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JOURNAL ARTICLE
Amir Yari, Paniz Fasih, Mohammad Hosseini Hooshiar, Ali Goodarzi, Seyedeh Farnaz Fattahi
PURPOSE: This study aimed to assess the performance of a deep learning algorithm (YOLOv5) in detecting different mandibular fracture types in panoramic images. METHODS: This study utilized a dataset of panoramic radiographic images with mandibular fractures. The dataset was divided into training, validation, and testing sets, with 60%, 20%, and 20% of the images, respectively. An equal number of control panoramic radiographs, which did not contain any fractures, were also randomly distributed among the three sets...
April 23, 2024: Dento Maxillo Facial Radiology
#2
JOURNAL ARTICLE
Katerina Deike, Andreas Decker, Paul Scheyhing, Julia Harten, Nadine Zimmermann, Daniel Paech, Oliver Peters, Silka D Freiesleben, Luisa-Sophie Schneider, Lukas Preis, Josef Priller, Eike Spruth, Slawek Altenstein, Andrea Lohse, Klaus Fliessbach, Okka Kimmich, Jens Wiltfang, Claudia Bartels, Niels Hansen, Frank Jessen, Ayda Rostamzadeh, Emrah Düzel, Wenzel Glanz, Enise I Incesoy, Michaela Butryn, Katharina Buerger, Daniel Janowitz, Michael Ewers, Robert Perneczky, Boris-Stephan Rauchmann, Stefan Teipel, Ingo Kilimann, Doreen Goerss, Christoph Laske, Matthias H Munk, Annika Spottke, Nina Roy, Michael Wagner, Sandra Roeske, Michael T Heneka, Frederic Brosseron, Alfredo Ramirez, Laura Dobisch, Steffen Wolfsgruber, Luca Kleineidam, Renat Yakupov, Melina Stark, Matthias C Schmid, Moritz Berger, Stefan Hetzer, Peter Dechent, Klaus Scheffler, Gabor C Petzold, Anja Schneider, Alexander Effland, Alexander Radbruch
OBJECTIVES: Impaired perivascular clearance has been suggested as a contributing factor to the pathogenesis of Alzheimer disease (AD). However, it remains unresolved when the anatomy of the perivascular space (PVS) is altered during AD progression. Therefore, this study investigates the association between PVS volume and AD progression in cognitively unimpaired (CU) individuals, both with and without subjective cognitive decline (SCD), and in those clinically diagnosed with mild cognitive impairment (MCI) or mild AD...
April 23, 2024: Investigative Radiology
#3
JOURNAL ARTICLE
Joon Hee Cho, Myeongju Kim, Hee Seung Nam, Seong Yun Park, Yong Seuk Lee
PURPOSE: The objective of this study was to develop a machine learning model that would predict lateral compartment osteoarthritis (OA) in the discoid lateral meniscus (DLM), from which to then identify factors contributing to lateral compartment OA, with a key focus on the patient's age. METHODS: Data were collected from 611 patients with symptomatic DLM diagnosed using magnetic resonance imaging between April 2003 and May 2022. Twenty features, including demographic, clinical and radiological data and six algorithms were used to develop the predictive machine learning models...
April 23, 2024: Knee Surgery, Sports Traumatology, Arthroscopy
#4
JOURNAL ARTICLE
Giovanni Castiglioni, Joaquín Vallejos, Jhon Intriago, María Isabel Hernández, Samuel Valenzuela, José Fernández, Ignacio Castro, Sergio Valenzuela, Pablo A Estévez, Cecilia Okuma
PURPOSE: We studied a pediatric group of patients with sellar-suprasellar tumors, aiming to develop a convolutional deep learning algorithm for radiological assistance to classify them into their respective cohort. METHODS: T1w and T2w preoperative magnetic resonance images of 226 Chilean patients were collected at the Institute of Neurosurgery Dr. Alfonso Asenjo (INCA), which were divided into three classes: healthy control (68 subjects), craniopharyngioma (58 subjects) and differential sellar/suprasellar tumors (100 subjects)...
