IU Indianapolis ScholarWorks :: Browsing by Subject "Medical imaging"

Browsing by Subject "Medical imaging"

Now showing 1 - 9 of 9

A review of deep learning and radiomics approaches for pancreatic cancer diagnosis from medical imaging
(Wolters Kluwer, 2023) Yao, Lanhong; Zhang, Zheyuan; Keles, Elif; Yazici, Cemal; Tirkes, Temel; Bagco, Ulas; Radiology and Imaging Sciences, School of Medicine
Purpose of review: Early and accurate diagnosis of pancreatic cancer is crucial for improving patient outcomes, and artificial intelligence (AI) algorithms have the potential to play a vital role in computer-aided diagnosis of pancreatic cancer. In this review, we aim to provide the latest and relevant advances in AI, specifically deep learning (DL) and radiomics approaches, for pancreatic cancer diagnosis using cross-sectional imaging examinations such as computed tomography (CT) and magnetic resonance imaging (MRI). Recent findings: This review highlights the recent developments in DL techniques applied to medical imaging, including convolutional neural networks (CNNs), transformer-based models, and novel deep learning architectures that focus on multitype pancreatic lesions, multiorgan and multitumor segmentation, as well as incorporating auxiliary information. We also discuss advancements in radiomics, such as improved imaging feature extraction, optimized machine learning classifiers and integration with clinical data. Furthermore, we explore implementing AI-based clinical decision support systems for pancreatic cancer diagnosis using medical imaging in practical settings. Summary: Deep learning and radiomics with medical imaging have demonstrated strong potential to improve diagnostic accuracy of pancreatic cancer, facilitate personalized treatment planning, and identify prognostic and predictive biomarkers. However, challenges remain in translating research findings into clinical practice. More studies are required focusing on refining these methods, addressing significant limitations, and developing integrative approaches for data analysis to further advance the field of pancreatic cancer diagnosis.
AI in Medical Imaging Informatics: Current Challenges and Future Directions
(IEEE, 2020-07) Panayides, Andreas S.; Amini, Amir; Filipovic, Nenad D.; Sharma, Ashish; Tsaftaris, Sotirios A.; Young, Alistair; Foran, David; Do, Nhan; Golemati, Spyretta; Kurc, Tahsin; Huang, Kun; Nikita, Konstantina S.; Veasey, Ben P.; Zervakis, Michalis; Saltz, Joel H.; Pattichis, Constantinos S.; Biostatistics & Health Data Science, School of Medicine
This paper reviews state-of-the-art research solutions across the spectrum of medical imaging informatics, discusses clinical translation, and provides future directions for advancing clinical practice. More specifically, it summarizes advances in medical imaging acquisition technologies for different modalities, highlighting the necessity for efficient medical data management strategies in the context of AI in big healthcare data analytics. It then provides a synopsis of contemporary and emerging algorithmic methods for disease classification and organ/ tissue segmentation, focusing on AI and deep learning architectures that have already become the de facto approach. The clinical benefits of in-silico modelling advances linked with evolving 3D reconstruction and visualization applications are further documented. Concluding, integrative analytics approaches driven by associate research branches highlighted in this study promise to revolutionize imaging informatics as known today across the healthcare continuum for both radiology and digital pathology applications. The latter, is projected to enable informed, more accurate diagnosis, timely prognosis, and effective treatment planning, underpinning precision medicine.
Artificial intelligence in gastrointestinal endoscopy: a comprehensive review
(Hellenic Society of Gastroenterology, 2024) Ali, Hassam; Muzammil, Muhammad Ali; Dahiya, Dushyant Singh; Ali, Farishta; Yasin, Shafay; Hanif, Waqar; Gangwani, Manesh Kumar; Aziz, Muhammad; Khalaf, Muhammad; Basuli, Debargha; Al-Haddad, Mohammad; Medicine, School of Medicine
Integrating artificial intelligence (AI) into gastrointestinal (GI) endoscopy heralds a significant leap forward in managing GI disorders. AI-enabled applications, such as computer-aided detection and computer-aided diagnosis, have significantly advanced GI endoscopy, improving early detection, diagnosis and personalized treatment planning. AI algorithms have shown promise in the analysis of endoscopic data, critical in conditions with traditionally low diagnostic sensitivity, such as indeterminate biliary strictures and pancreatic cancer. Convolutional neural networks can markedly improve the diagnostic process when integrated with cholangioscopy or endoscopic ultrasound, especially in the detection of malignant biliary strictures and cholangiocarcinoma. AI's capacity to analyze complex image data and offer real-time feedback can streamline endoscopic procedures, reduce the need for invasive biopsies, and decrease associated adverse events. However, the clinical implementation of AI faces challenges, including data quality issues and the risk of overfitting, underscoring the need for further research and validation. As the technology matures, AI is poised to become an indispensable tool in the gastroenterologist's arsenal, necessitating the integration of robust, validated AI applications into routine clinical practice. Despite remarkable advances, challenges such as operator-dependent accuracy and the need for intricate examinations persist. This review delves into the transformative role of AI in enhancing endoscopic diagnostic accuracy, particularly highlighting its utility in the early detection and personalized treatment of GI diseases.
