Towards ontology-based decision support systems for complex ultrasound diagnosis in obstetrics and gynecology

Maurice, P; Dhombres, Ferdinand; Blondiaux, Éléonore; Friszer, Stéphanie; Guilbaud, Lucie; Lelong, Nathalie; Khoshnood, Babak; Charlet, Jean; Perrot, Nicolas; Jauniaux, Eric; Jouannic, Jean‐Marie

doi:10.1016/j.jogoh.2017.03.004

Cited by 25 publications

(15 citation statements)

References 30 publications

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“…While it is well beyond the scope of this article to go into details regarding this huge and burgeoning branch of science, a few principles will be described. In medicine, the idea is that tremendous amounts of information ("big data") together with machine learning can create algorithms that perform as well as, if not better than, and much faster than human physicians [105]. The inspiration is the human brain, hence the designation artificial neuronal networks and machine learning, where the computer automatically recognizes patterns, based on entry of enormous quantities information bits, such as "ideal" ultrasound images of the fetal anatomy.…”

Section: Artificial Intelligencementioning

confidence: 99%

Obstetric ultrasound: where are we and where are we going?

Abramowicz

2021

Ultrasonography

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Ultrasonography. 2020 Aug 25. Epub ahead of print Diagnostic ultrasound (DUS) is, arguably, the most common technique used in obstetrical practice. From A mode, first described by Ian Donald for gynecology in the late 1950s, to B mode in the 1970s, real-time and gray-scale in the early 1980s, Doppler a little later, sophisticated color Doppler in the 1990s and three dimensional/four-dimensional ultrasound in the 2000s, DUS has not ceased to be closely associated with the practice of obstetrics. The latest innovation is the use of artificial intelligence which will, undoubtedly, take an increasing role in all aspects of our lives, including medicine and, specifically, obstetric ultrasound. In addition, in the future, new visualization methods may be developed, training methods expanded, and workflow and ergonomics improved.

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Section: Artificial Intelligencementioning

confidence: 99%

Obstetric ultrasound: where are we and where are we going?

Abramowicz

2021

Ultrasonography

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“…In software engineering, ontologies have a wide range of applications, including model transformations, cloud security engineering, decision support, search, and semantic integration (Kappel et al, 2006;Aljawarneh, Alawneh & Jaradat, 2017;Maurice et al, 2017;Bartussek et al, 2018;De Giacomo et al, 2018). Semantic integration is the process of merging the semantic contents of multiple ontologies.…”

Section: Ontology and Software Engineeringmentioning

confidence: 99%

Reverse engineering approach for improving the quality of mobile applications

Elsayed

ElDahshan

El-Sharawy

et al. 2019

PeerJ Computer Science

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BackgroundPortable-devices applications (Android applications) are becoming complex software systems that must be developed quickly and continuously evolved to fit new user requirements and execution contexts. Applications must be produced rapidly and advance persistently in order to fit new client requirements and execution settings. However, catering to these imperatives may bring about poor outline decisions on design choices, known as anti-patterns, which may possibly corrupt programming quality and execution. Thus, the automatic detection of anti-patterns is a vital process that facilitates both maintenance and evolution tasks. Additionally, it guides developers to refactor their applications and consequently enhance their quality.MethodsWe proposed a general method to detect mobile applications’ anti-patterns that can detect both semantic and structural design anti-patterns. The proposed method is via reverse-engineering and ontology by using a UML modeling environment, an OWL ontology-based platform and ontology-driven conceptual modeling. We present and test a new method that generates the OWL ontology of mobile applications and analyzes the relationships among object-oriented anti-patterns and offer methods to resolve the anti-patterns by detecting and treating 15 different design’s semantic and structural anti-patterns that occurred in analyzing of 29 mobile applications. We choose 29 mobile applications randomly. Selecting a browser is not a criterion in this method because the proposed method is applied on a design level. We demonstrate a semantic integration method to reduce the incidence of anti-patterns using the ontology merging on mobile applications.ResultsThe proposed method detected 15 semantic and structural design anti-patterns which have appeared 1,262 times in a random sample of 29 mobile applications. The proposed method introduced a new classification of the anti-patterns divided into four groups. “The anti-patterns in the class group” is the most group that has the maximum occurrences of anti-patterns and “The anti-patterns in the operation group” is the smallest one that has the minimum occurrences of the anti-patterns which are detected by the proposed method. The results also showed the correlation between the selected tools which we used as Modelio, the Protégé platform, and the OLED editor of the OntoUML. The results showed that there was a high positive relation between Modelio and Protégé which implies that the combination between both increases the accuracy level of the detection of anti-patterns. In the evaluation and analyzing the suitable integration method, we applied the different methods on homogeneous mobile applications and found that using ontology increased the detection percentage approximately by 11.3% in addition to guaranteed consistency.

