1
|
Zhang Z, Li G, Wang Z, Xia F, Zhao N, Nie H, Ye Z, Lin JS, Hui Y, Liu X. Deep-learning segmentation to select liver parenchyma for categorizing hepatic steatosis on multinational chest CT. Sci Rep 2024; 14:11987. [PMID: 38796521 PMCID: PMC11127985 DOI: 10.1038/s41598-024-62887-2] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/10/2023] [Accepted: 05/22/2024] [Indexed: 05/28/2024] Open
Abstract
Unenhanced CT scans exhibit high specificity in detecting moderate-to-severe hepatic steatosis. Even though many CTs are scanned from health screening and various diagnostic contexts, their potential for hepatic steatosis detection has largely remained unexplored. The accuracy of previous methodologies has been limited by the inclusion of non-parenchymal liver regions. To overcome this limitation, we present a novel deep-learning (DL) based method tailored for the automatic selection of parenchymal portions in CT images. This innovative method automatically delineates circular regions for effectively detecting hepatic steatosis. We use 1,014 multinational CT images to develop a DL model for segmenting liver and selecting the parenchymal regions. The results demonstrate outstanding performance in both tasks. By excluding non-parenchymal portions, our DL-based method surpasses previous limitations, achieving radiologist-level accuracy in liver attenuation measurements and hepatic steatosis detection. To ensure the reproducibility, we have openly shared 1014 annotated CT images and the DL system codes. Our novel research contributes to the refinement the automated detection methodologies of hepatic steatosis on CT images, enhancing the accuracy and efficiency of healthcare screening processes.
Collapse
Affiliation(s)
- Zhongyi Zhang
- Department of Nephrology, Multidisciplinary Innovation Center for Nephrology, The Second Hospital of Shandong University, Shandong University, Jinan, 250033, Shandong, China
| | - Guixia Li
- Department of Nephrology, Shenzhen Third People's Hospital, the Second Affiliated Hospital of Southern University of Science and Technology, Shenzhen, 518112, Guangdong, China
| | - Ziqiang Wang
- Department of Nephrology, The First Affiliated Hospital of Hainan Medical University, Haikou, 570102, Hainan, China
| | - Feng Xia
- Department of Cardiovascular Surgery, Wuhan Asia General Hospital, Wuhan, 430000, Hubei, China
| | - Ning Zhao
- The First Clinical Medical School, Shanxi Medical University, Taiyuan, 030001, Shanxi, China
| | - Huibin Nie
- Department of Nephrology, Chengdu First People's Hospital, Chengdu, 610021, Sichuan, China
| | - Zezhong Ye
- Independent Researcher, Boston, MA, 02115, USA
| | - Joshua S Lin
- Keck School of Medicine, University of Southern California, Los Angeles, CA, 90033, USA
| | - Yiyi Hui
- Department of Medical Imaging, Shandong Provincial Hospital Affiliated to Shandong First Medical University, Jinan, 250021, Shandong, China.
| | - Xiangchun Liu
- Department of Nephrology, Multidisciplinary Innovation Center for Nephrology, The Second Hospital of Shandong University, Shandong University, Jinan, 250033, Shandong, China.
| |
Collapse
|
2
|
Li S, Li XG, Zhou F, Zhang Y, Bie Z, Cheng L, Peng J, Li B. Automated segmentation of liver and hepatic vessels on portal venous phase computed tomography images using a deep learning algorithm. J Appl Clin Med Phys 2024:e14397. [PMID: 38773719 DOI: 10.1002/acm2.14397] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/27/2024] [Revised: 04/20/2024] [Accepted: 04/29/2024] [Indexed: 05/24/2024] Open
Abstract
BACKGROUND CT-image segmentation for liver and hepatic vessels can facilitate liver surgical planning. However, time-consuming process and inter-observer variations of manual segmentation have limited wider application in clinical practice. PURPOSE Our study aimed to propose an automated deep learning (DL) segmentation algorithm for liver and hepatic vessels on portal venous phase CT images. METHODS This retrospective study was performed to develop a coarse-to-fine DL-based algorithm that was trained, validated, and tested using private 413, 52, and 50 portal venous phase CT images, respectively. Additionally, the performance of the DL algorithm was extensively evaluated and compared with manual segmentation using an independent clinical dataset of preoperative contrast-enhanced CT images from 44 patients with hepatic focal lesions. The accuracy of DL-based segmentation was quantitatively evaluated using the Dice Similarity Coefficient (DSC) and complementary metrics [Normalized Surface Dice (NSD) and Hausdorff distance_95 (HD95) for liver segmentation, Recall and Precision for hepatic vessel segmentation]. The processing time for DL and manual segmentation was also compared. RESULTS Our DL algorithm achieved accurate liver segmentation with DSC of 0.98, NSD of 0.92, and HD95 of 1.52 mm. DL-segmentation of hepatic veins, portal veins, and inferior vena cava attained DSC of 0.86, 0.89, and 0.94, respectively. Compared with the manual approach, the DL algorithm significantly outperformed with better segmentation results for both liver and hepatic vessels, with higher accuracy of liver and hepatic vessel segmentation (all p < 0.001) in independent 44 clinical data. In addition, the DL method significantly reduced the manual processing time of clinical postprocessing (p < 0.001). CONCLUSIONS The proposed DL algorithm potentially enabled accurate and rapid segmentation for liver and hepatic vessels using portal venous phase contrast CT images.
Collapse
Affiliation(s)
- Shengwei Li
- Minimally Invasive Tumor Therapy Center, Beijing Hospital, Peking Union Medical College, Beijing, China
| | - Xiao-Guang Li
- Minimally Invasive Tumor Therapy Center, Beijing Hospital, Peking Union Medical College, Beijing, China
| | - Fanyu Zhou
- Minimally Invasive Tumor Therapy Center, Beijing Hospital, Peking Union Medical College, Beijing, China
| | - Yumeng Zhang
- Minimally Invasive Tumor Therapy Center, Beijing Hospital, Peking Union Medical College, Beijing, China
| | - Zhixin Bie
- Minimally Invasive Tumor Therapy Center, Beijing Hospital, Peking Union Medical College, Beijing, China
| | - Lin Cheng
- Minimally Invasive Tumor Therapy Center, Beijing Hospital, Peking Union Medical College, Beijing, China
| | - Jinzhao Peng
- Minimally Invasive Tumor Therapy Center, Beijing Hospital, Peking Union Medical College, Beijing, China
| | - Bin Li
- Minimally Invasive Tumor Therapy Center, Beijing Hospital, Peking Union Medical College, Beijing, China
| |
Collapse
|
3
|
Lu F, Meng Y, Song X, Li X, Liu Z, Gu C, Zheng X, Jing Y, Cai W, Pinyopornpanish K, Mancuso A, Romeiro FG, Méndez-Sánchez N, Qi X. Artificial Intelligence in Liver Diseases: Recent Advances. Adv Ther 2024; 41:967-990. [PMID: 38286960 DOI: 10.1007/s12325-024-02781-5] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/12/2023] [Accepted: 01/03/2024] [Indexed: 01/31/2024]
Abstract
Liver diseases cause a significant burden on public health worldwide. In spite of great advances during recent years, there are still many challenges in the diagnosis and treatment of liver diseases. During recent years, artificial intelligence (AI) has been widely used for the diagnosis, risk stratification, and prognostic prediction of various diseases based on clinical datasets and medical images. Accumulative studies have shown its performance for diagnosing patients with nonalcoholic fatty liver disease and liver fibrosis and assessing their severity, and for predicting treatment response and recurrence of hepatocellular carcinoma, outcomes of liver transplantation recipients, and risk of drug-induced liver injury. Herein, we aim to comprehensively summarize the current evidence regarding diagnostic, prognostic, and/or therapeutic role of AI in these common liver diseases.
Collapse
Affiliation(s)
- Feifei Lu
- College of Medicine and Biological Information Engineering, Northeastern University, Shenyang, China
- Liver Cirrhosis Study Group, Department of Gastroenterology, General Hospital of Northern Theater Command, No. 83 Wenhua Road, Shenyang, 110840, Liaoning Province, China
| | - Yao Meng
- Liver Cirrhosis Study Group, Department of Gastroenterology, General Hospital of Northern Theater Command, No. 83 Wenhua Road, Shenyang, 110840, Liaoning Province, China
- Postgraduate College, Dalian Medical University, Dalian, China
| | - Xiaoting Song
- Liver Cirrhosis Study Group, Department of Gastroenterology, General Hospital of Northern Theater Command, No. 83 Wenhua Road, Shenyang, 110840, Liaoning Province, China
- Postgraduate College, Dalian Medical University, Dalian, China
| | - Xiaotong Li
- Liver Cirrhosis Study Group, Department of Gastroenterology, General Hospital of Northern Theater Command, No. 83 Wenhua Road, Shenyang, 110840, Liaoning Province, China
- Postgraduate College, China Medical University, Shenyang, China
| | - Zhuang Liu
- Liver Cirrhosis Study Group, Department of Gastroenterology, General Hospital of Northern Theater Command, No. 83 Wenhua Road, Shenyang, 110840, Liaoning Province, China
- Postgraduate College, China Medical University, Shenyang, China
| | - Chunru Gu
- Liver Cirrhosis Study Group, Department of Gastroenterology, General Hospital of Northern Theater Command, No. 83 Wenhua Road, Shenyang, 110840, Liaoning Province, China
- Postgraduate College, China Medical University, Shenyang, China
| | - Xiaojie Zheng
- Liver Cirrhosis Study Group, Department of Gastroenterology, General Hospital of Northern Theater Command, No. 83 Wenhua Road, Shenyang, 110840, Liaoning Province, China
- Postgraduate College, China Medical University, Shenyang, China
| | - Yi Jing
- Neusoft Research of Intelligent Healthcare Technology, Co. Ltd., Shenyang, China
| | - Wei Cai
- Neusoft Research of Intelligent Healthcare Technology, Co. Ltd., Shenyang, China
| | - Kanokwan Pinyopornpanish
- Department of Internal Medicine, Faculty of Medicine, Chiang Mai University, Chiang Mai, Thailand
| | - Andrea Mancuso
- Medicina Interna 1, Azienda di Rilievo Nazionale Ad Alta Specializzazione Civico-Di Cristina-Benfratelli, Palermo, Italy.
| | | | - Nahum Méndez-Sánchez
- Liver Research Unit, Medica Sur Clinic and Foundation, National Autonomous University of Mexico, Mexico City, Mexico.
| | - Xingshun Qi
- College of Medicine and Biological Information Engineering, Northeastern University, Shenyang, China.