Automatic comprehensive radiological reports for clinical acute stroke MRIs
(Springer Nature, 2023-07-10) Liu, Chin-Fu; Zhao, Yi; Yedavalli, Vivek; Leigh, Richard; Falcao, Vito; STIR and VISTA Imaging investigators; Miller, Michael I.; Hillis, Argye E.; Faria, Andreia V.; Biostatistics and Health Data Science, School of Medicine
Background: Although artificial intelligence systems that diagnosis among different conditions from medical images are long term aims, specific goals for automation of human-labor, time-consuming tasks are not only feasible but equally important. Acute conditions that require quantitative metrics, such as acute ischemic strokes, can greatly benefit by the consistency, objectiveness, and accessibility of automated radiological reports. Methods: We used 1,878 annotated brain MRIs to generate a fully automated system that outputs radiological reports in addition to the infarct volume, 3D digital infarct mask, and the feature vector of anatomical regions affected by the acute infarct. This system is associated to a deep-learning algorithm for segmentation of the ischemic core and to parcellation schemes defining arterial territories and classically-identified anatomical brain structures. Results: Here we show that the performance of our system to generate radiological reports was comparable to that of an expert evaluator. The weight of the components of the feature vectors that supported the prediction of the reports, as well as the prediction probabilities are outputted, making the pre-trained models behind our system interpretable. The system is publicly available, runs in real time, in local computers, with minimal computational requirements, and it is readily useful for non-expert users. It supports large-scale processing of new and legacy data, enabling clinical and translational research. Conclusion: The generation of reports indicates that our fully automated system is able to extract quantitative, objective, structured, and personalized information from stroke MRIs.
On the Computation of the Average of Spatial Displacements
(ASME, 2022) Ge, Q. J.; Yu, Zihan; Arbab, Mona; Langer, Mark; Radiation Oncology, School of Medicine
Many applications in biomechanics and medical imaging call for the analysis of the kinematic errors in a group of patients statistically using the average displacement and the standard deviations from the average. This paper studies the problem of computing the average displacement from a set of given spatial displacements using three types of parametric representations: Euler angles and translation vectors, unit quaternions and translation vectors, and dual quaternions. It has been shown that the use of Euclidean norm in the space of unit quaternions reduces the problem to that of computing the average for each quaternion component separately and independently. While the resulting algorithm is simple, the change of the sign of a unit quaternion could lead to an incorrect result. A novel kinematic measure based on dual quaternions is introduced to capture the separation between two spatial displacement. This kinematic measure is then used to formulate a constrained least squares minimization problem. It has been shown that the problem decomposes into that of finding the optimal translation vector and the optimal unit quaternion. The former is simply the centroid of the set of given translation vectors and the latter can be obtained as the eigenvector corresponding to the least eigenvalue of a 4 × 4 positive definite symmetric matrix. It is found that the weight factor used in combining rotations and translations in the formulation does not play a role in the final outcome. Examples are provided to show the comparisons of these methods.
Peri-Implant Bone Loss and Peri-Implantitis: A Report of Three Cases and Review of the Literature
(Hindawi Publishing Corporation, 2016) John, Vanchit; Shin, Daniel; Marlow, Allison; Hamada, Yusuke; Department of Periodontics & Allied Dental Programs
Dental implant supported restorations have been added substantially to the clinical treatment options presented to patients. However, complications with these treatment options also arise due to improper patient selection and inadequate treatment planning combined with poor follow-up care. The complications related to the presence of inflammation include perimucositis, peri-implant bone loss, and peri-implantitis. Prevalence rates of these complications have been reported to be as high as 56%. Treatment options that have been reported include nonsurgical therapy, the use of locally delivered and systemically delivered antibiotics, and surgical protocols aimed at regenerating the lost bone and soft tissue around the implants. The aim of this article is to report on three cases and review some of the treatment options used in their management.
Treatment of an Erratic Extraction Socket for Implant Therapy in a Patient with Chronic Periodontitis
