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, 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.
Necrotising enterocolitis (NEC) is often managed with a temporary enterostomy. Neonates with enterostomy are at risk of growth retardation during critical neurodevelopment. We examined their growth using z-score. We identified all patients with enterostomy from NEC in two neonatal surgical units (NSU) during January 2012–December 2016. Weight-for-age z-score was calculated at birth, stoma formation and closure, noting severely underweight as z < − 3. We compared those kept in NSU until stoma closure with those discharged to local units or home (LU/H) with a stoma. A total of 74 patients were included. By stoma closure, 66 (89%) had deteriorated in z-score with 31 (42%) being severely underweight. There was no difference in z-score at stoma closure between NSU and LU/H despite babies sent to LU/H having a more distal stoma, higher birth weight and gestational age. Babies in LU/H spent a much shorter period on parenteral nutrition while living with their stoma for longer, many needing readmission.Conclusion: Growth failure is a common and severe problem in babies living with enterostomy following NEC. z-score allowed growth trajectory to be accounted for in nutrition prescription and timing of stoma closure. Care during this period should be focused on minimising harm.What is Known:• Necrotising enterocolitis (NEC) is a life-threatening condition affecting predominately premature and very low birth weight neonates. Emergency treatment with temporary enterostomy often leads to growth failure.• There is no consensus on the optimal timing for stoma reversal, hence prolonging impact on growth during crucial developmental periods. Both malnutrition and surgical NEC are independently associated with poor neurodevelopment outcome.What is New:• Our study found growth in 89% of babies deteriorated while living with a stoma, with 42% having a weight-for-age z-score < − 3, meeting the WHO criteria of being severely underweight, despite judicial use of parenteral nutrition. Applying z-score to weight measurements will allow growth trajectory to be accounted for in clinical decisions, including nutrition prescription (both enteral and parenteral), and guide timing of stoma closure.• Surgeons who target stoma closure at a certain weight risk waiting for an indefinite period of time, during which babies’ growth may falter.
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.
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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 interpretation of radiological findings, especially abdominal radiography (AR) and abdominal ultrasound (AUS). Interexpert 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 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 labeling of imaging data to create big-data for NEC databases.
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