- Liver Cirrhosis Study Group, Department of Gastroenterology, General Hospital of Northern Theater Command, No. 83 Wenhua Road, Shenyang, 110840, Liaoning Province, China.
- Postgraduate College, Dalian Medical University, Dalian, China.
- Postgraduate College, China Medical University, Shenyang, China.
| |
Collapse
|
4
|
Jimenez Ramos M, Kendall TJ, Drozdov I, Fallowfield JA. A data-driven approach to decode metabolic dysfunction-associated steatotic liver disease. Ann Hepatol 2024; 29:101278. [PMID: 38135251 PMCID: PMC10907333 DOI: 10.1016/j.aohep.2023.101278] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 11/30/2023] [Accepted: 12/04/2023] [Indexed: 12/24/2023]
Abstract
Metabolic dysfunction-associated steatotic liver disease (MASLD), defined by the presence of liver steatosis together with at least one out of five cardiometabolic factors, is the most common cause of chronic liver disease worldwide, affecting around one in three people. Yet the clinical presentation of MASLD and the risk of progression to cirrhosis and adverse clinical outcomes is highly variable. It, therefore, represents both a global public health threat and a precision medicine challenge. Artificial intelligence (AI) is being investigated in MASLD to develop reproducible, quantitative, and automated methods to enhance patient stratification and to discover new biomarkers and therapeutic targets in MASLD. This review details the different applications of AI and machine learning algorithms in MASLD, particularly in analyzing electronic health record, digital pathology, and imaging data. Additionally, it also describes how specific MASLD consortia are leveraging multimodal data sources to spark research breakthroughs in the field. Using a new national-level 'data commons' (SteatoSITE) as an exemplar, the opportunities, as well as the technical challenges of large-scale databases in MASLD research, are highlighted.
Collapse
Affiliation(s)
- Maria Jimenez Ramos
- Centre for Inflammation Research, Institute for Regeneration and Repair, University of Edinburgh, Edinburgh BioQuarter, 4-5 Little France Drive, Edinburgh EH16 4UU, UK
| | - Timothy J Kendall
- Centre for Inflammation Research, Institute for Regeneration and Repair, University of Edinburgh, Edinburgh BioQuarter, 4-5 Little France Drive, Edinburgh EH16 4UU, UK; Edinburgh Pathology, University of Edinburgh, 51 Little France Crescent, Old Dalkeith Rd, Edinburgh EH16 4SA, UK
| | - Ignat Drozdov
- Bering Limited, 54 Portland Place, London, W1B 1DY, UK
| | - Jonathan A Fallowfield
- Centre for Inflammation Research, Institute for Regeneration and Repair, University of Edinburgh, Edinburgh BioQuarter, 4-5 Little France Drive, Edinburgh EH16 4UU, UK.
| |
Collapse
|
5
|
Jeon SK, Joo I, Park J, Kim JM, Park SJ, Yoon SH. Fully-automated multi-organ segmentation tool applicable to both non-contrast and post-contrast abdominal CT: deep learning algorithm developed using dual-energy CT images. Sci Rep 2024; 14:4378. [PMID: 38388824 PMCID: PMC10883917 DOI: 10.1038/s41598-024-55137-y] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/25/2023] [Accepted: 02/20/2024] [Indexed: 02/24/2024] Open
Abstract
A novel 3D nnU-Net-based of algorithm was developed for fully-automated multi-organ segmentation in abdominal CT, applicable to both non-contrast and post-contrast images. The algorithm was trained using dual-energy CT (DECT)-obtained portal venous phase (PVP) and spatiotemporally-matched virtual non-contrast images, and tested using a single-energy (SE) CT dataset comprising PVP and true non-contrast (TNC) images. The algorithm showed robust accuracy in segmenting the liver, spleen, right kidney (RK), and left kidney (LK), with mean dice similarity coefficients (DSCs) exceeding 0.94 for each organ, regardless of contrast enhancement. However, pancreas segmentation demonstrated slightly lower performance with mean DSCs of around 0.8. In organ volume estimation, the algorithm demonstrated excellent agreement with ground-truth measurements for the liver, spleen, RK, and LK (intraclass correlation coefficients [ICCs] > 0.95); while the pancreas showed good agreements (ICC = 0.792 in SE-PVP, 0.840 in TNC). Accurate volume estimation within a 10% deviation from ground-truth was achieved in over 90% of cases involving the liver, spleen, RK, and LK. These findings indicate the efficacy of our 3D nnU-Net-based algorithm, developed using DECT images, which provides precise segmentation of the liver, spleen, and RK and LK in both non-contrast and post-contrast CT images, enabling reliable organ volumetry, albeit with relatively reduced performance for the pancreas.
Collapse
Affiliation(s)
- Sun Kyung Jeon
- Department of Radiology, Seoul National University Hospital, Seoul National University College of Medicine, 101 Daehak-ro, Jongno-gu, Seoul, 03080, Korea
- Department of Radiology, Seoul National University College of Medicine, Seoul, Korea
| | - Ijin Joo
- Department of Radiology, Seoul National University Hospital, Seoul National University College of Medicine, 101 Daehak-ro, Jongno-gu, Seoul, 03080, Korea.
- Department of Radiology, Seoul National University College of Medicine, Seoul, Korea.
- Institute of Radiation Medicine, Seoul National University Medical Research Center Seoul National University Hospital, Seoul, Korea.
| | - Junghoan Park
- Department of Radiology, Seoul National University Hospital, Seoul National University College of Medicine, 101 Daehak-ro, Jongno-gu, Seoul, 03080, Korea
- Department of Radiology, Seoul National University College of Medicine, Seoul, Korea
| | | | | | - Soon Ho Yoon
- Department of Radiology, Seoul National University Hospital, Seoul National University College of Medicine, 101 Daehak-ro, Jongno-gu, Seoul, 03080, Korea
- Department of Radiology, Seoul National University College of Medicine, Seoul, Korea
- MEDICALIP. Co. Ltd., Seoul, Korea
| |
Collapse
|
6
|
Song I, Thompson EW, Verma A, MacLean MT, Duda J, Elahi A, Tran R, Raghupathy P, Swago S, Hazim M, Bhattaru A, Schneider C, Vujkovic M, Torigian DA, Kahn CE, Gee JC, Borthakur A, Kripke CM, Carson CC, Carr R, Jehangir Q, Ko YA, Litt H, Rosen M, Mankoff DA, Schnall MD, Shou H, Chirinos J, Damrauer SM, Serper M, Chen J, Rader DJ, Witschey WRT, Sagreiya H. Clinical correlates of CT imaging-derived phenotypes among lean and overweight patients with hepatic steatosis. Sci Rep 2024; 14:53. [PMID: 38167550 PMCID: PMC10761858 DOI: 10.1038/s41598-023-49470-x] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/24/2023] [Accepted: 12/08/2023] [Indexed: 01/05/2024] Open
Abstract
The objective of this study is to define CT imaging derived phenotypes for patients with hepatic steatosis, a common metabolic liver condition, and determine its association with patient data from a medical biobank. There is a need to further characterize hepatic steatosis in lean patients, as its epidemiology may differ from that in overweight patients. A deep learning method determined the spleen-hepatic attenuation difference (SHAD) in Hounsfield Units (HU) on abdominal CT scans as a quantitative measure of hepatic steatosis. The patient cohort was stratified by BMI with a threshold of 25 kg/m2 and hepatic steatosis with threshold SHAD ≥ - 1 HU or liver mean attenuation ≤ 40 HU. Patient characteristics, diagnoses, and laboratory results representing metabolism and liver function were investigated. A phenome-wide association study (PheWAS) was performed for the statistical interaction between SHAD and the binary characteristic LEAN. The cohort contained 8914 patients-lean patients with (N = 278, 3.1%) and without (N = 1867, 20.9%) steatosis, and overweight patients with (N = 1863, 20.9%) and without (N = 4906, 55.0%) steatosis. Among all lean patients, those with steatosis had increased rates of cardiovascular disease (41.7 vs 27.8%), hypertension (86.7 vs 49.8%), and type 2 diabetes mellitus (29.1 vs 15.7%) (all p < 0.0001). Ten phenotypes were significant in the PheWAS, including chronic kidney disease, renal failure, and cardiovascular disease. Hepatic steatosis was found to be associated with cardiovascular, kidney, and metabolic conditions, separate from overweight BMI.