(Hindawi Publishing Corporation, 2016) Hamada, Yusuke; Prabhu, Srividya; John, Vanchit; Department of Periodontics & Allied Dental Programs
As implant therapy becomes more commonplace in daily practice, preservation and preparation of edentulous sites are key. Many times, however, implant therapy may not be considered at the time of tooth extraction and additional measures are not taken to conserve the edentulous site. While the healing process in extraction sockets has been well investigated and bone fill can be expected, there are cases where even when clinicians perform thorough debridement of the sockets, connective tissue infiltration into the socket can occur. This phenomenon, known as "erratic healing," may be associated with factors that lead to peri-implant disease and should be appropriately managed and treated prior to surgical implant placement. This case report describes the successful management of an erratic healing extraction socket in a 62-year-old Caucasian male patient with chronic periodontitis and the outcomes of an evidence-based treatment protocol performed prior to implant therapy. Careful preoperative analysis and cone beam computed tomography imaging can help detect signs of impaired healing in future implant sites and prevent surgical complications.
Understanding metric-related pitfalls in image analysis validation
(ArXiv, 2023-09-25) Reinke, Annika; Tizabi, Minu D.; Baumgartner, Michael; Eisenmann, Matthias; Heckmann-Nötzel, Doreen; Kavur, A. Emre; Rädsch, Tim; Sudre, Carole H.; Acion, Laura; Antonelli, Michela; Arbel, Tal; Bakas, Spyridon; Benis, Arriel; Blaschko, Matthew B.; Buettner, Florian; Cardoso, M. Jorge; Cheplygina, Veronika; Chen, Jianxu; Christodoulou, Evangelia; Cimini, Beth A.; Collins, Gary S.; Farahani, Keyvan; Ferrer, Luciana; Galdran, Adrian; Van Ginneken, Bram; Glocker, Ben; Godau, Patrick; Haase, Robert; Hashimoto, Daniel A.; Hoffman, Michael M.; Huisman, Merel; Isensee, Fabian; Jannin, Pierre; Kahn, Charles E.; Kainmueller, Dagmar; Kainz, Bernhard; Karargyris, Alexandros; Karthikesalingam, Alan; Kenngott, Hannes; Kleesiek, Jens; Kofler, Florian; Kooi, Thijs; Kopp-Schneider, Annette; Kozubek, Michal; Kreshuk, Anna; Kurc, Tahsin; Landman, Bennett A.; Litjens, Geert; Madani, Amin; Maier-Hein, Klaus; Martel, Anne L.; Mattson, Peter; Meijering, Erik; Menze, Bjoern; Moons, Karel G. M.; Müller, Henning; Nichyporuk, Brennan; Nickel, Felix; Petersen, Jens; Rafelski, Susanne M.; Rajpoot, Nasir; Reyes, Mauricio; Riegler, Michael A.; Rieke, Nicola; Saez-Rodriguez, Julio; Sánchez, Clara I.; Shetty, Shravya; Summers, Ronald M.; Taha, Abdel A.; Tiulpin, Aleksei; Tsaftaris, Sotirios A.; Van Calster, Ben; Varoquaux, Gaël; Yaniv, Ziv R.; Jäger, Paul F.; Maier-Hein, Lena; Pathology and Laboratory Medicine, School of Medicine
Validation metrics are key for the reliable tracking of scientific progress and for bridging the current chasm between artificial intelligence (AI) research and its translation into practice. However, increasing evidence shows that particularly in image analysis, metrics are often chosen inadequately in relation to the underlying research problem. This could be attributed to a lack of accessibility of metric-related knowledge: While taking into account the individual strengths, weaknesses, and limitations of validation metrics is a critical prerequisite to making educated choices, the relevant knowledge is currently scattered and poorly accessible to individual researchers. Based on a multi-stage Delphi process conducted by a multidisciplinary expert consortium as well as extensive community feedback, the present work provides the first reliable and comprehensive common point of access to information on pitfalls related to validation metrics in image analysis. Focusing on biomedical image analysis but with the potential of transfer to other fields, the addressed pitfalls generalize across application domains and are categorized according to a newly created, domain-agnostic taxonomy. To facilitate comprehension, illustrations and specific examples accompany each pitfall. As a structured body of information accessible to researchers of all levels of expertise, this work enhances global comprehension of a key topic in image analysis validation.
Using cognitive fit theory to evaluate patient understanding of medical images
(IEEE, 2017) Gichoya, Judy Wawira; Alarifi, Mohammad; Bhaduri, Ria; Tahir, Bilal; Purkayastha, Saptarshi; Radiology and Imaging Sciences, School of Medicine
Patients are increasingly presented with their health data through patient portals in an attempt to engage patients in their own care. Due to the large amounts of data generated during a patient visit, the medical information when shared with patients can be overwhelming and cause anxiety due to lack of understanding. Health care organizations are attempting to improve transparency by providing patients with access to visit information. In this paper, we present our findings from a research study to evaluate patient understanding of medical images. We used cognitive fit theory to evaluate existing tools and images that are shared with patients and analyzed the relevance of such sharing. We discover that medical images need a lot of customization before they can be shared with patients. We suggest that new tools for medical imaging should be developed to fit the cognitive abilities of patients.

Browsing by Subject "Medical imaging"

Results Per Page

Sort Options