Diagnosis of focal liver lesions from ultrasound using deep learning

Schmauch, Benoît; Hérent, Paul; Jehanno, Paul; Dehaene, Olivier; Saillard, Charlie; Aubé, Christophe; Luciani, Alain; Lassau, Nathalie; Jégou, Simon

doi:10.1016/j.diii.2019.02.009

Cited by 117 publications

(79 citation statements)

References 15 publications

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“…Several studies have applied deep learning to liver US imaging to detect or characterize focal liver lesions [13][14][15][16][17]. These studies are summarized in Table 2.…”

Section: Focal Liver Diseasementioning

confidence: 99%

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Current status of deep learning applications in abdominal ultrasonography

Song

2021

Ultrasonography

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Ultrasonography. 2020 Sep 2. Epub ahead of print Deep learning is one of the most popular artificial intelligence techniques used in the medical field. Although it is at an early stage compared to deep learning analyses of computed tomography or magnetic resonance imaging, studies applying deep learning to ultrasound imaging have been actively conducted. This review analyzes recent studies that applied deep learning to ultrasound imaging of various abdominal organs and explains the challenges encountered in these applications.

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“…Several studies have applied deep learning to liver US imaging to detect or characterize focal liver lesions [13][14][15][16][17]. These studies are summarized in Table 2.…”

Section: Focal Liver Diseasementioning

confidence: 99%

“…Schmauch et al [16] used the dataset that was provided during a public challenge during the 2018 Journées Francophones de Radiologie in Paris, France. Although their model was tested on the dataset by the challenge organizers, no detailed information was provided as to how the dataset was collected or what lesions it contained.…”

Section: Focal Liver Diseasementioning

confidence: 99%

Current status of deep learning applications in abdominal ultrasonography

Song

2021

Ultrasonography

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“…AI is already being used with clinical ultrasound, although the data are admittedly limited at this time. In a straightforward study, a dataset of 376 liver ultrasound scans from multiple institutions were used to examine the ability of AI to differentiate between benign liver lesions . All images were analyzed, cropped, and adjusted to a standard size, then normalized for echointensity using upper portions of each image.…”

Section: Artificial Intelligencementioning

confidence: 99%

Emerging technologies in neuromuscular ultrasound

Hobson‐Webb

2020

Muscle and Nerve

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Neuromuscular ultrasound is an accepted and valuable element in the evaluation of peripheral nerve and muscle disease. However, ultrasound has several limitations to consider, including operator dependency and lack of a viable contrast agent. Fortunately, new technological advances show promise in resolving these issues. Ultrahigh resolution ultrasound enables imaging of the nerve at the fascicular level. Shear wave elastography imaging can provide measures of tissue stiffness that can act as a surrogate measure of nerve and muscle health. Photoacoustic imaging may overcome neuromuscular ultrasound's current lack of contrast agents to detect inflammation and other functional changes within nerve and muscle, while artificial intelligence stands to address operator dependency and improve diagnostic imaging. The basic principles of each of these technologies are discussed along with current research and potential future applications in neuromuscular imaging. K E Y W O R D S artificial intelligence, elastography, high resolution ultrasound, neuromuscular ultrasound, photoacoustic imaging

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“…The human yield when characterizing a liver lesion from ultrasound images is limited. Schmauch et al[ 9 ] designed a DL system able to detect and classify space-occupying lesions in the liver as benign or malignant. After a supervised training using a database of 367 images together with the radiological reports, the resulting algorithm detected and characterized the lesions with a mean receiver operating characteristic of 0.93 and 0.916, respectively.…”

Section: Ai In the Diagnosis Of Hccmentioning

confidence: 99%

“…After a supervised training using a database of 367 images together with the radiological reports, the resulting algorithm detected and characterized the lesions with a mean receiver operating characteristic of 0.93 and 0.916, respectively. Although the system requires validation, it could increase the diagnostic yield of ultrasound and warn of possibly malignant lesions[ 9 ].…”

Section: Ai In the Diagnosis Of Hccmentioning

confidence: 99%

Application of artificial intelligence in the diagnosis and treatment of hepatocellular carcinoma: A review

Pérez

Grande

2020

WJG

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Although artificial intelligence (AI) was initially developed many years ago, it has experienced spectacular advances over the last 10 years for application in the field of medicine, and is now used for diagnostic, therapeutic and prognostic purposes in almost all fields. Its application in the area of hepatology is especially relevant for the study of hepatocellular carcinoma (HCC), as this is a very common tumor, with particular radiological characteristics that allow its diagnosis without the need for a histological study. However, the interpretation and analysis of the resulting images is not always easy, in addition to which the images vary during the course of the disease, and prognosis and treatment response can be conditioned by multiple factors. The vast amount of data available lend themselves to study and analysis by AI in its various branches, such as deep-learning (DL) and machine learning (ML), which play a fundamental role in decision-making as well as overcoming the constraints involved in human evaluation. ML is a form of AI based on automated learning from a set of previously provided data and training in algorithms to organize and recognize patterns. DL is a more extensive form of learning that attempts to simulate the working of the human brain, using a lot more data and more complex algorithms. This review specifies the type of AI used by the various authors. However, well-designed prospective studies are needed in order to avoid as far as possible any bias that may later affect the interpretability of the images and thereby limit the acceptance and application of these models in clinical practice. In addition, professionals now need to understand the true usefulness of these techniques, as well as their associated strengths and limitations.

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Diagnosis of focal liver lesions from ultrasound using deep learning

Cited by 117 publications

References 15 publications

Current status of deep learning applications in abdominal ultrasonography

Current status of deep learning applications in abdominal ultrasonography

Emerging technologies in neuromuscular ultrasound

Application of artificial intelligence in the diagnosis and treatment of hepatocellular carcinoma: A review

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