Collapse
Affiliation(s)
- Isabel Song
- Department of Radiology, Perelman School of Medicine, University of Pennsylvania, 3400 Spruce Street, Philadelphia, PA, 19104, USA
| | - Elizabeth W Thompson
- Department of Radiology, Perelman School of Medicine, University of Pennsylvania, 3400 Spruce Street, Philadelphia, PA, 19104, USA
| | - Anurag Verma
- Department of Medicine, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
| | - Matthew T MacLean
- Department of Radiology, Perelman School of Medicine, University of Pennsylvania, 3400 Spruce Street, Philadelphia, PA, 19104, USA
- Department of Genetics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
| | - Jeffrey Duda
- Department of Radiology, Perelman School of Medicine, University of Pennsylvania, 3400 Spruce Street, Philadelphia, PA, 19104, USA
| | - Ameena Elahi
- Department of Radiology, Perelman School of Medicine, University of Pennsylvania, 3400 Spruce Street, Philadelphia, PA, 19104, USA
| | - Richard Tran
- Department of Radiology, Perelman School of Medicine, University of Pennsylvania, 3400 Spruce Street, Philadelphia, PA, 19104, USA
| | - Pavan Raghupathy
- Department of Radiology, Perelman School of Medicine, University of Pennsylvania, 3400 Spruce Street, Philadelphia, PA, 19104, USA
| | - Sophia Swago
- Department of Radiology, Perelman School of Medicine, University of Pennsylvania, 3400 Spruce Street, Philadelphia, PA, 19104, USA
| | - Mohamad Hazim
- Department of Radiology, Perelman School of Medicine, University of Pennsylvania, 3400 Spruce Street, Philadelphia, PA, 19104, USA
| | - Abhijit Bhattaru
- Department of Radiology, Perelman School of Medicine, University of Pennsylvania, 3400 Spruce Street, Philadelphia, PA, 19104, USA
| | - Carolin Schneider
- Department of Medicine, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
| | - Marijana Vujkovic
- Department of Genetics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
| | - Drew A Torigian
- Department of Radiology, Perelman School of Medicine, University of Pennsylvania, 3400 Spruce Street, Philadelphia, PA, 19104, USA
| | - Charles E Kahn
- Department of Radiology, Perelman School of Medicine, University of Pennsylvania, 3400 Spruce Street, Philadelphia, PA, 19104, USA
| | - James C Gee
- Department of Radiology, Perelman School of Medicine, University of Pennsylvania, 3400 Spruce Street, Philadelphia, PA, 19104, USA
| | - Arijitt Borthakur
- Department of Radiology, Perelman School of Medicine, University of Pennsylvania, 3400 Spruce Street, Philadelphia, PA, 19104, USA
| | - Colleen M Kripke
- Department of Medicine, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
| | - Christopher C Carson
- Department of Medicine, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
| | - Rotonya Carr
- Department of Medicine, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
- Department of Medicine, School of Medicine, University of Washington, Seattle, WA, USA
| | - Qasim Jehangir
- Department of Medicine, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
| | - Yi-An Ko
- Department of Genetics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
| | - Harold Litt
- Department of Radiology, Perelman School of Medicine, University of Pennsylvania, 3400 Spruce Street, Philadelphia, PA, 19104, USA
| | - Mark Rosen
- Department of Radiology, Perelman School of Medicine, University of Pennsylvania, 3400 Spruce Street, Philadelphia, PA, 19104, USA
| | - David A Mankoff
- Department of Radiology, Perelman School of Medicine, University of Pennsylvania, 3400 Spruce Street, Philadelphia, PA, 19104, USA
| | - Mitchell D Schnall
- Department of Radiology, Perelman School of Medicine, University of Pennsylvania, 3400 Spruce Street, Philadelphia, PA, 19104, USA
| | - Haochang Shou
- Department of Biostatistics, Epidemiology, and Informatics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
| | - Julio Chirinos
- Department of Medicine, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
| | - Scott M Damrauer
- Department of Surgery, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
| | - Marina Serper
- Department of Medicine, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
| | - Jinbo Chen
- Department of Biostatistics, Epidemiology, and Informatics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
| | - Daniel J Rader
- Department of Medicine, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
- Department of Genetics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
- Institute for Translational Medicine and Therapeutics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
| | - Walter R T Witschey
- Department of Radiology, Perelman School of Medicine, University of Pennsylvania, 3400 Spruce Street, Philadelphia, PA, 19104, USA
| | - Hersh Sagreiya
- Department of Radiology, Perelman School of Medicine, University of Pennsylvania, 3400 Spruce Street, Philadelphia, PA, 19104, USA.
| |
Collapse
|
7
|
Hu N, Yan G, Tang M, Wu Y, Song F, Xia X, Chan LWC, Lei P. CT-based methods for assessment of metabolic dysfunction associated with fatty liver disease. Eur Radiol Exp 2023; 7:72. [PMID: 37985560 PMCID: PMC10661153 DOI: 10.1186/s41747-023-00387-0] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/24/2023] [Accepted: 09/12/2023] [Indexed: 11/22/2023] Open
Abstract
Metabolic dysfunction-associated fatty liver disease (MAFLD), previously called metabolic nonalcoholic fatty liver disease, is the most prevalent chronic liver disease worldwide. The multi-factorial nature of MAFLD severity is delineated through an intricate composite analysis of the grade of activity in concert with the stage of fibrosis. Despite the preeminence of liver biopsy as the diagnostic and staging reference standard, its invasive nature, pronounced interobserver variability, and potential for deleterious effects (encompassing pain, infection, and even fatality) underscore the need for viable alternatives. We reviewed computed tomography (CT)-based methods for hepatic steatosis quantification (liver-to-spleen ratio; single-energy "quantitative" CT; dual-energy CT; deep learning-based methods; photon-counting CT) and hepatic fibrosis staging (morphology-based CT methods; contrast-enhanced CT biomarkers; dedicated postprocessing methods including liver surface nodularity, liver segmental volume ratio, texture analysis, deep learning methods, and radiomics). For dual-energy and photon-counting CT, the role of virtual non-contrast images and material decomposition is illustrated. For contrast-enhanced CT, normalized iodine concentration and extracellular volume fraction are explained. The applicability and salience of these approaches for clinical diagnosis and quantification of MAFLD are discussed.Relevance statementCT offers a variety of methods for the assessment of metabolic dysfunction-associated fatty liver disease by quantifying steatosis and staging fibrosis.Key points• MAFLD is the most prevalent chronic liver disease worldwide and is rapidly increasing.• Both hardware and software CT advances with high potential for MAFLD assessment have been observed in the last two decades.• Effective estimate of liver steatosis and staging of liver fibrosis can be possible through CT.
Collapse
Affiliation(s)
- Na Hu
- Department of Radiology, The Affiliated Hospital of Guizhou Medical University, Guiyang, China
| | - Gang Yan
- Department of Nuclear Medicine, The Affiliated Hospital of Guizhou Medical University, Guiyang, China
| | - Maowen Tang
- Department of Radiology, The Affiliated Hospital of Guizhou Medical University, Guiyang, China
| | - Yuhui Wu
- Department of Radiology, The Affiliated Hospital of Guizhou Medical University, Guiyang, China
| | - Fasong Song
- Department of Radiology, The Affiliated Hospital of Guizhou Medical University, Guiyang, China
| | - Xing Xia
- Department of Radiology, The Affiliated Hospital of Guizhou Medical University, Guiyang, China
| | - Lawrence Wing-Chi Chan
- Department of Health Technology and Informatics, The Hong Kong Polytechnic University, Kowloon, Hong Kong SAR, China.
| | - Pinggui Lei
- Department of Radiology, The Affiliated Hospital of Guizhou Medical University, Guiyang, China.
- Department of Health Technology and Informatics, The Hong Kong Polytechnic University, Kowloon, Hong Kong SAR, China.
| |
Collapse
|
8
|
Radiya K, Joakimsen HL, Mikalsen KØ, Aahlin EK, Lindsetmo RO, Mortensen KE. Performance and clinical applicability of machine learning in liver computed tomography imaging: a systematic review. Eur Radiol 2023; 33:6689-6717. [PMID: 37171491 PMCID: PMC10511359 DOI: 10.1007/s00330-023-09609-w] [Citation(s) in RCA: 5] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/03/2022] [Revised: 02/02/2023] [Accepted: 02/06/2023] [Indexed: 05/13/2023]
Abstract
OBJECTIVES Machine learning (ML) for medical imaging is emerging for several organs and image modalities. Our objectives were to provide clinicians with an overview of this field by answering the following questions: (1) How is ML applied in liver computed tomography (CT) imaging? (2) How well do ML systems perform in liver CT imaging? (3) What are the clinical applications of ML in liver CT imaging? METHODS A systematic review was carried out according to the guidelines from the PRISMA-P statement. The search string focused on studies containing content relating to artificial intelligence, liver, and computed tomography. RESULTS One hundred ninety-one studies were included in the study. ML was applied to CT liver imaging by image analysis without clinicians' intervention in majority of studies while in newer studies the fusion of ML method with clinical intervention have been identified. Several were documented to perform very accurately on reliable but small data. Most models identified were deep learning-based, mainly using convolutional neural networks. Potentially many clinical applications of ML to CT liver imaging have been identified through our review including liver and its lesion segmentation and classification, segmentation of vascular structure inside the liver, fibrosis and cirrhosis staging, metastasis prediction, and evaluation of chemotherapy. CONCLUSION Several studies attempted to provide transparent result of the model. To make the model convenient for a clinical application, prospective clinical validation studies are in urgent call. Computer scientists and engineers should seek to cooperate with health professionals to ensure this. KEY POINTS • ML shows great potential for CT liver image tasks such as pixel-wise segmentation and classification of liver and liver lesions, fibrosis staging, metastasis prediction, and retrieval of relevant liver lesions from similar cases of other patients. • Despite presenting the result is not standardized, many studies have attempted to provide transparent results to interpret the machine learning method performance in the literature. • Prospective studies are in urgent call for clinical validation of ML method, preferably carried out by cooperation between clinicians and computer scientists.
Collapse
Affiliation(s)
- Keyur Radiya
- Department of Gastroenterological Surgery at University Hospital of North Norway (UNN), Tromso, Norway.
- Department of Clinical Medicine, UiT The Arctic University of Norway, Tromso, Norway.
