algorithm. Parameters of VAG signals such as energy, energy spread. frequency, and frequency spread were extracted from their adaptive TFD's. The parameters carry information about the combined TF dynamics of the signals. The mean and standard deviation of the parameters were computed, and each VAG signal was represented by a set of just six features. Statistical pattern classification experiments based on logistic regression analysis of the parameters showed an overall normal/abnormal screening accuracy of 68.9% with 90 VAG signals (51 normals and 39 abnormals), and a higher accuracy of 77.5% with a database of 71 signals with 51 normals and 20 abnormals of a specific type of patellofemoral disorder. The proposed method of VAG signal analysis is independent of joint angle and clinical information, and shows good potential for noninvasive diagnosis and monitoring of patellofemoral disorders such as chondromalacia patella.

Adaptive time-frequency analysis of knee joint vibroarthrographic signals for noninvasive screening of articular cartilage pathology

Spectral and complexity analysis of scalp EEG characteristics for mild cognitive impairment and early Alzheimer's disease

Externally detected vibroarthrographic (VAG) signals bear diagnostic information related to the roughness, softening, breakdown, or the state of lubrication of the articular cartilage surfaces of the knee joint. Analysis of VAG signals could provide quantitative indices for noninvasive diagnosis of articular cartilage breakdown and staging of osteoarthritis. We propose the use of statistical parameters of VAG signals, including the form factor involving the variance of the signal and its derivatives, skewness, kurtosis, and entropy, to classify VAG signals as normal or abnormal. With a database of 89 VAG signals, screening efficiency of up to 0.82 was achieved, in terms of the area under the receiver operating characteristics curve, using a neural network classifier based on radial basis functions.

Screening of knee-joint vibroarthrographic signals using statistical parameters and radial basis functions

Devising techniques and instrumentation for early detection of knee arthritis and chondromalacia presents a challenge in the domain of biomedical engineering. The purpose of the present investigation was to characterize normal knees and knees affected by osteoarthritis, rheumatoid arthritis, and chondromalacia using a set of noninvasive acceleration measurements. Ultraminiature accelerometers were placed on the skin over the patella in four groups of subjects, and acceleration measurements were obtained during leg rotation. Acceleration measurements were significantly different in the four groups of subjects in the time and frequency domains. Power spectral analysis revealed that the average power was significantly different for these groups over a 100–500 Hz range. Noninvasive acceleration measurements can characterize the normal, arthritis, and chondromalacia knees. However, a study on a larger group of subjects is indicated.

Noninvasive acceleration measurements to characterize knee arthritis and chondromalacia

Spondyloarthropathy represents a group of joint diseases with a tendency to reactive new bone formation. Spondyloarthropathy includes Reiter's syndrome, ankylosing spondylitis, and the arthropathy of inflammatory diseases (ulcerative colitis and Crohn's disease). Usually, an extensive investigation is required to distinguish spondyloarthropathy of the knee joint from rheumatoid arthritis. Recently, Reddy et al. (Ann. Biomed. Eng. 23:78–84, 1995) have developed the accelerometry technique to characterize various types of arthritis. The question remains if noninvasive acceleration measurements can be used to distinguish between spondyloarthropathy and rheumatoid arthritis. An ultraminiature accelerometer was placed on the patella, and the subject was asked to rhythmically rotate the knee from 90 flexion to full extension. Results have shown that the mean power of acceleration signal in the range of 100–500 Hz is significantly different (p < 0.05) for spondyloarthropathy patients when compared to rheumatoid arthritis patients. The noninvasive accelerometry technique represents a potential tool for characterization of spondyloarthropathy patients. © 2001 Biomedical Engineering Society.

Noninvasive measurement of acceleration at the knee joint in patients with rheumatoid arthritis and spondyloarthropathy of the knee.

Fractal analysis of acceleration signals from patients with CPPD, rheumatoid arthritis, and spondyloarthroparthy of the finger joint

Recent emphasis on healthcare cost containment has created a growing need for developing automated diagnostic systems to aid the physician. Such systems should be reliable and robust. Neural networks have been used to provide advice to the physician. However, the reliability and robustness of a neural network depend on various factors such as the architecture, the initial weights used in training and the training data. In order to improve the reliability and robustness, we have developed hybrid intelligent systems with committee neural networks and fuzzy logic. Our concept of committee networks involves training and evaluating several networks and recruiting five networks into the committee based on initial testing. A majority opinion of the member networks of the committee is used to arrive at the final decision. The system reliability is improved since several networks in the committee are used in to make the decision. We have developed and evaluated hybrid intelligent systems with fuzzy logic and committee networks for distinguishing spondyloarthropathy of the knee from rheumatoid arthritis. Testing revealed that majority opinion of the committee well classified the arthritis.

Hybrid fuzzy logic committee neural networks for classification in medical decision support systems

In the area of medical diagnosis, noninvasive quantitative patient assessment techniques are fast replacing the traditional qualitative patient evaluation methods based on "feeling". Noninvasive diagnosis of arthritis and chondromalacia presents a continuing problem in the domain of biomedical engineering.

http://ieeexplore.ieee.org/iel5/1028/5760826/05760842.pdf

Bruce M. Rothschild

Papers

Noninvasive acceleration measurements to characterize knee arthritis and chondromalacia

Noninvasive measurement of acceleration at the knee joint in patients with rheumatoid arthritis and spondyloarthropathy of the knee.

Fractal analysis of acceleration signals from patients with CPPD, rheumatoid arthritis, and spondyloarthroparthy of the finger joint

Hybrid fuzzy logic committee neural networks for classification in medical decision support systems

Accelerometer measurements for noninvasive diagnosis of degenerative joint diseases