April 22, 2024: Child's Nervous System: ChNS: Official Journal of the International Society for Pediatric Neurosurgery
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JOURNAL ARTICLE
Yasemin Kayadibi, Mehmet Sakıpcan Saracoglu, Seda Aladag Kurt, Enes Deger, Fatma Nur Soylu Boy, Nese Ucar, Gul Esen Icten
RATIONALE AND OBJECTIVES: To investigate the effectiveness of machine learning-based clinical, radiomics, and combined models in differentiating idiopathic granulomatous mastitis (IGM) from malignancy, both presenting as non-mass enhancement (NME) lesions on magnetic resonance imaging (MRI), and to compare these models with radiological evaluation. MATERIAL AND METHODS: A total of 178 patients (69 IGM and 109 breast cancer patients) with NME on breast MRI evaluated between March 2018 and April 2022, were included in this two-center study...
April 18, 2024: Academic Radiology
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JOURNAL ARTICLE
Harkiran K Kooner, Maksym Sharma, Marrissa J McIntosh, Inderdeep Dhaliwal, J Michael Nicholson, Miranda Kirby, Sarah Svenningsen, Grace Parraga
RATIONALE AND OBJECTIVES: It remains difficult to predict longitudinal outcomes in long-COVID, even with chest CT and functional MRI. 129 Xe MRI reflects airway dysfunction, measured using ventilation defect percent (VDP) and in long-COVID patients, MRI VDP was abnormal, suggestive of airways disease. While MRI VDP and quality-of-life improved 15-month post-COVID infection, both remained abnormal. To better understand the relationship of airways disease and quality-of-life improvements in patients with long-COVID, we extracted 129 Xe ventilation MRI textures and generated machine-learning models in an effort to predict improved quality-of-life, 15-month post-infection...
April 17, 2024: Academic Radiology
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REVIEW
Serlie Hartoonian, Matine Hosseini, Iman Yousefi, Mina Mahdian, Mitra Ghazizadeh Ahsaie
BACKGROUND: Artificial intelligence (AI) technology has been increasingly developed in oral and maxillofacial imaging. The aim of this systematic review was to assess the applications and performance of the developed algorithms in different dentomaxillofacial imaging modalities. STUDY DESIGN: A systematic search of PubMed and Scopus databases was performed. The search strategy was set as a combination of the following keywords: "Artificial Intelligence," "Machine Learning," "Deep Learning," "Neural Networks," "Head and Neck Imaging," and "Maxillofacial Imaging...
January 3, 2024: Oral Surgery, Oral Medicine, Oral Pathology and Oral Radiology
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JOURNAL ARTICLE
Qiang Wang, Henrik Nilsson, Keyang Xu, Xufu Wei, Danyu Chen, Dongqin Zhao, Xiaojun Hu, Anrong Wang, Guojie Bai
OBJECTIVES: This study aimed to investigate tumor heterogeneity of colorectal liver metastases (CRLM) and stratify the patients into different risk groups of prognoses following liver resection by applying an unsupervised radiomics machine-learning approach to preoperative CT images. METHODS: This retrospective study retrieved clinical information and CT images of 197 patients with CRLM from The Cancer Imaging Archive (TCIA) database. Radiomics features were extracted from a segmented liver lesion identified at the portal venous phase...
April 10, 2024: European Journal of Radiology
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JOURNAL ARTICLE
Ali S Tejani, Yee Seng Ng, Yin Xi, Jesse C Rayan
Artificial intelligence (AI) algorithms are prone to bias at multiple stages of model development, with potential for exacerbating health disparities. However, bias in imaging AI is a complex topic that encompasses multiple coexisting definitions. Bias may refer to unequal preference to a person or group owing to preexisting attitudes or beliefs, either intentional or unintentional. However, cognitive bias refers to systematic deviation from objective judgment due to reliance on heuristics, and statistical bias refers to differences between true and expected values, commonly manifesting as systematic error in model prediction (ie, a model with output unrepresentative of real-world conditions)...