| | - Henrik Lykke Joakimsen
- Institute of Clinical Medicine, UiT The Arctic University of Norway, Tromso, Norway
- Centre for Clinical Artificial Intelligence (SPKI), University Hospital of North Norway, Tromso, Norway
| | - Karl Øyvind Mikalsen
- Department of Clinical Medicine, UiT The Arctic University of Norway, Tromso, Norway
- Centre for Clinical Artificial Intelligence (SPKI), University Hospital of North Norway, Tromso, Norway
- UiT Machine Learning Group, Department of Physics and Technology, UiT the Arctic University of Norway, Tromso, Norway
| | - Eirik Kjus Aahlin
- Department of Gastroenterological Surgery at University Hospital of North Norway (UNN), Tromso, Norway
| | - Rolv-Ole Lindsetmo
- Department of Clinical Medicine, UiT The Arctic University of Norway, Tromso, Norway
- Head Clinic of Surgery, Oncology and Women Health, University Hospital of North Norway, Tromso, Norway
| | - Kim Erlend Mortensen
- Department of Gastroenterological Surgery at University Hospital of North Norway (UNN), Tromso, Norway
- Department of Clinical Medicine, UiT The Arctic University of Norway, Tromso, Norway
| |
Collapse
|
9
|
Torgersen J, Akers S, Huo Y, Terry JG, Carr JJ, Ruutiainen AT, Skanderson M, Levin W, Lim JK, Taddei TH, So-Armah K, Bhattacharya D, Rentsch CT, Shen L, Carr R, Shinohara RT, McClain M, Freiberg M, Justice AC, Re VL. Performance of an automated deep learning algorithm to identify hepatic steatosis within noncontrast computed tomography scans among people with and without HIV. Pharmacoepidemiol Drug Saf 2023; 32:1121-1130. [PMID: 37276449 PMCID: PMC10527049 DOI: 10.1002/pds.5648] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/10/2022] [Revised: 05/06/2023] [Accepted: 05/31/2023] [Indexed: 06/07/2023]
Abstract
PURPOSE Hepatic steatosis (fatty liver disease) affects 25% of the world's population, particularly people with HIV (PWH). Pharmacoepidemiologic studies to identify medications associated with steatosis have not been conducted because methods to evaluate liver fat within digitized images have not been developed. We determined the accuracy of a deep learning algorithm (automatic liver attenuation region-of-interest-based measurement [ALARM]) to identify steatosis within clinically obtained noncontrast abdominal CT images compared to manual radiologist review and evaluated its performance by HIV status. METHODS We performed a cross-sectional study to evaluate the performance of ALARM within noncontrast abdominal CT images from a sample of patients with and without HIV in the US Veterans Health Administration. We evaluated the ability of ALARM to identify moderate-to-severe hepatic steatosis, defined by mean absolute liver attenuation <40 Hounsfield units (HU), compared to manual radiologist assessment. RESULTS Among 120 patients (51 PWH) who underwent noncontrast abdominal CT, moderate-to-severe hepatic steatosis was identified in 15 (12.5%) persons via ALARM and 12 (10%) by radiologist assessment. Percent agreement between ALARM and radiologist assessment of absolute liver attenuation <40 HU was 95.8%. Sensitivity, specificity, positive predictive value, and negative predictive value of ALARM were 91.7% (95%CI, 51.5%-99.8%), 96.3% (95%CI, 90.8%-99.0%), 73.3% (95%CI, 44.9%-92.2%), and 99.0% (95%CI, 94.8%-100%), respectively. No differences in performance were observed by HIV status. CONCLUSIONS ALARM demonstrated excellent accuracy for moderate-to-severe hepatic steatosis regardless of HIV status. Application of ALARM to radiographic repositories could facilitate real-world studies to evaluate medications associated with steatosis and assess differences by HIV status.
Collapse
Affiliation(s)
- Jessie Torgersen
- Department of Medicine, Penn Center for AIDS Research, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
- Department of Biostatistics, Epidemiology, and Informatics, Center for Clinical Epidemiology and Biostatistics, Center for Real World Effectiveness and Safety of Therapeutics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
- Corporal Michael J. Crescenz VA Medical Center, Philadelphia, PA, USA
| | - Scott Akers
- Corporal Michael J. Crescenz VA Medical Center, Philadelphia, PA, USA
| | - Yuankai Huo
- Department of Computer Science, Vanderbilt University, Nashville, TN, USA
| | - James G. Terry
- Department of Radiology and Radiological Sciences, Vanderbilt University School of Medicine, Nashville, TN, USA
| | - J. Jeffrey Carr
- Department of Radiology and Radiological Sciences, Vanderbilt University School of Medicine, Nashville, TN, USA
| | | | - Melissa Skanderson
- Department of Medicine, Yale School of Medicine, New Haven, CT, USA
- VA Connecticut Healthcare System, West Haven, CT, USA
| | - Woody Levin
- Department of Medicine, Yale School of Medicine, New Haven, CT, USA
- VA Connecticut Healthcare System, West Haven, CT, USA
| | - Joseph K. Lim
- Department of Medicine, Yale School of Medicine, New Haven, CT, USA
- VA Connecticut Healthcare System, West Haven, CT, USA
| | - Tamar H. Taddei
- Department of Medicine, Yale School of Medicine, New Haven, CT, USA
- VA Connecticut Healthcare System, West Haven, CT, USA
| | - Kaku So-Armah
- Department of Medicine, Boston University School of Medicine, Boston, MA, USA
| | - Debika Bhattacharya
- VA Greater Los Angeles Healthcare System and David Geffen School of Medicine at UCLA, Los Angeles, CA, USA
| | - Christopher T. Rentsch
- Department of Medicine, Yale School of Medicine, New Haven, CT, USA
- VA Connecticut Healthcare System, West Haven, CT, USA
- Faculty of Epidemiology and Population Health, London School of Hygiene & Tropical Medicine, London, UK
| | - Li Shen
- Department of Biostatistics, Epidemiology, and Informatics, Center for Clinical Epidemiology and Biostatistics, Center for Real World Effectiveness and Safety of Therapeutics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
| | - Rotonya Carr
- Department of Medicine, Division of Gastroenterology, University of Washington, Seattle, WA, USA
| | - Russell T. Shinohara
- Department of Biostatistics, Epidemiology, and Informatics, Center for Clinical Epidemiology and Biostatistics, Center for Real World Effectiveness and Safety of Therapeutics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
- Center for Biomedical Image Computing and Analysis (CBICA), Department of Radiology, University of Pennsylvania, Philadelphia, PA, 19104
- Penn Statistics in Imaging and Visualization Endeavor (PennSIVE), Center for Clinical Epidemiology and Biostatistics, University of Pennsylvania, Philadelphia, PA, 19104
| | | | - Matthew Freiberg
- Department of Medicine, Vanderbilt University School of Medicine, Nashville, TN, USA
| | - Amy C. Justice
- Department of Medicine, Yale School of Medicine, New Haven, CT, USA
- VA Connecticut Healthcare System, West Haven, CT, USA
- Division of Health Policy and Management, Yale School of Public Health, New Haven, CT, USA
| | - Vincent Lo Re
- Department of Medicine, Penn Center for AIDS Research, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
- Department of Biostatistics, Epidemiology, and Informatics, Center for Clinical Epidemiology and Biostatistics, Center for Real World Effectiveness and Safety of Therapeutics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
| |
Collapse
|
10
|
Li YZ, Wang Y, Huang YH, Xiang P, Liu WX, Lai QQ, Gao YY, Xu MS, Guo YF. RSU-Net: U-net based on residual and self-attention mechanism in the segmentation of cardiac magnetic resonance images. COMPUTER METHODS AND PROGRAMS IN BIOMEDICINE 2023; 231:107437. [PMID: 36863157 DOI: 10.1016/j.cmpb.2023.107437] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 05/30/2022] [Revised: 11/20/2022] [Accepted: 02/18/2023] [Indexed: 06/18/2023]
Abstract
BACKGROUND Automated segmentation techniques for cardiac magnetic resonance imaging (MRI) are beneficial for evaluating cardiac functional parameters in clinical diagnosis. However, due to the characteristics of unclear image boundaries and anisotropic resolution anisotropy produced by cardiac magnetic resonance imaging technology, most of the existing methods still have the problems of intra-class uncertainty and inter-class uncertainty. However, due to the irregularity of the anatomical shape of the heart and the inhomogeneity of tissue density, the boundaries of its anatomical structures become uncertain and discontinuous. Therefore, fast and accurate segmentation of cardiac tissue remains a challenging problem in medical image processing. METHODOLOGY We collected cardiac MRI data from 195 patients as training set and 35patients from different medical centers as external validation set. Our research proposed a U-net network architecture with residual connections and a self-attentive mechanism (Residual Self-Attention U-net, RSU-Net). The network relies on the classic U-net network, adopts the U-shaped symmetric architecture of the encoding and decoding mode, improves the convolution module in the network, introduces skip connections, and improves the network's capacity for feature extraction. Then for solving locality defects of ordinary convolutional networks. To achieve a global receptive field, a self-attention mechanism is introduced at the bottom of the model. The loss function employs a combination of Cross Entropy Loss and Dice Loss to jointly guide network training, resulting in more stable network training. RESULTS In our study, we employ the Hausdorff distance (HD) and the Dice similarity coefficient (DSC) as metrics for assessing segmentation outcomes. Comparsion was made with the segmentation frameworks of other papers, and the comparison results prove that our RSU-Net network performs better and can make accurate segmentation of the heart. New ideas for scientific research. CONCLUSION Our proposed RSU-Net network combines the advantages of residual connections and self-attention. This paper uses the residual links to facilitate the training of the network. In this paper, a self-attention mechanism is introduced, and a bottom self-attention block (BSA Block) is used to aggregate global information. Self-attention aggregates global information, and has achieved good segmentation results on the cardiac segmentation dataset. It facilitates the diagnosis of cardiovascular patients in the future.
Collapse
Affiliation(s)
- Yuan-Zhe Li
- Department of CT/MRI, The Second Affiliated Hospital of Fujian Medical University, Quanzhou 362000, China
| | - Yi Wang
- Department of CT/MRI, The Second Affiliated Hospital of Fujian Medical University, Quanzhou 362000, China
| | - Yin-Hui Huang
- Department of Neurology, Jinjiang Municipal Hospital, Quanzhou 362000, China
| | - Ping Xiang
- Department of Radiology, The First Affiliated Hospital of Zhejiang Chinese Medical University (Zhejiang Provincial Hospital of Traditional Chinese Medicine), Hangzhou 310000, China
| | - Wen-Xi Liu
- Department of CT/MRI, The Second Affiliated Hospital of Fujian Medical University, Quanzhou 362000, China
| | - Qing-Quan Lai
- Department of CT/MRI, The Second Affiliated Hospital of Fujian Medical University, Quanzhou 362000, China
| | - Yi-Yuan Gao
- Department of Radiology, The First Affiliated Hospital of Zhejiang Chinese Medical University (Zhejiang Provincial Hospital of Traditional Chinese Medicine), Hangzhou 310000, China
| | - Mao-Sheng Xu
- Department of Radiology, The First Affiliated Hospital of Zhejiang Chinese Medical University (Zhejiang Provincial Hospital of Traditional Chinese Medicine), Hangzhou 310000, China.
| | - Yi-Fan Guo
- Department of Radiology, The First Affiliated Hospital of Zhejiang Chinese Medical University (Zhejiang Provincial Hospital of Traditional Chinese Medicine), Hangzhou 310000, China.
| |
Collapse
|
11
|
Prinz S, Murray JM, Strack C, Nattenmüller J, Pomykala KL, Schlemmer HP, Badde S, Kleesiek J. Novel measures for the diagnosis of hepatic steatosis using contrast-enhanced computer tomography images. Eur J Radiol 2023; 160:110708. [PMID: 36724687 DOI: 10.1016/j.ejrad.2023.110708] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/17/2022] [Revised: 12/23/2022] [Accepted: 01/17/2023] [Indexed: 01/22/2023]
Abstract
PURPOSE Hepatic steatosis is often diagnosed non-invasively. Various measures and accompanying diagnostic thresholds based on contrast-enhanced CT and virtual non-contrast images have been proposed. We compare these established criteria to novel and fully automated measures. METHOD CT data sets of 197 patients were analyzed. Regions of interest (ROIs) were manually drawn for the liver, spleen, portal vein, and aorta to calculate four established measures of liver-fat. Two novel measures capturing the deviation between the empirical distributions of HU measurements across all voxels within the liver and spleen were calculated. These measures were calculated with both manual ROIs and using fully automated organ segmentations. Agreement between the different measures was evaluated using correlational analysis, as well as their ability to discriminate between fatty and healthy liver. RESULTS Established and novel measures of fatty liver were at a high level of agreement. Novel methods were statistically indistinguishable from the established ones when taking established diagnostic thresholds or physicians' diagnoses as ground truth and this high performance level persisted for automatically selected ROIs. CONCLUSION Automatically generated organ segmentations led to comparable results as manual ROIs, suggesting that the implementation of automated methods can prove to be a valuable tool for incidental diagnosis. Differences in the distribution of HU measurements across voxels between liver and spleen can serve as surrogate markers for the liver-fat-content. Novel measures do not exhibit a measurable disadvantage over established methods based on simpler measures such as across-voxel averages in a population with low incidence of fatty liver.
