Potential usefulness of an artificial neural network for differential diagnosis of interstitial lung diseases: pilot study.

Asada, Naoki; Doi, Kei; MacMahon, Heber; Montner, Steven M.; Giger, Maryellen L.; Abê, Chiaki; Wu, Yuzheng

doi:10.1148/radiology.177.3.2244001

Cited by 134 publications

(46 citation statements)

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“…Accuracy Sensitivity Specificity Inter-observer variability 0.984 ± 0.00475 0.957 ± 0.0174 0.993 ± 0.00306 Classification correction (9) 0.969 ± 0.00803 0.943 ± 0.0330 0.978 ± 0.0106 Rule-based (2) 0.961 ± 0.0116 0.940 ± 0.0389 0.969 ± 0.0153 PC int., entropy, corr. location (8) 0.956 ± 0.0157 0.912 ± 0.0617 0.972 ± 0.0248 PC int., corrected location (7) 0.953 ± 0.0177 0.906 ± 0.0649 0.970 ± 0.0288 PC int.…”

Section: Methodsmentioning

confidence: 99%

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Computer aided diagnosis in chest radiography

Mouton¹,

Douglas

2003

Academic Radiology

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Section: Methodsmentioning

confidence: 99%

“…Such studies have continued to appear, e.g. [231,9]. In a recent paper Ashizawa et al [11,12] trained a neural network to estimate the likelihood of 11 diseases based on 10 clinical parameters and 16 radiological findings.…”

Section: Chapter 2 Computer Analysis Of Chest Radiographs -A Reviewmentioning

confidence: 99%

Computer aided diagnosis in chest radiography

Mouton¹,

Douglas

2003

Academic Radiology

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“…Numerous Computer-Aided Diagnosis systems have been developed in recent years for detection (CADd) and diagnosis (CADx) of pulmonary nodules and interstitial lung disease in chest radiography and CT. Several researchers [3,4,7] showed that artificial neural networks can provide powerful tools in the diagnosis of interstitial lung diseases. Other work [16,22,8] showed that texture features can be used to detect interstitial lung diseases.…”

Section: Related Workmentioning

confidence: 99%

An investigation into the relationship between semantic and content based similarity using LIDC

Kim

Dasovich

Bhaumik

et al. 2010

Proceedings of the International Conference on Multimedia Information Retrieval

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There is considerable research in the field of content-based medical image retrieval; however, few of the current systems investigate the relationship between the radiologists' visual impression of image similarity and the computer calculated content-based similarity. Furthermore, those research studies that investigate these relationships analyze the visual similarity with respect to degree of malignancy without including specific characteristics that are important in the diagnosis process.The creation of the NIH/NCI Lung Image Database Consortium (LIDC) dataset offers the opportunity to perform the proposed research. Each nodule out of the 932 distinct nodules (larger than 3mm in diameter) was delineated and annotated by up to four radiologists using nine semantic characteristics that are important in the lung nodule interpretation process. Using the LIDC images, we propose to encode the radiologists' characteristic-based similarity and further discover if there is any relationship between this conceptual/characteristic-based similarity and the contentbased similarity for lung nodule interpretation.Our preliminary results show that it is a challenging problem to model the characteristic-based and content-based relationships for a broad category of lung nodules. A correlation of only 0.1 was obtained between the characteristicbased similarity and the predicted characteristic-based similarity using an artificial neural networked trained on four types of low-level image features (size, intensity, shape, and texture) calculated for 640 random pairs of nodules. Future research is necessary to investigate the appropriateness of the considered image features to model both the variation in the human interpretation of the lung nodules and the perceived characteristic-based similarity.

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“…The nodes are the sites of processing with the arcs affecting a node by positively or negatively weighting its outcome [12,24]. This statistical weighting has the effect of influencing both the input to a node as well as its output and hence its influence on ''downstream'' nodes [25].…”

Section: Artificial Neural Networkmentioning

confidence: 99%

Decision support systems for clinical radiological practice — towards the next generation

Stivaros¹,

Gledson²,

Nenadić³

et al. 2010

BJR

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ABSTRACT. The huge amount of information that needs to be assimilated in order to keep pace with the continued advances in modern medical practice can form an insurmountable obstacle to the individual clinician. Within radiology, the recent development of quantitative imaging techniques, such as perfusion imaging, and the development of imaging-based biomarkers in modern therapeutic assessment has highlighted the need for computer systems to provide the radiological community with support for academic as well as clinical/translational applications. This article provides an overview of the underlying design and functionality of radiological decision support systems with examples tracing the development and evolution of such systems over the past 40 years. More importantly, we discuss the specific design, performance and usage characteristics that previous systems have highlighted as being necessary for clinical uptake and routine use. Additionally, we have identified particular failings in our current methodologies for data dissemination within the medical domain that must be overcome if the next generation of decision support systems is to be implemented successfully. What is a decision support system? Decision support systems (DSS) are a set of manual or computer-based tools that assist in some decision-making activity. In today's information-driven environment, DSS are commonly understood to be a variety of computerised information management systems, designed to help resolve complicated problems and/or questions by supporting the decision-making process. DSS are gaining increasing popularity in various domains including business, engineering, the military and medicine. These systems are especially valuable in situations where the amount of available information is prohibitive for the intuition of a human decision maker and where precision and optimal performance are of importance [1]. Medical decision support systemsThe huge amount of information that needs to be assimilated in order to keep pace with continued advances in modern medical practice can form an insurmountable obstacle to the individual clinician. In medical applications decision quality is of crucial importance, whilst human decision-making performance can be suboptimal and deteriorate as the complexity of the problem increases.For these reasons, the development of medical DSS is becoming increasingly important [1] and the routine uptake of these ''intelligent'' systems is becoming more common [2]. One of the earliest rule-based expert systems, DENDRAL [3], was implemented in the 1960s and was designed to provide support to organic chemists. This was further developed over the early 1970s by the same team at Stanford University into arguably the first rule-based medical DSS, MYCIN [4]. This system attempted to identify bacteria causing severe infections and recommend appropriate antibiotics. From these early DSS and the subsequent development of knowledge engineering, we now have DSS based on established architectures. Specific radiological cons...

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Potential usefulness of an artificial neural network for differential diagnosis of interstitial lung diseases: pilot study.

Cited by 134 publications

References 0 publications

Computer aided diagnosis in chest radiography

Computer aided diagnosis in chest radiography

An investigation into the relationship between semantic and content based similarity using LIDC

Decision support systems for clinical radiological practice — towards the next generation

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