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Using Ai in a Collective disease model for better NEC detection.
2018 International Conference on Computing, Electronics & Communications Engineering (iCCECE)
Abstract:
Despite 60 years of research into necrotising enterocolitis (NEC), our understanding of the disease has not improved enough to achieve better outcomes. Even though NEC has remained the leading cause of death and poor outcomes in preterm infants, there remain vital questions on how to define, differentiate and detect the condition. Numerous international groups have recently highlighted NEC as a research priority and called for broader engagement of the scientific community to move the field forward. The three foremost barriers at present are lack of suitable definition(s), lack of clean datasets and consequently a lack of scope to gain sufficient insights from data. This research paper proposes a new direction of travel to advance neonatal gastro-intestinal monitoring and strengthen our efforts to gain better insights from global databases. An integrated machine learning based model is recommended to produce a comprehensive disease model to manage the complexity of this multi-variate disease. This intelligent disease model would be used in the daily neonatal settings to help aggregate data to support clinical decision making, better capture the complexity of each patient to enrich global datasets to create bigger and better data. This paper reviews current machine learning and CAD technologies in neonatology and suggests an innovative approach for an NEC disease model.
Using Ai to better detect NEC in Xrays, help mitigate risks associated with radiology staff shortages.
2019 International Conference on Computing, Electronics & Communications Engineering (iCCECE)
Abstract:
Despite decades of exploration into necrotising enterocolitis (NEC), we still lack the capacity to accurately diagnose the disease to improve outcomes in its management. Existing diagnostics struggle to delineate NEC from other neonatal intestinal diseases; it is also unable to highlight those likely to deteriorate to needing emergency life-saving surgery before it is too late. The diagnosis of NEC is heavily dependent on the interpretation of radiological findings, especially abdominal radiography (AR) and abdominal ultrasound (AUS). Inter-expert variability in interpreting AR imaging, and in the case of AUS, performing and interpreting the test, remains an unresolved challenge. With the compounding impact of the shrinking radiology workforce, a novel approach is imperative. Computer-assisted detection (CAD) and classification of abnormal pathology in medical imaging is a rapidly evolving field of clinical and biomedical research. This technology is widely used as a preliminary screening tool. This research paper proposes a deep learning-based model to classify AR images in an automated manner, generating class activation maps (CAM) from various imaging features consistent with NEC pathology, as agreed by expert consensus papers (in neonatology and paediatric radiology). It also compares it with conventional machine learning methods. The suggested model aims to produce heatmaps for various imaging features to highlight NEC pathology in AR (or in future AUS). Once the model is trained, validation is done through quantitative measures and visually by the attending radiologist (clinician) reviewing the validity of the colour maps highlighting the pathology of the AR image (future extension to AUS). As the volume of imaging data is increasing year by year, CAD can be a key strategy to assist radiology departments to meet service needs. This technology can greatly assist in screening for NEC, improving the detection of NEC and potentially aid in the earlier identification of disease. Furthermore, it can fast track research cost-effectively by creating big data through the automatic labelling of imaging data to create big-data for NEC databases.
Abdominal ultrasound should become part of standard care for early diagnosis and management of necrotising enterocolitis: a narrative review.
2019 Archives of Diseases in Childhood publication
Abstract:
Necrotising enterocolitis (NEC) is a leading cause of death and disability in preterm newborns. Early diagnosis through non-invasive investigations is a crucial strategy that can significantly improve outcomes. Hence, this review gives particular attention to the emerging role of abdominal ultrasound (AUS) in the early diagnosis of NEC, its performance against abdominal radiograph and the benefits of AUS use in daily practice. AUS has been used in the diagnosis and management of NEC for a couple of decades. However, its first-line use has been minimal, despite growing evidence demonstrating AUS can be a critical tool in the early diagnosis and management of NEC. In 2018, the NEC group of the International Neonatal Consortium recommended using AUS to detect pneumatosis and/or portal air in preterm NEC as part of the ‘Two out of three’ model. To facilitate widespread adoption, and future improvement in practice and outcomes, a collaboration between neonatologists, surgeons and radiologists is needed to generate standard operating procedures and indications for use for AUS. The pace and scale of the benefit generated by use of AUS can be amplified through use of computer-aided detection and artificial intelligence.
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