Collapse
Affiliation(s)
- Sebastian Prinz
- Division of Radiology, German Cancer Research Center (DKFZ), 69120 Heidelberg, Germany; Medical Faculty Heidelberg, Heidelberg University, 69120 Heidelberg, Germany.
| | - Jacob M Murray
- Division of Radiology, German Cancer Research Center (DKFZ), 69120 Heidelberg, Germany; Medical Faculty Heidelberg, Heidelberg University, 69120 Heidelberg, Germany; Institute for AI in Medicine (IKIM), University Medicine Essen, 45131 Essen, Germany
| | - Christian Strack
- Division of Radiology, German Cancer Research Center (DKFZ), 69120 Heidelberg, Germany; Medical Faculty Heidelberg, Heidelberg University, 69120 Heidelberg, Germany
| | - Johanna Nattenmüller
- Department of Diagnostic and Interventional Radiology, Heidelberg University Hospital, 69120 Heidelberg, Germany; Department of Diagnostic and Interventional Radiology, Medical Center-University of Freiburg, Faculty of Medicine, University of Freiburg, 79106 Freiburg, Germany
| | - Kelsey L Pomykala
- Institute for AI in Medicine (IKIM), University Medicine Essen, 45131 Essen, Germany
| | - Heinz-Peter Schlemmer
- Division of Radiology, German Cancer Research Center (DKFZ), 69120 Heidelberg, Germany
| | - Stephanie Badde
- Department of Psychology, Tufts University, 02511 Medford, MA, USA
| | - Jens Kleesiek
- Institute for AI in Medicine (IKIM), University Medicine Essen, 45131 Essen, Germany; German Cancer Consortium (DKTK), Partner Sites Heidelberg and Essen, 69120 Heidelberg, Germany; Cancer Research Center Cologne Essen, West German Cancer Center Essen, 45122 Essen, Germany
| |
Collapse
|
12
|
Kanakaraj P, Ramadass K, Bao S, Basford M, Jones LM, Lee HH, Xu K, Schilling KG, Carr JJ, Terry JG, Huo Y, Sandler KL, Netwon AT, Landman BA. Workflow Integration of Research AI Tools into a Hospital Radiology Rapid Prototyping Environment. J Digit Imaging 2022; 35:1023-1033. [PMID: 35266088 PMCID: PMC9485498 DOI: 10.1007/s10278-022-00601-2] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/20/2021] [Revised: 01/14/2022] [Accepted: 01/23/2022] [Indexed: 11/25/2022] Open
Abstract
The field of artificial intelligence (AI) in medical imaging is undergoing explosive growth, and Radiology is a prime target for innovation. The American College of Radiology Data Science Institute has identified more than 240 specific use cases where AI could be used to improve clinical practice. In this context, thousands of potential methods are developed by research labs and industry innovators. Deploying AI tools within a clinical enterprise, even on limited retrospective evaluation, is complicated by security and privacy concerns. Thus, innovation must be weighed against the substantive resources required for local clinical evaluation. To reduce barriers to AI validation while maintaining rigorous security and privacy standards, we developed the AI Imaging Incubator. The AI Imaging Incubator serves as a DICOM storage destination within a clinical enterprise where images can be directed for novel research evaluation under Institutional Review Board approval. AI Imaging Incubator is controlled by a secure HIPAA-compliant front end and provides access to a menu of AI procedures captured within network-isolated containers. Results are served via a secure website that supports research and clinical data formats. Deployment of new AI approaches within this system is streamlined through a standardized application programming interface. This manuscript presents case studies of the AI Imaging Incubator applied to randomizing lung biopsies on chest CT, liver fat assessment on abdomen CT, and brain volumetry on head MRI.
Collapse
Affiliation(s)
| | | | - Shunxing Bao
- Computer Science, Vanderbilt University, Nashville, TN USA
| | - Melissa Basford
- Vanderbilt Institute for Clinical and Translational Research, Vanderbilt University Medical Center, Nashville, TN USA
| | - Laura M. Jones
- Vanderbilt Institute for Clinical and Translational Research, Vanderbilt University Medical Center, Nashville, TN USA
| | - Ho Hin Lee
- Computer Science, Vanderbilt University, Nashville, TN USA
| | - Kaiwen Xu
- Computer Science, Vanderbilt University, Nashville, TN USA
| | - Kurt G. Schilling
- Vanderbilt University Institute of Imaging Science, Vanderbilt University Medical Center, Nashville, TN USA ,Department of Radiology and Radiological Sciences, Vanderbilt University Medical Center, Nashville, TN USA
| | - John Jeffrey Carr
- Department of Radiology and Radiological Sciences, Vanderbilt University Medical Center, Nashville, TN USA
| | - James Gregory Terry
- Department of Radiology and Radiological Sciences, Vanderbilt University Medical Center, Nashville, TN USA
| | - Yuankai Huo
- Computer Science, Vanderbilt University, Nashville, TN USA ,Data Science Institute, Vanderbilt University, Nashville, TN USA
| | - Kim Lori Sandler
- Department of Radiology and Radiological Sciences, Vanderbilt University Medical Center, Nashville, TN USA
| | - Allen T. Netwon
- Department of Radiology and Radiological Sciences, Vanderbilt University Medical Center, Nashville, TN USA
| | - Bennett A. Landman
- Computer Science, Vanderbilt University, Nashville, TN USA ,Vanderbilt University Institute of Imaging Science, Vanderbilt University Medical Center, Nashville, TN USA ,Department of Radiology and Radiological Sciences, Vanderbilt University Medical Center, Nashville, TN USA ,Electrical Engineering, Vanderbilt University, Nashville, TN USA ,Biomedical Engineering, Vanderbilt University, Nashville, TN USA ,Data Science Institute, Vanderbilt University, Nashville, TN USA
| |
Collapse
|
13
|
Li Y, Wang X, Zhang J, Zhang S, Jiao J. Applications of artificial intelligence (AI) in researches on non-alcoholic fatty liver disease(NAFLD) : A systematic review. Rev Endocr Metab Disord 2022; 23:387-400. [PMID: 34396467 DOI: 10.1007/s11154-021-09681-x] [Citation(s) in RCA: 9] [Impact Index Per Article: 4.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Accepted: 08/07/2021] [Indexed: 10/20/2022]
Abstract
Non-alcoholic fatty liver disease (NAFLD) is one of the most important causes of chronic liver disease in the world, it has been found that cardiovascular and renal risks and diseases are also highly prevalent in adults with NAFLD. Diagnosis and treatment of NAFLD face many challenges, although the medical science has been very developed. Efficiency, accuracy and individualization are the main goals to be solved. Evaluation of the severity of NAFLD involves a variety of clinical parameters, how to optimize non-invasive evaluation methods is a necessary issue that needs to be discussed in this field. Artificial intelligence (AI) has become increasingly widespread in healthcare applications, and it has been also brought many new insights into better analyzing chronic liver disease, including NAFLD. This paper reviewed AI related researches in NAFLD field published recently, summarized diagnostic models based on electronic health record and lab test, ultrasound and radio imaging, and liver histopathological data, described the application of therapeutic models in personalized lifestyle guidance and the development of drugs for NAFLD. In addition, we also analyzed present AI models in distinguishing healthy VS NAFLD/NASH, and fibrosis VS non-fibrosis in the evaluation of NAFLD progression. We hope to provide alternative directions for the future research.
Collapse
Affiliation(s)
- Yifang Li
- Department of Gastroenterolgy & Hepatology, China-Japan Union Hospital, Jilin University, Changchun, 130033, China
| | - Xuetao Wang
- Department of Gastroenterolgy & Hepatology, China-Japan Union Hospital, Jilin University, Changchun, 130033, China
| | - Jun Zhang
- Department of Gastroenterolgy & Hepatology, China-Japan Union Hospital, Jilin University, Changchun, 130033, China
| | - Shanshan Zhang
- Department of Gastroenterolgy & Hepatology, China-Japan Union Hospital, Jilin University, Changchun, 130033, China
| | - Jian Jiao
- Department of Gastroenterolgy & Hepatology, China-Japan Union Hospital, Jilin University, Changchun, 130033, China.