May 2024: Radiographics: a Review Publication of the Radiological Society of North America, Inc
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JOURNAL ARTICLE
Annemarie Uhlig, Johannes Uhlig, Andreas Leha, Lorenz Biggemann, Sophie Bachanek, Michael Stöckle, Mathias Reichert, Joachim Lotz, Philip Zeuschner, Alexander Maßmann
OBJECTIVES: To distinguish histological subtypes of renal tumors using radiomic features and machine learning (ML) based on multiphase computed tomography (CT). MATERIAL AND METHODS: Patients who underwent surgical treatment for renal tumors at two tertiary centers from 2012 to 2022 were included retrospectively. Preoperative arterial (corticomedullary) and venous (nephrogenic) phase CT scans from these centers, as well as from external imaging facilities, were manually segmented, and standardized radiomic features were extracted...
April 18, 2024: European Radiology
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JOURNAL ARTICLE
Frank Niemeyer, Fabio Galbusera, Martijn Beukers, René Jonas, Youping Tao, Marion Fusellier, Marianna A Tryfonidou, Cornelia Neidlinger-Wilke, Annette Kienle, Hans-Joachim Wilke
BACKGROUND: Intervertebral disc degeneration is frequent in dogs and can be associated with symptoms and functional impairments. The degree of disc degeneration can be assessed on T2-weighted MRI scans using the Pfirrmann classification scheme, which was developed for the human spine. However, it could also be used to quantify the effectiveness of disc regeneration therapies. We developed and tested a deep learning tool able to automatically score the degree of disc degeneration in dog spines, starting from an existing model designed to process images of human patients...
June 2024: JOR Spine
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REVIEW
Zhifan Li, Xinran Liu, Ya Gao, Xingru Lu, Junqiang Lei
OBJECTIVE: This study aims to evaluate the diagnostic accuracy of ultrasound imaging (US)-based radiomics for the early prediction of response to neoadjuvant chemotherapy (NAC) in breast cancer patients. METHODS: We comprehensively searched PubMed, Cochrane Library, Embase, and Web of Science databases up to 1 January 2023 for eligible studies. We assessed the methodological quality of the enrolled studies with Radiomics Quality Score (RQS) and the Quality Assessment of Diagnostic Accuracy Studies-2 tools...
April 17, 2024: La Radiologia Medica
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JOURNAL ARTICLE
Austin Young, Kevin Tan, Faiq Tariq, Michael X Jin, Avraham Y Bluestone
Introduction In recent years, artificial intelligence (AI) in medical imaging has undergone unprecedented innovation and advancement, sparking a revolutionary transformation in healthcare. The field of radiology is particularly implicated, as clinical radiologists are expected to interpret an ever-increasing number of complex cases in record time. Machine learning software purchased by our institution is expected to help our radiologists come to a more prompt diagnosis by delivering point-of-care quantitative analysis of suspicious findings and streamlining clinical workflow...
March 2024: Curēus
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JOURNAL ARTICLE
Yang Song, Sirui Ma, Bing Mao, Kun Xu, Yuan Liu, Jingdong Ma, Jun Jia
OBJECTIVES: Preoperative diagnosis of oral ameloblastoma (AME) and odontogenic keratocyst (OKC) has been a challenge in dentistry. This study uses radiomics approaches and machine learning (ML) algorithms to characterize cone beam computed tomography (CBCT) image features for the preoperative differential diagnosis of AME and OKC and compares ML algorithms to expert radiologists to validate performance. METHODS: We retrospectively collected the data of 326 patients with AME and OKC, where all diagnoses were confirmed by histopathologic tests...
April 16, 2024: Dento Maxillo Facial Radiology
#15
JOURNAL ARTICLE
Alison Starke, Jacqueline Poxon, Kishen Patel, Paula Wells, Max Morris, Pandora Rudd, Karen Tipples, Niall MacDougall
OBJECTIVES: To determine if Limbus, an AI auto-contouring software, can offer meaningful time savings for prostate radiotherapy treatment planning. METHODS: Three clinical oncologists recorded the time taken to contour prostate and seminal vesicles, lymph nodes, bladder, rectum, bowel and femoral heads on CT scans for 30 prostate patients (15 prostate, 15 prostate and nodes). Limbus 1.6.0 was used to generate these contours on the 30 CT scans. The time taken by the oncologists to modify individual Limbus contours was noted and compared with manual contouring times...
