AIMC Topic: Retrospective Studies

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Lung-PNet: An Automated Deep Learning Model for the Diagnosis of Invasive Adenocarcinoma in Pure Ground-Glass Nodules on Chest CT.

AJR. American journal of roentgenology
Pure ground-glass nodules (pGGNs) on chest CT representing invasive adenocarcinoma (IAC) warrant lobectomy with lymph node resection. For pGGNs representing other entities, close follow-up or sublobar resection without node dissection may be appropr...

Novel Machine Learning Algorithms for Prediction of Treatment Decisions in Adult Patients With Class III Malocclusion.

Journal of oral and maxillofacial surgery : official journal of the American Association of Oral and Maxillofacial Surgeons
BACKGROUND: Management of Class III (Cl III) dentoskeletal phenotype is often expert-driven.

Retrospective evaluation of the improvement in the urinary status-related quality of life after robot-assisted radical prostatectomy.

International journal of urology : official journal of the Japanese Urological Association
OBJECTIVES: This study aimed to investigate the characteristics of patients who report improvement in quality of life (QOL) related to urinary status after undergoing robot-assisted radical prostatectomy (RARP) for localized prostate cancer.

Improving measurement of blood-brain barrier permeability with reduced scan time using deep-learning-derived capillary input function.

NeuroImage
PURPOSE: In Dynamic contrast-enhanced MRI (DCE-MRI), Arterial Input Function (AIF) has been shown to be a significant contributor to uncertainty in the estimation of kinetic parameters. This study is to assess the feasibility of using a deep learning...

Left recurrent nerve lymph node dissection in robotic esophagectomy for esophageal cancer without esophageal traction.

World journal of surgical oncology
BACKGROUND: Because the robotic arm is located on the dorsal side of the patient, when the esophagus is pulled dorsally for the left recurrent nerve lymph node (LRLN) dissection, the robotic arm interferes with the surgical field. This made it diffic...

Shortening Acquisition Time and Improving Image Quality for Pelvic MRI Using Deep Learning Reconstruction for Diffusion-Weighted Imaging at 1.5 T.

Academic radiology
RATIONALE AND OBJECTIVES: To determine the impact on acquisition time reduction and image quality of a deep learning (DL) reconstruction for accelerated diffusion-weighted imaging (DWI) of the pelvis at 1.5 T compared to standard DWI.

Deep learning-based motion correction algorithm for coronary CT angiography: Lowering the phase requirement for morphological and functional evaluation.

Journal of applied clinical medical physics
PURPOSE: To investigate the performance of a deep learning-based motion correction algorithm (MCA) at various cardiac phases of coronary computed tomography angiography (CCTA), and determine the extent to which it may allow for reliable morphological...

Artificial intelligence for the diagnosis of clinically significant prostate cancer based on multimodal data: a multicenter study.

BMC medicine
BACKGROUND: The introduction of multiparameter MRI and novel biomarkers has greatly improved the prediction of clinically significant prostate cancer (csPCa). However, decision-making regarding prostate biopsy and prebiopsy examinations is still diff...

A novel machine learning model for predicting clinical pregnancy after laparoscopic tubal anastomosis.

BMC pregnancy and childbirth
BACKGROUND: Laparoscopic tubal anastomosis (LTA) is a treatment for women who require reproduction after ligation, and there are no reliable prediction models or clinically useful tools for predicting clinical pregnancy in women who receive this proc...

Artificial intelligence in the service of intrauterine insemination and timed intercourse in spontaneous cycles.

Fertility and sterility
OBJECTIVE: To develop a machine learning model designed to predict the time of ovulation and optimal fertilization window for performing intrauterine insemination or timed intercourse (TI) in natural cycles.