A systematic review on the role of artificial intelligence in sonographic diagnosis of thyroid cancer: Past, present and future

• URL: http://arxiv.org/abs/2006.05861v1
• Date: Wed, 10 Jun 2020 14:38:05 GMT
• Title: A systematic review on the role of artificial intelligence in sonographic diagnosis of thyroid cancer: Past, present and future
• Authors: Fatemeh Abdolali, Atefeh Shahroudnejad, Abhilash Rakkunedeth Hareendranathan, Jacob L Jaremko, Michelle Noga, Kumaradevan Punithakumar
• Abstract summary: This review follows a methodology-based classification of the different techniques available for thyroid cancer diagnosis. We reflect on the trends and challenges of the field of sonographic diagnosis of thyroid malignancies. Machine learning will continue to play a fundamental role in the development of future thyroid cancer diagnosis frameworks.
• Score: 0.6523396727243321
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Thyroid cancer is common worldwide, with a rapid increase in prevalence across North America in recent years. While most patients present with palpable nodules through physical examination, a large number of small and medium-sized nodules are detected by ultrasound examination. Suspicious nodules are then sent for biopsy through fine needle aspiration. Since biopsies are invasive and sometimes inconclusive, various research groups have tried to develop computer-aided diagnosis systems. Earlier approaches along these lines relied on clinically relevant features that were manually identified by radiologists. With the recent success of artificial intelligence (AI), various new methods are being developed to identify these features in thyroid ultrasound automatically. In this paper, we present a systematic review of state-of-the-art on AI application in sonographic diagnosis of thyroid cancer. This review follows a methodology-based classification of the different techniques available for thyroid cancer diagnosis. With more than 50 papers included in this review, we reflect on the trends and challenges of the field of sonographic diagnosis of thyroid malignancies and potential of computer-aided diagnosis to increase the impact of ultrasound applications on the future of thyroid cancer diagnosis. Machine learning will continue to play a fundamental role in the development of future thyroid cancer diagnosis frameworks.

Related papers

• An updated overview of radiomics-based artificial intelligence (AI) methods in breast cancer screening and diagnosis [0.0]
  Current imaging methods for diagnosing breast cancer (BC) are associated with limited sensitivity and specificity and modest positive predictive power. Recent progress in image analysis using artificial intelligence (AI) has created great promise to improve BC diagnosis and subtype differentiation. Radiomics have been developed to improve sensitivity and specificity of early BC diagnosis and classification.
  arXiv  Detail & Related papers  (2024-06-20T21:01:11Z)
• A Survey of Artificial Intelligence in Gait-Based Neurodegenerative Disease Diagnosis [51.07114445705692]
  neurodegenerative diseases (NDs) traditionally require extensive healthcare resources and human effort for medical diagnosis and monitoring. As a crucial disease-related motor symptom, human gait can be exploited to characterize different NDs. The current advances in artificial intelligence (AI) models enable automatic gait analysis for NDs identification and classification.
  arXiv  Detail & Related papers  (2024-05-21T06:44:40Z)
• Advancements in Radiomics and Artificial Intelligence for Thyroid Cancer Diagnosis [1.6687900252134487]
  The application of AI and radiomics to thyroid cancer diagnosis is examined in this review. A review of multiple databases was conducted in compliance with PRISMA guidelines until October 2023.
  arXiv  Detail & Related papers  (2024-04-09T11:05:20Z)
• Can GPT-4V(ision) Serve Medical Applications? Case Studies on GPT-4V for Multimodal Medical Diagnosis [59.35504779947686]
  GPT-4V is OpenAI's newest model for multimodal medical diagnosis. Our evaluation encompasses 17 human body systems. GPT-4V demonstrates proficiency in distinguishing between medical image modalities and anatomy. It faces significant challenges in disease diagnosis and generating comprehensive reports.
  arXiv  Detail & Related papers  (2023-10-15T18:32:27Z)
• AI in Thyroid Cancer Diagnosis: Techniques, Trends, and Future Directions [3.2071249735671348]
  This review paper summarizes a large collection of articles related to artificial intelligence (AI)-based techniques used in the diagnosis of thyroid cancer. The focus of this study is on how AI-based tools can support the diagnosis and treatment of thyroid cancer, through supervised, unsupervised, or hybrid techniques.
  arXiv  Detail & Related papers  (2023-08-25T17:27:53Z)
• A Pathologist-Informed Workflow for Classification of Prostate Glands in Histopathology [62.997667081978825]
  Pathologists diagnose and grade prostate cancer by examining tissue from needle biopsies on glass slides. Cancer's severity and risk of metastasis are determined by the Gleason grade, a score based on the organization and morphology of prostate cancer glands. This paper proposes an automated workflow that follows pathologists' textitmodus operandi, isolating and classifying multi-scale patches of individual glands.
  arXiv  Detail & Related papers  (2022-09-27T14:08:19Z)
• Key-frame Guided Network for Thyroid Nodule Recognition using Ultrasound Videos [13.765306481109988]
  This paper proposes a novel method for the automated recognition of thyroid nodules through an exploration of ultrasound videos and key-frames. We first propose a detection-localization framework to automatically identify the clinical key-frame with a typical nodule in each ultrasound video. Based on the localized key-frame, we develop a key-frame guided video classification model for thyroid recognition.
  arXiv  Detail & Related papers  (2022-06-27T14:03:26Z)
• Multi-Scale Hybrid Vision Transformer for Learning Gastric Histology: AI-Based Decision Support System for Gastric Cancer Treatment [50.89811515036067]
  Gastric endoscopic screening is an effective way to decide appropriate gastric cancer (GC) treatment at an early stage, reducing GC-associated mortality rate. We propose a practical AI system that enables five subclassifications of GC pathology, which can be directly matched to general GC treatment guidance.
  arXiv  Detail & Related papers  (2022-02-17T08:33:52Z)
• Hierarchical Classification of Pulmonary Lesions: A Large-Scale Radio-Pathomics Study [38.78350161086617]
  Diagnosis of pulmonary lesions from computed tomography (CT) is important but challenging for clinical decision making in lung cancer related diseases. Deep learning has achieved great success in computer aided diagnosis (CADx) area for lung cancer, whereas it suffers from label ambiguity due to the difficulty in the radiological diagnosis. Considering that invasive pathological analysis serves as the clinical golden standard of lung cancer diagnosis, in this study, we solve the label ambiguity issue via a large-scale radio-pathomics dataset. This retrospective dataset, named Pulmonary-RadPath, enables development and validation of accurate deep learning systems to predict invasive pathological labels with a non-
  arXiv  Detail & Related papers  (2020-10-08T15:14:34Z)
• Spatio-spectral deep learning methods for in-vivo hyperspectral laryngeal cancer detection [49.32653090178743]
  Early detection of head and neck tumors is crucial for patient survival. Hyperspectral imaging (HSI) can be used for non-invasive detection of head and neck tumors. We present multiple deep learning techniques for in-vivo laryngeal cancer detection based on HSI.
  arXiv  Detail & Related papers  (2020-04-21T17:07:18Z)
• Segmentation for Classification of Screening Pancreatic Neuroendocrine Tumors [72.65802386845002]
  This work presents comprehensive results to detect in the early stage the pancreatic neuroendocrine tumors (PNETs) in abdominal CT scans. To the best of our knowledge, this task has not been studied before as a computational task. Our approach outperforms state-of-the-art segmentation networks and achieves a sensitivity of $89.47%$ at a specificity of $81.08%$.
  arXiv  Detail & Related papers  (2020-04-04T21:21:44Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.