| |
Collapse
|
14
|
Bjornsson PA, Baker A, Fleps I, Pauchard Y, Palsson H, Ferguson SJ, Sigurdsson S, Gudnason V, Helgason B, Ellingsen LM. Fast and robust femur segmentation from computed tomography images for patient-specific hip fracture risk screening. COMPUTER METHODS IN BIOMECHANICS AND BIOMEDICAL ENGINEERING: IMAGING & VISUALIZATION 2022. [DOI: 10.1080/21681163.2022.2068160] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 10/18/2022]
Affiliation(s)
- Pall Asgeir Bjornsson
- The Department of Electrical and Computer Engineering, The University of Iceland, Reykjavik, Iceland
| | - Alexander Baker
- The Institute for Biomechanics, ETH Zurich, Zurich, Switzerland
| | - Ingmar Fleps
- The Institute for Biomechanics, ETH Zurich, Zurich, Switzerland
| | - Yves Pauchard
- McCaig Institute for Bone and Joint Health, The University of Calgary, Calgary, AB Canada
| | - Halldor Palsson
- The Department of Industrial Engineering, Mechanical Engineering, and Computer Science, The University of Iceland, Reykjavik, Iceland
| | | | | | - Vilmundur Gudnason
- The Icelandic Heart Association, Kopavogur, Iceland
- The Department of Medicine, The University of Iceland, Reykjavik, Iceland
| | | | - Lotta Maria Ellingsen
- The Department of Electrical and Computer Engineering, The University of Iceland, Reykjavik, Iceland
- The Department of Electrical and Computer Engineering, The Johns Hopkins University, Baltimore, MD, USA
| |
Collapse
|
15
|
Gunasekharan A, Jiang J, Nickerson A, Jalil S, Mumtaz K. Application of artificial intelligence in non-alcoholic fatty liver disease and viral hepatitis. Artif Intell Gastroenterol 2022; 3:46-53. [DOI: 10.35712/aig.v3.i2.46] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 12/31/2021] [Revised: 02/18/2022] [Accepted: 04/28/2022] [Indexed: 02/06/2023] Open
Abstract
Non-alcoholic fatty liver disease (NAFLD) and chronic viral hepatitis are among the most significant causes of liver-related mortality worldwide. It is critical to develop reliable methods of predicting progression to fibrosis, cirrhosis, and decompensated liver disease. Current screening methods such as biopsy and transient elastography are limited by invasiveness and observer variation in analysis of data. Artificial intelligence (AI) provides a unique opportunity to more accurately diagnose NAFLD and viral hepatitis, and to identify patients at high risk for disease progression. We conducted a literature review of existing evidence for AI in NAFLD and viral hepatitis. Thirteen articles on AI in NAFLD and 14 on viral hepatitis were included in our analysis. We found that machine learning algorithms were comparable in accuracy to current methods for diagnosis and fibrosis prediction (MELD-Na score, liver biopsy, FIB-4 score, and biomarkers). They also reliably predicted hepatitis C treatment failure and hepatic encephalopathy, for which there are currently no established prediction tools. These studies show that AI could be a helpful adjunct to existing techniques for diagnosing, monitoring, and treating both NAFLD and viral hepatitis.
Collapse
Affiliation(s)
| | - Joanna Jiang
- Department of Medicine, Ohio State University, Columbus, OH 43210, United States
| | - Ashley Nickerson
- Department of Medicine, Ohio State University, Columbus, OH 43210, United States
| | - Sajid Jalil
- Department of Medicine, Ohio State University, Columbus, OH 43210, United States
| | - Khalid Mumtaz
- Department of Medicine, Ohio State University, Columbus, OH 43210, United States
| |
Collapse
|
16
|
Balsano C, Alisi A, Brunetto MR, Invernizzi P, Burra P, Piscaglia F. The application of artificial intelligence in hepatology: A systematic review. Dig Liver Dis 2022; 54:299-308. [PMID: 34266794 DOI: 10.1016/j.dld.2021.06.011] [Citation(s) in RCA: 6] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 03/02/2021] [Revised: 06/04/2021] [Accepted: 06/07/2021] [Indexed: 02/06/2023]
Abstract
The integration of human and artificial intelligence (AI) in medicine has only recently begun but it has already become obvious that intelligent systems can dramatically improve the management of liver diseases. Big data made it possible to envisage transformative developments of the use of AI for diagnosing, predicting prognosis and treating liver diseases, but there is still a lot of work to do. If we want to achieve the 21st century digital revolution, there is an urgent need for specific national and international rules, and to adhere to bioethical parameters when collecting data. Avoiding misleading results is essential for the effective use of AI. A crucial question is whether it is possible to sustain, technically and morally, the process of integration between man and machine. We present a systematic review on the applications of AI to hepatology, highlighting the current challenges and crucial issues related to the use of such technologies.
Collapse
Affiliation(s)
- Clara Balsano
- Dept. of Life, Health and Environmental Sciences MESVA, University of L'Aquila, Piazza S. Salvatore Tommasi 1, 67100, Coppito, L'Aquila. Italy; Francesco Balsano Foundation, Via Giovanni Battista Martini 6, 00198, Rome, Italy.
| | - Anna Alisi
- Research Unit of Molecular Genetics of Complex Phenotypes, Bambino Gesù Children's Hospital, IRCCS, Rome, Italy
| | - Maurizia R Brunetto
- Hepatology Unit and Laboratory of Molecular Genetics and Pathology of Hepatitis Viruses, University Hospital of Pisa, Pisa, Italy
| | - Pietro Invernizzi
- Division of Gastroenterology and Center of Autoimmune Liver Diseases, Department of Medicine and Surgery, San Gerardo Hospital, University of Milano, Bicocca, Italy
| | - Patrizia Burra
- Multivisceral Transplant Unit, Department of Surgery, Oncology, Gastroenterology, Padua University Hospital, Padua, Italy
| | - Fabio Piscaglia
- Division of Internal Medicine, IRCCS Azienda Ospedaliero Universitaria di Bologna, Bologna, Italy
| | | |
Collapse
|
17
|
Park HJ, Yoon JS, Lee SS, Suk HI, Park B, Sung YS, Hong SB, Ryu H. Deep Learning-Based Assessment of Functional Liver Capacity Using Gadoxetic Acid-Enhanced Hepatobiliary Phase MRI. Korean J Radiol 2022; 23:720-731. [PMID: 35434977 PMCID: PMC9240292 DOI: 10.3348/kjr.2021.0892] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/14/2021] [Revised: 01/12/2022] [Accepted: 01/13/2022] [Indexed: 11/15/2022] Open
Affiliation(s)
- Hyo Jung Park
- Department of Radiology and Research Institute of Radiology, University of Ulsan College of Medicine, Asan Medical Center, Seoul, Korea
| | - Jee Seok Yoon
- Department of Brain and Cognitive Engineering, Korea University, Seoul, Korea
| | - Seung Soo Lee
- Department of Radiology and Research Institute of Radiology, University of Ulsan College of Medicine, Asan Medical Center, Seoul, Korea
| | - Heung-Il Suk
- Department of Brain and Cognitive Engineering, Korea University, Seoul, Korea
- Department of Artificial Intelligence, Korea University, Seoul, Korea
| | - Bumwoo Park
- Department of Radiology and Research Institute of Radiology, University of Ulsan College of Medicine, Asan Medical Center, Seoul, Korea
| | - Yu Sub Sung
- Department of Radiology and Research Institute of Radiology, University of Ulsan College of Medicine, Asan Medical Center, Seoul, Korea
| | - Seung Baek Hong
- Department of Radiology, Pusan National University Hospital, Busan, Korea
| | - Hwaseong Ryu
- Department of Radiology, Pusan National University Yangsan Hospital, Yangsan, Korea
| |
Collapse
|
18
|
Starekova J, Hernando D, Pickhardt PJ, Reeder SB. Quantification of Liver Fat Content with CT and MRI: State of the Art. Radiology 2021; 301:250-262. [PMID: 34546125 PMCID: PMC8574059 DOI: 10.1148/radiol.2021204288] [Citation(s) in RCA: 84] [Impact Index Per Article: 28.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/13/2022]
Abstract
Hepatic steatosis is defined as pathologically elevated liver fat content and has many underlying causes. Nonalcoholic fatty liver disease (NAFLD) is the most common chronic liver disease worldwide, with an increasing prevalence among adults and children. Abnormal liver fat accumulation has serious consequences, including cirrhosis, liver failure, and hepatocellular carcinoma. In addition, hepatic steatosis is increasingly recognized as an independent risk factor for the metabolic syndrome, type 2 diabetes, and, most important, cardiovascular mortality. During the past 2 decades, noninvasive imaging-based methods for the evaluation of hepatic steatosis have been developed and disseminated. Chemical shift-encoded MRI is now established as the most accurate and precise method for liver fat quantification. CT is important for the detection and quantification of incidental steatosis and may play an increasingly prominent role in risk stratification, particularly with the emergence of CT-based screening and artificial intelligence. Quantitative imaging methods are increasingly used for diagnostic work-up and management of steatosis, including treatment monitoring. The purpose of this state-of-the-art review is to provide an overview of recent progress and current state of the art for liver fat quantification using CT and MRI, as well as important practical considerations related to clinical implementation.
Collapse
Affiliation(s)
- Jitka Starekova
- From the Departments of Radiology (J.S., D.H., P.J.P., S.B.R.), Medical Physics (D.H., S.B.R.), Biomedical Engineering (S.B.R.), Medicine (S.B.R.), and Emergency Medicine (S.B.R.), University of Wisconsin, 1111 Highland Ave, Madison, WI 53705
| | - Diego Hernando
- From the Departments of Radiology (J.S., D.H., P.J.P., S.B.R.), Medical Physics (D.H., S.B.R.), Biomedical Engineering (S.B.R.), Medicine (S.B.R.), and Emergency Medicine (S.B.R.), University of Wisconsin, 1111 Highland Ave, Madison, WI 53705
| | - Perry J Pickhardt
- From the Departments of Radiology (J.S., D.H., P.J.P., S.B.R.), Medical Physics (D.H., S.B.R.), Biomedical Engineering (S.B.R.), Medicine (S.B.R.), and Emergency Medicine (S.B.R.), University of Wisconsin, 1111 Highland Ave, Madison, WI 53705
| | - Scott B Reeder
- From the Departments of Radiology (J.S., D.H., P.J.P., S.B.R.), Medical Physics (D.H., S.B.R.), Biomedical Engineering (S.B.R.), Medicine (S.B.R.), and Emergency Medicine (S.B.R.), University of Wisconsin, 1111 Highland Ave, Madison, WI 53705
| |
Collapse
|
19
|
Li M, Lian F, Wang C, Guo S. Dual adversarial convolutional networks with multilevel cues for pancreatic segmentation. Phys Med Biol 2021; 66. [PMID: 34271564 DOI: 10.1088/1361-6560/ac155f] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/23/2021] [Accepted: 07/16/2021] [Indexed: 12/11/2022]
Abstract
Accurate organ segmentation is a relatively challenging subject in medical imaging, especially for the pancreas, whose morphological characteristics are subtle but variable. In this paper, a novel dual adversarial convolutional network with multilevel cues (DACN-MC) is proposed to segment the pancreas in computerized tomography (CT). DACN-MC first involves a duplex adversarial network using a conventional model for biomedical image segmentation, which ensures the veracity of the predicted probability volumes and ultimately enhances the quality of the obtained maps. Specifically, one of the adversarial networks helps the predicted maps to resemble the ground truths by importing extra guidance into the original loss functions. The other adversarial network further judges whether the obtained maps are well segmented and improves the image quality once again. Then, a multilevel cue collection module (MCCM) is introduced to gather many useful details for pancreas segmentation. In other words, we collect several sets of material formed by features from different layers and pick out a group with optimal performance for use in the ultimate algorithm. The experimental results show that dual adversarial convolutional networks together with multilevel cue collection help our proposed algorithm to achieve competitive segmentation performance, based on the results of several evaluation indexes.
