Use Of Computer-assisted Diagnosis Research Articles

Multi-View Auxiliary Diagnosis Algorithm for Lung Nodules

Lung is an important organ of human body. More and more people are suffering from lung diseases due to air pollution. These diseases are usually highly infectious. Such as lung tuberculosis, novel coronavirus COVID-19, etc. Lung nodule is a kind of high-density globular lesion in the lung. Physicians need to spend a lot of time and energy to observe the computed tomography image sequences to make a diagnosis, which is inefficient. For this reason, the use of computer-assisted diagnosis of lung nodules has become the current main trend. In the process of computer-aided diagnosis, how to reduce the false positive rate while ensuring a low missed detection rate is a difficulty and focus of current research. To solve this problem, we propose a three-dimensional optimization model to achieve the extraction of suspected regions, improve the traditional deep belief network, and to modify the dispersion matrix between classes. We construct a multi-view model, fuse local three-dimensional information into two-dimensional images, and thereby to reduce the complexity of the algorithm. And alleviate the problem of unbalanced training caused by only a small number of positive samples. Experiments show that the false positive rate of the algorithm proposed in this paper is as low as 12%, which is in line with clinical application standards.

Diagnostic performance of a computer-assisted diagnostic system: sensitivity of BONENAVI for bone scintigraphy in patients with disseminated skeletal metastasis is not so high

PurposeBone scintigraphy (BS) of disseminated skeletal metastasis is sometimes misinterpreted as normal. The use of computer-assisted diagnosis (CAD) may resolve this problem. We investigated the performance of a CAD system, BONENAVI, in the diagnosis of disseminated skeletal metastasis.MethodsCases of disseminated skeletal metastasis were selected from a BS log. These patients’ BSs were analyzed by BONENAVI to obtain an artificial neural network (ANN) and bone scan index (BSI). Clinical features (type of primary cancer, CT type, and BS type) were compared with the BONENAVI (ANN and BSI) results. The BS findings (diffuse increased axial skeleton uptake, inhomogeneity of uptake, proximal extremity contrast, and degree of renal uptake) and ANN or BSI were evaluated. Then, negative ANN patients were presented.ResultsFifty-four patients were diagnosed as having disseminated skeletal metastasis. Regarding the primary cancers, 12 had prostate cancer, 16 gastric cancers, 16 breast cancers, and 10 miscellaneous cancers. Total sensitivity of ANN (≥ 0.5) was 76% (41/54). ANN values correlated with the BS type among clinical features. Diffuse increased axial skeleton uptake was mostly correlated with ANN of the BS findings.ConclusionThe BONENAVI CAD system was partially helpful in diagnosing disseminated skeletal metastasis, but the sensitivity of BONENAVI was not sufficient and underestimated the disseminated skeletal metastasis. Further improvement of this CAD system is necessary to improve the detectability of disseminated skeletal metastasis.

Erratum to: The use of computer-assisted diagnosis in cardiac perfusion nuclear medicine studies: A review (part 3)

Erratum to: The use of computer-assisted diagnosis in cardiac perfusion nuclear medicine studies: A review (part 3)

The use of computer-assisted diagnosis in cardiac perfusion nuclear medicine studies: A review (part 3)

Computer-assisted diagnosis (CAID) is commonly used to evaluate cardiac nuclear medicine studies such as thallium perfusion scans. Part 1 of this series (Journal of Digital Imaging, 5:209-222, 1992) reviewed the basic theory underlying CAID in nuclear medicine and its use in planar thallium imaging. Part 2 discussed the application of CAID to SPECT perfusion studies (Journal of Digital Imaging, 6:1-15, 1993). This article reviews new variations of CAID programs for SPECT imaging and the application of expert systems and neural networks to CAID of nuclear medicine perfusion studies.

The use of computer-assisted diagnosis in cardiac perfusion nuclear medicine studies: a review (Part 2).

Computer-assisted diagnosis (CAID) is commonly used to evaluate cardiac nuclear medicine studies such as thallium perfusion scans. Part I of this series (Journal of Digital Imaging, Vol 5, No 4, pp 209-222) reviewed the basic theory underlying CAID in nuclear medicine and its use in planar thallium imaging. This review discusses the application of CAID to SPECT perfusion studies.

The use of computer-assisted diagnosis in cardiac-perfusion nuclear medicine studies: a review.

The use of computer-assisted diagnosis has become widespread in cardiac nuclear medicine. Quantitative programs are commercially available for ventriculography, phase analysis, and thallium 201 perfusion studies. The goal of these programs is to eliminate interobserver variability by objectively analyzing the studies without causing a loss of accuracy. In addition, by using quantitative data not apparent on visual inspection, some programs attempt to increase sensitivity for disease above that possible by the visual reading of images. Programs that analyze perfusion studies to detect coronary artery disease have received the most interest. Results have varied, but sensitivities as high as 95% have been reported. This review discusses the techniques of computer-assisted diagnosis for thallium 201 myocardial-perfusion studies. The circumferential and washout profile methods are discussed in detail.