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“…The semantic design and features of this CDSS have been published in the medical semantics informatics community [10]. One key feature of this system is the personalized imaging protocol [11]: The system guides the operator through a structured acquisition of decisive ultrasound images to optimize the diagnostic pathway. We deemed this system “intelligent” because these personalized imaging protocols are not precalculated, but dynamically derived by the system (by SPARQL queries on the early pregnancy ontology of the knowledge base) from the guided image analysis of the current case.…”

Section: Introductionmentioning

confidence: 99%

“…In a preliminary study, this novel system demonstrated efficient support for a precise ultrasound image analysis, with a precision of 83% for the identification of signs in a series of 208 retrospectively collected ultrasound images of various types of ectopic pregnancies [10,11].…”

Section: Introductionmentioning

confidence: 99%

A Novel Intelligent Scan Assistant System for Early Pregnancy Diagnosis by Ultrasound: Clinical Decision Support System Evaluation Study

Dhombres¹,

Maurice²,

Guilbaud³

et al. 2019

J Med Internet Res

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Background Early pregnancy ultrasound scans are usually performed by nonexpert examiners in obstetrics/gynecology (OB/GYN) emergency departments. Establishing the precise diagnosis of pregnancy location is key for appropriate management of early pregnancies, and experts are usually able to locate a pregnancy in the first scan. A decision-support system based on a semantic, expert-validated knowledge base may improve the diagnostic performance of nonexpert examiners for early pregnancy transvaginal ultrasound. Objective This study aims to evaluate a novel Intelligent Scan Assistant System for early pregnancy ultrasound to diagnose the pregnancy location and determine the image quality. Methods Two trainees performed virtual transvaginal ultrasound examinations of early pregnancy cases with and without the system. The ultrasound images and reports were blindly reviewed by two experts using scoring methods. A diagnosis of pregnancy location and ultrasound image quality were compared between scans performed with and without the system. Results Each trainee performed a virtual vaginal examination for all 32 cases with and without use of the system. The analysis of the 128 resulting scans showed higher quality of the images (quality score: +23%; P <.001), less images per scan (4.6 vs 6.3 [without the CDSS]; P <.001), and higher confidence in reporting conclusions (trust score: +20%; P <.001) with use of the system. Further, use of the system cost an additional 8 minutes per scan. We observed a correct diagnosis of pregnancy location in 39 (61%) and 52 (81%) of 64 scans in the nonassisted mode and assisted mode, respectively. Additionally, an exact diagnosis (with precise ectopic location) was made in 30 (47%) and 49 (73%) of the 64 scans without and with use of the system, respectively. These differences in diagnostic performance (+20% for correct location diagnosis and +30% for exact diagnosis) were both statistically significant ( P =.002 and P <.001, respectively). Conclusions The Intelligent Scan Assistant System is based on an expert-validated knowledge base and demonstrates significant improvement in early pregnancy scanning, both in diagnostic performance (pregnancy location and precise diagnosis) and scan quality (selection of images, confidence, and image quality).

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Towards ontology-based decision support systems for complex ultrasound diagnosis in obstetrics and gynecology

Cited by 25 publications

References 30 publications

Obstetric ultrasound: where are we and where are we going?

Obstetric ultrasound: where are we and where are we going?

Reverse engineering approach for improving the quality of mobile applications

A Novel Intelligent Scan Assistant System for Early Pregnancy Diagnosis by Ultrasound: Clinical Decision Support System Evaluation Study

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