Collapse
Affiliation(s)
- Meiyu Li
- College of Electronic Science and Engineering, Jilin University, Changchun 130012, People's Republic of China
| | - Fenghui Lian
- School of Aviation Operations and Services, Air Force Aviation University, Changchun 130000, People's Republic of China
| | - Chunyu Wang
- School of Aviation Operations and Services, Air Force Aviation University, Changchun 130000, People's Republic of China
| | - Shuxu Guo
- College of Electronic Science and Engineering, Jilin University, Changchun 130012, People's Republic of China
| |
Collapse
|
20
|
Kim DW, Ha J, Lee SS, Kwon JH, Kim NY, Sung YS, Yoon JS, Suk HI, Lee Y, Kang BK. Population-based and Personalized Reference Intervals for Liver and Spleen Volumes in Healthy Individuals and Those with Viral Hepatitis. Radiology 2021; 301:339-347. [PMID: 34402668 DOI: 10.1148/radiol.2021204183] [Citation(s) in RCA: 10] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/14/2022]
Abstract
Background Reference intervals guiding volumetric assessment of the liver and spleen have yet to be established. Purpose To establish population-based and personalized reference intervals for liver volume, spleen volume, and liver-to-spleen volume ratio (LSVR). Materials and Methods This retrospective study consecutively included healthy adult liver donors from 2001 to 2013 (reference group) and from 2014 to 2016 (healthy validation group) and patients with viral hepatitis from 2007 to 2017. Liver volume, spleen volume, and LSVR were measured with CT by using a deep learning algorithm. In the reference group, the reference intervals for the volume indexes were determined by using the population-based (ranges encompassing the central 95% of donors) and personalized (quantile regression modeling of the 2.5th and 97.5th percentiles as a function of age, sex, height, and weight) approaches. The validity of the reference intervals was evaluated in the healthy validation group and the viral hepatitis group. Results The reference and healthy validation groups had 2989 donors (mean age ± standard deviation, 30 years ± 9; 1828 men) and 472 donors (mean age, 30 years ± 9; 334 men), respectively. The viral hepatitis group had 158 patients (mean age, 48 years ± 12; 95 men). The population-based reference intervals were 824.5-1700.0 cm3 for liver volume, 81.1-322.0 cm3 for spleen volume, and 3.96-13.78 for LSVR. Formulae and a web calculator (https://i-pacs.com/calculators) were presented to calculate the personalized reference intervals. In the healthy validation group, both the population-based and personalized reference intervals were used to classify the volume indexes of 94%-96% of the donors as falling within the reference interval. In the viral hepatitis group, when compared with the population-based reference intervals, the personalized reference intervals helped identify more patients with volume indexes outside the reference interval (liver volume, 21.5% [34 of 158] vs 13.3% [21 of 158], P = .01; spleen volume, 29.1% [46 of 158] vs 22.2% [35 of 158], P = .01; LSVR, 35.4% [56 of 158] vs 26.6% [42 of 158], P < .001). Conclusion Reference intervals derived from a deep learning approach in healthy adults may enable evidence-based assessments of liver and spleen volume in clinical practice. © RSNA, 2021 Online supplemental material is available for this article. See also the editorial by Ringl in this issue.
Collapse
Affiliation(s)
- Dong Wook Kim
- From the Department of Radiology and Research Institute of Radiology (D.W.K., J.H., S.S.L., J.H.K., Y.S.S.) and Department of Clinical Epidemiology and Biostatistics (N.Y.K.), University of Ulsan College of Medicine, Asan Medical Center, 88 Olympic-ro 43-gil, Songpa-gu, Seoul 05505, Republic of Korea; Department of Brain and Cognitive Engineering (J.S.Y., H.I.S.) and Department of Artificial Intelligence (H.I.S.), Korea University, Seoul, Republic of Korea; Department of Radiology, Haeundae Paik Hospital, Inje University College of Medicine, Busan, Republic of Korea (Y.L.); and Department of Radiology, Hanyang University Medical Center, Hanyang University School of Medicine, Seoul, Republic of Korea (B.K.K.)
| | - Jiyeon Ha
- From the Department of Radiology and Research Institute of Radiology (D.W.K., J.H., S.S.L., J.H.K., Y.S.S.) and Department of Clinical Epidemiology and Biostatistics (N.Y.K.), University of Ulsan College of Medicine, Asan Medical Center, 88 Olympic-ro 43-gil, Songpa-gu, Seoul 05505, Republic of Korea; Department of Brain and Cognitive Engineering (J.S.Y., H.I.S.) and Department of Artificial Intelligence (H.I.S.), Korea University, Seoul, Republic of Korea; Department of Radiology, Haeundae Paik Hospital, Inje University College of Medicine, Busan, Republic of Korea (Y.L.); and Department of Radiology, Hanyang University Medical Center, Hanyang University School of Medicine, Seoul, Republic of Korea (B.K.K.)
| | - Seung Soo Lee
- From the Department of Radiology and Research Institute of Radiology (D.W.K., J.H., S.S.L., J.H.K., Y.S.S.) and Department of Clinical Epidemiology and Biostatistics (N.Y.K.), University of Ulsan College of Medicine, Asan Medical Center, 88 Olympic-ro 43-gil, Songpa-gu, Seoul 05505, Republic of Korea; Department of Brain and Cognitive Engineering (J.S.Y., H.I.S.) and Department of Artificial Intelligence (H.I.S.), Korea University, Seoul, Republic of Korea; Department of Radiology, Haeundae Paik Hospital, Inje University College of Medicine, Busan, Republic of Korea (Y.L.); and Department of Radiology, Hanyang University Medical Center, Hanyang University School of Medicine, Seoul, Republic of Korea (B.K.K.)
| | - Ji Hye Kwon
- From the Department of Radiology and Research Institute of Radiology (D.W.K., J.H., S.S.L., J.H.K., Y.S.S.) and Department of Clinical Epidemiology and Biostatistics (N.Y.K.), University of Ulsan College of Medicine, Asan Medical Center, 88 Olympic-ro 43-gil, Songpa-gu, Seoul 05505, Republic of Korea; Department of Brain and Cognitive Engineering (J.S.Y., H.I.S.) and Department of Artificial Intelligence (H.I.S.), Korea University, Seoul, Republic of Korea; Department of Radiology, Haeundae Paik Hospital, Inje University College of Medicine, Busan, Republic of Korea (Y.L.); and Department of Radiology, Hanyang University Medical Center, Hanyang University School of Medicine, Seoul, Republic of Korea (B.K.K.)
| | - Na Young Kim
- From the Department of Radiology and Research Institute of Radiology (D.W.K., J.H., S.S.L., J.H.K., Y.S.S.) and Department of Clinical Epidemiology and Biostatistics (N.Y.K.), University of Ulsan College of Medicine, Asan Medical Center, 88 Olympic-ro 43-gil, Songpa-gu, Seoul 05505, Republic of Korea; Department of Brain and Cognitive Engineering (J.S.Y., H.I.S.) and Department of Artificial Intelligence (H.I.S.), Korea University, Seoul, Republic of Korea; Department of Radiology, Haeundae Paik Hospital, Inje University College of Medicine, Busan, Republic of Korea (Y.L.); and Department of Radiology, Hanyang University Medical Center, Hanyang University School of Medicine, Seoul, Republic of Korea (B.K.K.)
| | - Yu Sub Sung
- From the Department of Radiology and Research Institute of Radiology (D.W.K., J.H., S.S.L., J.H.K., Y.S.S.) and Department of Clinical Epidemiology and Biostatistics (N.Y.K.), University of Ulsan College of Medicine, Asan Medical Center, 88 Olympic-ro 43-gil, Songpa-gu, Seoul 05505, Republic of Korea; Department of Brain and Cognitive Engineering (J.S.Y., H.I.S.) and Department of Artificial Intelligence (H.I.S.), Korea University, Seoul, Republic of Korea; Department of Radiology, Haeundae Paik Hospital, Inje University College of Medicine, Busan, Republic of Korea (Y.L.); and Department of Radiology, Hanyang University Medical Center, Hanyang University School of Medicine, Seoul, Republic of Korea (B.K.K.)
| | - Jee Seok Yoon
- From the Department of Radiology and Research Institute of Radiology (D.W.K., J.H., S.S.L., J.H.K., Y.S.S.) and Department of Clinical Epidemiology and Biostatistics (N.Y.K.), University of Ulsan College of Medicine, Asan Medical Center, 88 Olympic-ro 43-gil, Songpa-gu, Seoul 05505, Republic of Korea; Department of Brain and Cognitive Engineering (J.S.Y., H.I.S.) and Department of Artificial Intelligence (H.I.S.), Korea University, Seoul, Republic of Korea; Department of Radiology, Haeundae Paik Hospital, Inje University College of Medicine, Busan, Republic of Korea (Y.L.); and Department of Radiology, Hanyang University Medical Center, Hanyang University School of Medicine, Seoul, Republic of Korea (B.K.K.)
| | - Heung-Il Suk
- From the Department of Radiology and Research Institute of Radiology (D.W.K., J.H., S.S.L., J.H.K., Y.S.S.) and Department of Clinical Epidemiology and Biostatistics (N.Y.K.), University of Ulsan College of Medicine, Asan Medical Center, 88 Olympic-ro 43-gil, Songpa-gu, Seoul 05505, Republic of Korea; Department of Brain and Cognitive Engineering (J.S.Y., H.I.S.) and Department of Artificial Intelligence (H.I.S.), Korea University, Seoul, Republic of Korea; Department of Radiology, Haeundae Paik Hospital, Inje University College of Medicine, Busan, Republic of Korea (Y.L.); and Department of Radiology, Hanyang University Medical Center, Hanyang University School of Medicine, Seoul, Republic of Korea (B.K.K.)
