Assessment of germinal matrix hemorrhage on head ultrasound with deep learning algorithms

Kim, Kevin Y.; Nowrangi, Rajeev; McGehee, Arianna; Joshi, Neil; Acharya, Patricia T.

doi:10.1007/s00247-021-05239-w

Cited by 4 publications

(3 citation statements)

References 19 publications

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“…More studies could also look at how well DL works for accurately grading germinal matrix hemorrhages and maybe even small hemorrhages that a radiologist can see on an MRI but not on a head ultrasound. This could be useful in improving the diagnostic capabilities of head ultrasound in various clinical scenarios 157 .…”

Section: Resultsmentioning

confidence: 99%

“…In the above discussion, we have addressed the primary applications of DL in relation to disease prediction. These include DL for analyzing conditions such as PDA (patent ductus arteriosus) 158 , IVH (intraventricular ventricular hemorrhage) 155 , 157 , BPD (bronchopulmonary dysplasia) 170 , ROP (retinopathy of prematurity) 171 – 173 , retinal hemorrhage 174 diagnosis. This also includes DL applications for analyzing MR images 159 , 175 and combined with EHR data 176 , 177 for predicting neurocognitive outcome and mortality.…”

Section: Resultsmentioning

confidence: 99%

“…When it comes to DL applications, there has been less research conducted compared to ML applications. The focus of DL with imaging and non-imaging data focused on brain segmentation 159 , 169 , 175 , 177 , 188 , IVH diagnosis 157 , EEG analysis 184 , 185 , neurocognitive outcome 176 , PDA and ROP diagnosis 171 – 173 . Upcoming articles and research will surely be from the DL field, though.…”

Section: Discussionmentioning

confidence: 99%

See 2 more Smart Citations

The past, current, and future of neonatal intensive care units with artificial intelligence: a systematic review

Keles,

Bagci

2023

npj Digit. Med.

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Machine learning and deep learning are two subsets of artificial intelligence that involve teaching computers to learn and make decisions from any sort of data. Most recent developments in artificial intelligence are coming from deep learning, which has proven revolutionary in almost all fields, from computer vision to health sciences. The effects of deep learning in medicine have changed the conventional ways of clinical application significantly. Although some sub-fields of medicine, such as pediatrics, have been relatively slow in receiving the critical benefits of deep learning, related research in pediatrics has started to accumulate to a significant level, too. Hence, in this paper, we review recently developed machine learning and deep learning-based solutions for neonatology applications. We systematically evaluate the roles of both classical machine learning and deep learning in neonatology applications, define the methodologies, including algorithmic developments, and describe the remaining challenges in the assessment of neonatal diseases by using PRISMA 2020 guidelines. To date, the primary areas of focus in neonatology regarding AI applications have included survival analysis, neuroimaging, analysis of vital parameters and biosignals, and retinopathy of prematurity diagnosis. We have categorically summarized 106 research articles from 1996 to 2022 and discussed their pros and cons, respectively. In this systematic review, we aimed to further enhance the comprehensiveness of the study. We also discuss possible directions for new AI models and the future of neonatology with the rising power of AI, suggesting roadmaps for the integration of AI into neonatal intensive care units.

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Section: Resultsmentioning

confidence: 99%

Section: Resultsmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%

See 1 more Smart Citation

The past, current, and future of neonatal intensive care units with artificial intelligence: a systematic review

Keles,

Bagci

2023

npj Digit. Med.

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Automated segmentation of ventricular volumes and subarachnoid hemorrhage from computed tomography images: Evaluation of a rule-based pipeline approach

Butler,

Shah,

Ozgen

et al. 2024

Neuroradiol J

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Changes in ventricular size, related to brain edema and hydrocephalus, as well as the extent of hemorrhage are associated with adverse outcomes in patients with subarachnoid hemorrhage (SAH). Frequently, these are measured manually using consecutive non-contrast computed tomography scans. Here, we developed a rule-based approach which incorporates both intensity and spatial normalization and utilizes user-defined thresholds and anatomical templates to segment both lateral ventricle (LV) and SAH blood volumes automatically from CT images. The algorithmic segmentations were evaluated against two expert neuroradiologists on representative slices from 20 admission scans from aneurysmal SAH patients. Previous methods have been developed to automate this time-consuming task, but they lack user feedback and are hard to implement due to large-scale data and complex design processes. Our results using automatic ventricular segmentation aligned well with expert reviewers with a median Dice coefficient of 0.81, AUC of 0.91, sensitivity of 81%, and precision of 84%. Automatic segmentation of SAH blood was most reliable near the base of the brain with a median Dice coefficient of 0.51, an AUC of 0.75, precision of 68%, and sensitivity of 50%. Ultimately, we developed a rule-based method that is easily adaptable through user feedback, generates spatially normalized segmentations that are comparable regardless of brain morphology or acquisition conditions, and automatically segments LV with good overall reliability and basal SAH blood with good precision. Our approach could benefit longitudinal studies in patients with SAH by streamlining assessment of edema and hydrocephalus progression, as well as blood resorption.

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Deep Learning-based approach for screening neonatal cerebral lesions on ultrasound images in China: a stepwise, multicenter, early-stage clinical validation study

Zhou,

Lin,

Zhang

et al. 2024

Preprint

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Timely and accurate diagnosis of severe neonatal cerebral lesions is critical for preventing long-term neurological damage and addressing life-threatening conditions. Cranial ultrasound (CUS) is the primary screening tool, but the process is time-consuming and reliant on operator proficiency, leading to variability in both image quality and diagnostic outcomes across different radiologists. While artificial intelligence (AI) has made significant strides in many areas of healthcare, its application in CUS screening remains limited. In this study, we developed and evaluated a Neonatal Cerebral Lesions Screening system (NCLS) capable of automatically extracting standard views from CUS videos and identifying cases with severe cerebral lesions. The system was trained and validated using a dataset of 8,757 neonatal CUS images. It demonstrated strong performance, achieving an area under the curve (AUC) of 0.982 and 0.969, with sensitivities of 0.875 and 0.885 on internal and external validation sets, respectively. Furthermore, the NCLS outperformed junior radiologists (with 1–2 years of CUS diagnostic experience) and performed comparably to mid-level radiologists (with 3–7 years of experience), with 55.11% faster examination efficiency.

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Assessment of germinal matrix hemorrhage on head ultrasound with deep learning algorithms

Cited by 4 publications

References 19 publications

The past, current, and future of neonatal intensive care units with artificial intelligence: a systematic review

The past, current, and future of neonatal intensive care units with artificial intelligence: a systematic review

Automated segmentation of ventricular volumes and subarachnoid hemorrhage from computed tomography images: Evaluation of a rule-based pipeline approach

Deep Learning-based approach for screening neonatal cerebral lesions on ultrasound images in China: a stepwise, multicenter, early-stage clinical validation study

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