April 16, 2024: British Journal of Radiology
#16
JOURNAL ARTICLE
Yukihiro Nomura, Shouhei Hanaoka, Naoto Hayashi, Takeharu Yoshikawa, Saori Koshino, Chiaki Sato, Momoko Tatsuta, Yuya Tanaka, Shintaro Kano, Moto Nakaya, Shohei Inui, Masashi Kusakabe, Takahiro Nakao, Soichiro Miki, Takeyuki Watadani, Ryusuke Nakaoka, Akinobu Shimizu, Osamu Abe
PURPOSE: The quality and bias of annotations by annotators (e.g., radiologists) affect the performance changes in computer-aided detection (CAD) software using machine learning. We hypothesized that the difference in the years of experience in image interpretation among radiologists contributes to annotation variability. In this study, we focused on how the performance of CAD software changes with retraining by incorporating cases annotated by radiologists with varying experience. METHODS: We used two types of CAD software for lung nodule detection in chest computed tomography images and cerebral aneurysm detection in magnetic resonance angiography images...
April 16, 2024: International Journal of Computer Assisted Radiology and Surgery
#17
JOURNAL ARTICLE
Hanife Pertek, Mustafa Kamaşak, Soner Kotan, Fatma Pertek Hatipoğlu, Ömer Hatipoğlu, Taha Emre Köse
OBJECTIVE: This study aimed to evaluate the usability of morphometric features obtained from mandibular panoramic radiographs in gender determination using machine learning algorithms. MATERIALS AND METHODS: High-resolution radiographs of 200 patients aged 20-77 (41.0 ± 12.7) were included in the study. Twelve different morphometric measurements were extracted from each digital panoramic radiography included in the study. These measurements were used as features in the machine learning phase in which six different machine learning algorithms were used (k-nearest neighbor, decision trees, support vector machines, naive Bayes, linear discrimination analysis, and neural networks)...
April 16, 2024: Oral Radiology
#18
JOURNAL ARTICLE
Benjamin D Killeen, Shreayan Chaudhary, Greg Osgood, Mathias Unberath
PURPOSE: The expanding capabilities of surgical systems bring with them increasing complexity in the interfaces that humans use to control them. Robotic C-arm X-ray imaging systems, for instance, often require manipulation of independent axes via joysticks, while higher-level control options hide inside device-specific menus. The complexity of these interfaces hinder "ready-to-hand" use of high-level functions. Natural language offers a flexible, familiar interface for surgeons to express their desired outcome rather than remembering the steps necessary to achieve it, enabling direct access to task-aware, patient-specific C-arm functionality...
April 15, 2024: International Journal of Computer Assisted Radiology and Surgery
#19
JOURNAL ARTICLE
Meng-Wen Liu, Xue Zhang, Yan-Mei Wang, Xu Jiang, Jiu-Ming Jiang, Meng Li, Li Zhang
BACKGROUND: Accurate prediction of occult lymph node metastasis (ONM) is an important basis for determining whether lymph node (LN) dissection is necessary in clinical stage IA lung adenocarcinoma patients. The aim of this study is to determine the best machine learning algorithm for radiomics modeling and to compare the performances of the radiomics model, the clinical-radilogical model and the combined model incorporate both radiomics features and clinical-radilogical features in preoperatively predicting ONM in clinical stage IA lung adenocarcinoma patients...
March 29, 2024: Journal of Thoracic Disease
#20
JOURNAL ARTICLE
Benjamin D Killeen, Han Zhang, Jan Mangulabnan, Mehran Armand, Russell H Taylor, Greg Osgood, Mathias Unberath
Surgical phase recognition (SPR) is a crucial element in the digital transformation of the modern operating theater. While SPR based on video sources is well-established, incorporation of interventional X-ray sequences has not yet been explored. This paper presents Pelphix, a first approach to SPR for X-ray-guided percutaneous pelvic fracture fixation, which models the procedure at four levels of granularity - corridor, activity, view, and frame value - simulating the pelvic fracture fixation workflow as a Markov process to provide fully annotated training data...
October 2023: Medical Image Computing and Computer-assisted Intervention: MICCAI ..
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