Deep learning for image-based liver analysis — A comprehensive review focusing on malignant lesions

Survarachakan, Shanmugapriya; Prasad, Pravda Jith Ray; Naseem, Rabia; Frutos, Javier Pérez de; Kumar, Rahul Prasanna; Langø, Thomas; Cheikh, Faouzi Alaya; Elle, Ole Jakob; Lindseth, Frank

doi:10.1016/j.artmed.2022.102331

Cited by 21 publications

(14 citation statements)

References 79 publications

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“…DL is a subset of machine learning which has become possible with increasing computing power. Compared to traditional machine learning algorithms and shallow networks, current DL algorithms are characterized by large amounts of processable data, high computational power, and large network size [ 58 , 59 ].…”

Section: Discussionmentioning

confidence: 99%

Deep learning for detection of age-related macular degeneration: A systematic review and meta-analysis of diagnostic test accuracy studies

Leng

Shi

et al. 2023

PLoS ONE

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Objective To evaluate the diagnostic accuracy of deep learning algorithms to identify age-related macular degeneration and to explore factors impacting the results for future model training. Methods Diagnostic accuracy studies published in PubMed, EMBASE, the Cochrane Library, and ClinicalTrails.gov before 11 August 2022 which employed deep learning for age-related macular degeneration detection were identified and extracted by two independent researchers. Sensitivity analysis, subgroup, and meta-regression were performed by Review Manager 5.4.1, Meta-disc 1.4, and Stata 16.0. The risk of bias was assessed using QUADAS-2. The review was registered (PROSPERO CRD42022352753). Results The pooled sensitivity and specificity in this meta-analysis were 94% (P = 0, 95% CI 0.94–0.94, I2 = 99.7%) and 97% (P = 0, 95% CI 0.97–0.97, I2 = 99.6%), respectively. The pooled positive likelihood ratio, negative likelihood ratio, diagnostic odds ratio, and the area under the curve value were 21.77(95% CI 15.49–30.59), 0.06 (95% CI 0.04–0.09), 342.41 (95% CI 210.31–557.49), and 0.9925, respectively. Meta-regression indicated that types of AMD (P = 0.1882, RDOR = 36.03) and layers of the network (P = 0.4878, RDOR = 0.74) contributed to the heterogeneity. Conclusions Convolutional neural networks are mostly adopted deep learning algorithms in age-related macular degeneration detection. Convolutional neural networks, especially ResNets, are effective in detecting age-related macular degeneration with high diagnostic accuracy. Types of age-related macular degeneration and layers of the network are the two essential factors that impact the model training process. Proper layers of the network will make the model more reliable. More datasets established by new diagnostic methods will be used to train deep learning models in the future, which will benefit for fundus application screening, long-range medical treatment, and reducing the workload of physicians.

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Section: Discussionmentioning

confidence: 99%

Deep learning for detection of age-related macular degeneration: A systematic review and meta-analysis of diagnostic test accuracy studies

Leng

Shi

et al. 2023

PLoS ONE

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“…Machine learning is a subfield of AI in which a program is designed to learn from experience using training data. On a research level, this process has already been evaluated in a variety of medical fields (e. g., detection of polyps during colonoscopy) [6,7]. Support vector machines (SVM) and artificial neural networks (ANN) are machine learning methods that can be applied to evaluate image data.…”

Section: Zusammenfassungmentioning

confidence: 99%

Artificial intelligence for the classification of focal liver lesions in ultrasound – a systematic review

