Classification of neonatal diseases with limited thermal Image data

Ervural, Saim; Ceylan, Murat

doi:10.1007/s11042-021-11391-0

Cited by 5 publications

(3 citation statements)

References 49 publications

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“… Study Approach Purpose Dataset Type of data (image/non-image) Performance Pros(+) Cons(-) Hauptmann et al, 2019 187 3D (2D plus time) CNN architecture Ability of CNNs to reconstruct highly accelerated radial real‐time data in patients with congenital heart disease 250 CHD patients. Cardiovascular MRI with cine images +Potential use of a CNN for reconstruction real time radial data Lei et al, 2022 158 MobileNet-V2 CNN Detect PDA with AI 300 patients 461 echocardiograms Echocardiography 88% (AUC) +Diagnosis of PDA with AI - Does not detect the position of PDA Ornek et al, 2021 189 VGG16 (CNN) To focus on dedicated regions to monitor the neonates and decides the health status of the neonates (healthy/unhealthy) 38 neonates 3800 Neonatal thermograms 95% (accuracy) +Known with this study how VGG16 decides on neonatal thermograms -Without clinical explanation Ervural et al, 2021 190 Data Augmentation and CNN Detect health status of neonates 44 neonates 880 images Neonatal thermograms 62.2% to 94.5% (accuracy) +Significant results with data augmentation -Less clinically applicable -Small dataset Ervural et al, 2021 191 Deep siamese neural network(D-SNN) Prediagnosis to experts in disease detection in neonates 67 neonates, 1340 images Neonatal thermograms 99.4% (infection diseases accuracy in 96.4% (oesophageal atresia accuracy), 97.4% (in intestinal atresia-accuracy, 94.02% (necrotising enterocolitis accuracy) +D-SNN is effective in the classification of neonatal diseases with limited data -Small sample size Ceschin et al, 2018 188 3D CNNs Automated classification of ...…”

Section: Resultsmentioning

confidence: 88%

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: 88%

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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“…Furthermore, Nagy et al and Khanam et al [ 19 , 21 ] both proposed an algorithm for the measurement of pulse and breathing rate. Simultaneously, Ervural et al [ 22 ] classified neonatal diseases using thermographic data in combination with CNNs. Although these DL-based approaches showed promising results, the models were trained on relatively small neonatal image datasets.…”

Section: Introductionmentioning

confidence: 99%

A Setup for Camera-Based Detection of Simulated Pathological States Using a Neonatal Phantom

Voss

Lyra

Blase

et al. 2022

Sensors

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Premature infants are among the most vulnerable patients in a hospital. Due to numerous complications associated with immaturity, a continuous monitoring of vital signs with a high sensitivity and accuracy is required. Today, wired sensors are attached to the patient’s skin. However, adhesive electrodes can be potentially harmful as they can damage the very thin immature skin. Although unobtrusive monitoring systems using cameras show the potential to replace cable-based techniques, advanced image processing algorithms are data-driven and, therefore, need much data to be trained. Due to the low availability of public neonatal image data, a patient phantom could help to implement algorithms for the robust extraction of vital signs from video recordings. In this work, a camera-based system is presented and validated using a neonatal phantom, which enabled a simulation of common neonatal pathologies such as hypo-/hyperthermia and brady-/tachycardia. The implemented algorithm was able to continuously measure and analyze the heart rate via photoplethysmography imaging with a mean absolute error of 0.91 bpm, as well as the distribution of a neonate’s skin temperature with a mean absolute error of less than 0.55 °C. For accurate measurements, a temperature gain offset correction on the registered image from two infrared thermography cameras was performed. A deep learning-based keypoint detector was applied for temperature mapping and guidance for the feature extraction. The presented setup successfully detected several levels of hypo- and hyperthermia, an increased central-peripheral temperature difference, tachycardia and bradycardia.

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“…They made a custom perforation in the top of the incubator, while covering the hole with plastic wrap for heat preservation during the 2-3 minutes of recording. In more recent studies, researchers have employed infrared thermal imaging for diverse neonatal monitoring applications such as respiration estimation by evaluating the change in body temperature in different ROIs from the patient [61], in the detection of various cardiovascular, abdominal, or pulmonary neonatal conditions observable from body temperature variation [62], in the identification of sleep propensity by analyzing distal skin temperatures before sleep [63], or in hypothermia prevention in preterm newborns post birth by monitoring the overall body temperature comparing peripheral and central temperatures from the foot and abdomen regions, respectively [64].…”

Section: Video-based Neonatal Patient Monitoringmentioning

confidence: 99%

Machine Vision for Patient Monitoring in the Neonatal Intensive Care Unit

Dosso¹

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Continuous patient monitoring of newborns in the neonatal intensive care unit (NICU) is often performed with wired sensors which can be cumbersome, can interfere with parental bonding, and can irritate the patient's fragile skin. Non-contact video-based patient monitoring systems are therefore a preferrable solution. While a multitude of high-performing machine vision technologies have been successfully implemented on an adult population, such methods often fail in neonatal population. In this thesis, we assess state-of-the-art adult-based methods to bridge the gap to an understudied neonatal population in the NICU environment. To this end, several important machine vision concepts are investigated, including scene understanding, image classification, face detection, semantic segmentation, motion detection, face tracking, and heart rate estimation. In each of these areas, we assess the state-of-the-art and identify its applicability to a neonatal population. In cases where serious limitations are observed, this thesis pushes the state-of-the-art

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Classification of neonatal diseases with limited thermal Image data

Cited by 5 publications

References 49 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

A Setup for Camera-Based Detection of Simulated Pathological States Using a Neonatal Phantom

Machine Vision for Patient Monitoring in the Neonatal Intensive Care Unit

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