| | - Yedaun Lee
- From the Department of Radiology and Research Institute of Radiology (D.W.K., J.H., S.S.L., J.H.K., Y.S.S.) and Department of Clinical Epidemiology and Biostatistics (N.Y.K.), University of Ulsan College of Medicine, Asan Medical Center, 88 Olympic-ro 43-gil, Songpa-gu, Seoul 05505, Republic of Korea; Department of Brain and Cognitive Engineering (J.S.Y., H.I.S.) and Department of Artificial Intelligence (H.I.S.), Korea University, Seoul, Republic of Korea; Department of Radiology, Haeundae Paik Hospital, Inje University College of Medicine, Busan, Republic of Korea (Y.L.); and Department of Radiology, Hanyang University Medical Center, Hanyang University School of Medicine, Seoul, Republic of Korea (B.K.K.)
| | - Bo-Kyeong Kang
- From the Department of Radiology and Research Institute of Radiology (D.W.K., J.H., S.S.L., J.H.K., Y.S.S.) and Department of Clinical Epidemiology and Biostatistics (N.Y.K.), University of Ulsan College of Medicine, Asan Medical Center, 88 Olympic-ro 43-gil, Songpa-gu, Seoul 05505, Republic of Korea; Department of Brain and Cognitive Engineering (J.S.Y., H.I.S.) and Department of Artificial Intelligence (H.I.S.), Korea University, Seoul, Republic of Korea; Department of Radiology, Haeundae Paik Hospital, Inje University College of Medicine, Busan, Republic of Korea (Y.L.); and Department of Radiology, Hanyang University Medical Center, Hanyang University School of Medicine, Seoul, Republic of Korea (B.K.K.)
| |
Collapse
|
21
|
Popa SL, Ismaiel A, Cristina P, Cristina M, Chiarioni G, David L, Dumitrascu DL. Non-Alcoholic Fatty Liver Disease: Implementing Complete Automated Diagnosis and Staging. A Systematic Review. Diagnostics (Basel) 2021; 11:diagnostics11061078. [PMID: 34204822 PMCID: PMC8231502 DOI: 10.3390/diagnostics11061078] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/29/2021] [Revised: 06/05/2021] [Accepted: 06/10/2021] [Indexed: 12/12/2022] Open
Abstract
Background: Non-alcoholic fatty liver disease (NAFLD) is a fast-growing pathology around the world, being considered the most common chronic liver disease. It is diagnosed based on the presence of steatosis in more than 5% of hepatocytes without significant alcohol consumption. This review aims to provide a comprehensive overview of current studies of artificial intelligence (AI) applications that may help physicians in implementing a complete automated NAFLD diagnosis and staging. Methods: PubMed, EMBASE, Cochrane Library, and WILEY databases were screened for relevant publications in relation to AI applications in NAFLD. The search terms included: (non-alcoholic fatty liver disease OR NAFLD) AND (artificial intelligence OR machine learning OR neural networks OR deep learning OR automated diagnosis OR computer-aided diagnosis OR digital pathology OR automated ultrasound OR automated computer tomography OR automated magnetic imaging OR electronic health records). Results: Our search identified 37 articles about automated NAFLD diagnosis, out of which 15 articles analyzed imagistic techniques, 15 articles analyzed digital pathology, and 7 articles analyzed electronic health records (EHC). All studies included in this review show an accurate capacity of automated diagnosis and staging in NAFLD using AI-based software. Conclusions: We found significant evidence demonstrating that implementing a complete automated system for NAFLD diagnosis, staging, and risk stratification is currently possible, considering the accuracy, sensibility, and specificity of available AI-based tools.
Collapse
Affiliation(s)
- Stefan L. Popa
- 2nd Medical Department, “Iuliu Hatieganu” University of Medicine and Pharmacy, 400006 Cluj-Napoca, Romania; (S.L.P.); (L.D.); (D.L.D.)
| | - Abdulrahman Ismaiel
- 2nd Medical Department, “Iuliu Hatieganu” University of Medicine and Pharmacy, 400006 Cluj-Napoca, Romania; (S.L.P.); (L.D.); (D.L.D.)
- Correspondence:
| | - Pop Cristina
- Department of Pharmacology, Physiology and Pathophysiology, Faculty of Pharmacy, “Iuliu Hațieganu” University of Medicine and Pharmacy, 400349 Cluj-Napoca, Romania; (P.C.); (M.C.)
| | - Mogosan Cristina
- Department of Pharmacology, Physiology and Pathophysiology, Faculty of Pharmacy, “Iuliu Hațieganu” University of Medicine and Pharmacy, 400349 Cluj-Napoca, Romania; (P.C.); (M.C.)
| | - Giuseppe Chiarioni
- Division of Gastroenterology, University of Verona, 1-37126 AOUI Verona, Italy;
| | - Liliana David
- 2nd Medical Department, “Iuliu Hatieganu” University of Medicine and Pharmacy, 400006 Cluj-Napoca, Romania; (S.L.P.); (L.D.); (D.L.D.)
| | - Dan L. Dumitrascu
- 2nd Medical Department, “Iuliu Hatieganu” University of Medicine and Pharmacy, 400006 Cluj-Napoca, Romania; (S.L.P.); (L.D.); (D.L.D.)
| |
Collapse
|
22
|
Ahn Y, Yoon JS, Lee SS, Suk HI, Son JH, Sung YS, Lee Y, Kang BK, Kim HS. Deep Learning Algorithm for Automated Segmentation and Volume Measurement of the Liver and Spleen Using Portal Venous Phase Computed Tomography Images. Korean J Radiol 2020; 21:987-997. [PMID: 32677383 PMCID: PMC7369202 DOI: 10.3348/kjr.2020.0237] [Citation(s) in RCA: 34] [Impact Index Per Article: 8.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/06/2020] [Revised: 05/06/2020] [Accepted: 05/11/2020] [Indexed: 02/06/2023] Open
Abstract
Objective Measurement of the liver and spleen volumes has clinical implications. Although computed tomography (CT) volumetry is considered to be the most reliable noninvasive method for liver and spleen volume measurement, it has limited application in clinical practice due to its time-consuming segmentation process. We aimed to develop and validate a deep learning algorithm (DLA) for fully automated liver and spleen segmentation using portal venous phase CT images in various liver conditions. Materials and Methods A DLA for liver and spleen segmentation was trained using a development dataset of portal venous CT images from 813 patients. Performance of the DLA was evaluated in two separate test datasets: dataset-1 which included 150 CT examinations in patients with various liver conditions (i.e., healthy liver, fatty liver, chronic liver disease, cirrhosis, and post-hepatectomy) and dataset-2 which included 50 pairs of CT examinations performed at ours and other institutions. The performance of the DLA was evaluated using the dice similarity score (DSS) for segmentation and Bland-Altman 95% limits of agreement (LOA) for measurement of the volumetric indices, which was compared with that of ground truth manual segmentation. Results In test dataset-1, the DLA achieved a mean DSS of 0.973 and 0.974 for liver and spleen segmentation, respectively, with no significant difference in DSS across different liver conditions (p = 0.60 and 0.26 for the liver and spleen, respectively). For the measurement of volumetric indices, the Bland-Altman 95% LOA was −0.17 ± 3.07% for liver volume and −0.56 ± 3.78% for spleen volume. In test dataset-2, DLA performance using CT images obtained at outside institutions and our institution was comparable for liver (DSS, 0.982 vs. 0.983; p = 0.28) and spleen (DSS, 0.969 vs. 0.968; p = 0.41) segmentation. Conclusion The DLA enabled highly accurate segmentation and volume measurement of the liver and spleen using portal venous phase CT images of patients with various liver conditions.
Collapse
Affiliation(s)
- Yura Ahn
- Department of Radiology and Research Institute of Radiology, Asan Medical Center, University of Ulsan College of Medicine, Seoul, Korea
| | - Jee Seok Yoon
- Department of Brain and Cognitive Engineering, Korea University, Seoul, Korea
| | - Seung Soo Lee
- Department of Radiology and Research Institute of Radiology, Asan Medical Center, University of Ulsan College of Medicine, Seoul, Korea.
| | - Heung Il Suk
- Department of Brain and Cognitive Engineering, Korea University, Seoul, Korea.,Department of Artificial Intelligence, Korea University, Seoul, Korea.
| | - Jung Hee Son
- Department of Radiology and Research Institute of Radiology, Asan Medical Center, University of Ulsan College of Medicine, Seoul, Korea
| | - Yu Sub Sung
- Department of Radiology and Research Institute of Radiology, Asan Medical Center, University of Ulsan College of Medicine, Seoul, Korea
| | - Yedaun Lee
- Department of Radiology, Haeundae Paik Hospital, Inje University College of Medicine, Busan, Korea
| | - Bo Kyeong Kang
- Department of Radiology, Hanyang University Medical Center, Hanyang University School of Medicine, Seoul, Korea
| | - Ho Sung Kim
- Department of Radiology and Research Institute of Radiology, Asan Medical Center, University of Ulsan College of Medicine, Seoul, Korea
| |
Collapse
|
23
|
Park HJ, Park B, Lee SS. Radiomics and Deep Learning: Hepatic Applications. Korean J Radiol 2020; 21:387-401. [PMID: 32193887 PMCID: PMC7082656 DOI: 10.3348/kjr.2019.0752] [Citation(s) in RCA: 71] [Impact Index Per Article: 17.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/09/2019] [Accepted: 01/05/2020] [Indexed: 12/12/2022] Open
Abstract
Radiomics and deep learning have recently gained attention in the imaging assessment of various liver diseases. Recent research has demonstrated the potential utility of radiomics and deep learning in staging liver fibroses, detecting portal hypertension, characterizing focal hepatic lesions, prognosticating malignant hepatic tumors, and segmenting the liver and liver tumors. In this review, we outline the basic technical aspects of radiomics and deep learning and summarize recent investigations of the application of these techniques in liver disease.
Collapse
Affiliation(s)
- Hyo Jung Park
- Department of Radiology and Research Institute of Radiology, Asan Medical Center, University of Ulsan College of Medicine, Seoul, Korea
| | - Bumwoo Park
- Health Innovation Big Data Center, Asan Institute for Life Sciences, Asan Medical Center, Seoul, Korea
| | - Seung Soo Lee
- Department of Radiology and Research Institute of Radiology, Asan Medical Center, University of Ulsan College of Medicine, Seoul, Korea.
| |
Collapse
|