et al. 2023

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Focal liver lesions are detected in about 15 % of abdominal ultrasound examinations. The diagnosis of frequent benign lesions can be determined reliably based on the characteristic B-mode appearance of cysts, hemangiomas or typical focal fatty changes. In case of focal liver lesions which remain unclear in B-mode ultrasound, contrast enhanced ultrasound (CEUS) increases diagnostic accuracy for the distinction of benign from malignant liver lesions. Artificial intelligence describes applications which try to emulate human intelligence, at least in subfields such as the classification of images. Since ultrasound is considered to be a particularly examiner-dependent technique, the application of artificial intelligence could be an interesting approach for an objective and accurate diagnosis. In this systematic review we analyzed how artificial intelligence can be used to classify the dignity and entity of focal liver lesions on the basis of B-mode or CEUS data. Based on a structured search on Scopus, Web of Science, PubMed and IEEE we found 52 studies, which met the inclusion criteria. Studies showed good diagnostic performance for both the classification of dignity and the differentiation of individual tumor entities. The results could be improved by inclusion of clinical parameters and were comparable to those of experienced investigators in terms of diagnostic accuracy. However, due to the limited spectrum of lesions included in the studies and a lack of independent validation cohorts, the transfer of the results into clinical practice is limited. Raumforderungen der Leber finden sich in ca. 15 % abdominaler Ultraschalluntersuchungen. Die Diagnose der meisten benignen Läsionen kann bei charakteristischem Befund, z.B. bei Zysten, Hämangiomen und typischen fokalen Fettverteilungsvarianten oft bereits in der B-Bild-Sonographie zuverlässig gestellt werden. Bei unklaren Befunden erhöht der Einsatz der Kontrastmittelsonographie (CEUS) die diagnostische Treffsicherheit bezüglich der Unterscheidung von benignen und malignen Leberläsionen. Künstliche Intelligenz beschreibt Anwendungen, die versuchen in Bereichen wie der Klassifikation von Bildern humane Intelligenz nachzubilden. Da die Sonographie als besonders untersucherabhängig gilt, könnte die Anwendung künstlicher Intelligenz ein interessanter Ansatz für eine objektive und treffsichere Diagnose sein. Im Rahmen dieser systematischen Übersichtsarbeit haben wir analysiert, wie gut künstliche Intelligenz die Dignität und Entität von Leberläsionen auf Basis von B-Bild- oder CEUS-Daten bestimmen kann. Basierend auf einer strukturierten Recherche in Scopus, Web of Science, PubMed und IEEE fanden wir 52 Studien, welche die Einschlusskriterien erfüllten. Es zeigte sich eine gute diagnostische Genauigkeit sowohl für die Differenzierung der Dignität als auch der verschiedener Tumorentitäten. Die Ergebnisse ließen sich durch die zusätzliche Berücksichtigung klinischer Parameter verbessern und waren bezüglich der diagnostischen Genauigkeit mit der erfahrener Untersucher vergleichbar. Aufgrund des begrenzten Spektrums untersuchter Läsionen und häufig fehlender unabhängiger Validierungskohorten sind die Ergebnisse jedoch nur begrenzt auf die klinische Anwendung übertragbar.

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“…Li [38]. Literature [39] summarized more than 90 related papers, and compared the evaluation indicators, showed that the hybrid model performs better in liver disease and lesion segmentation tasks, and reviewed the existing algorithms and their performance in various image analysis tasks.…”

Section: Related Workmentioning

confidence: 99%

Research on liver cancer segmentation method based on PCNN image processing and SE-ResUnet

Zang

Lǐ

et al. 2023

Preprint

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As one of the malignant tumors with high mortality, the initial symptoms of liver cancer are not obvious. In addition, the liver is the largest internal organ of the human body, and its structure and distribution are relatively complex. Therefore, in order to help doctors judge liver cancer more accurately, this paper proposes a variant model based on Unet network. Before segmentation, the image is preprocessed, and Pulse Coupled Neural Network(PCNN) algorithm is used to filterthe image adaptively to make the image clearer. For the segmentation model, the SE module is used as the input of the residual network, and then its output is connected to the Unet model through bilinear interpolation to perform the down-sampling and up-sampling operations. The Lits and collected liver cancer images are combined into a new data set for training. The results show that this method has better segmentation performance and accuracy than the original Unet method, and the dice coefficient, mIou and other evaluation indicators have increased by at least 2.1%, which is a method that can be applied to cancer segmentation.

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Deep learning for image-based liver analysis — A comprehensive review focusing on malignant lesions

Cited by 21 publications

References 79 publications

Deep learning for detection of age-related macular degeneration: A systematic review and meta-analysis of diagnostic test accuracy studies

Deep learning for detection of age-related macular degeneration: A systematic review and meta-analysis of diagnostic test accuracy studies

Artificial intelligence for the classification of focal liver lesions in ultrasound – a systematic review

Research on liver cancer segmentation method based on PCNN image processing and SE-ResUnet

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