In Machine learning

Lahoti Preethi, Gummadi Krishna, Weikum Gerhard

2023

Failure analysis, Trustworthy ML, Uncertainty modeling

In Frontiers in cardiovascular medicine

INTRODUCTION : The primary factor for cardiovascular disease and upcoming cardiovascular events is atherosclerosis. Recently, carotid plaque texture, as observed on ultrasonography, is varied and difficult to classify with the human eye due to substantial inter-observer variability. High-resolution magnetic resonance (MR) plaque imaging offers naturally superior soft tissue contrasts to computed tomography (CT) and ultrasonography, and combining different contrast weightings may provide more useful information. Radiation freeness and operator independence are two additional benefits of M RI. However, other than preliminary research on MR texture analysis of basilar artery plaque, there is currently no information addressing MR radiomics on the carotid plaque.

METHODS : For the automatic segmentation of MRI scans to detect carotid plaque for stroke risk assessment, there is a need for a computer-aided autonomous framework to classify MRI scans automatically. We used to detect carotid plaque from MRI scans for stroke risk assessment pre-trained models, fine-tuned them, and adjusted hyperparameters according to our problem.

RESULTS : Our trained YOLO V3 model achieved 94.81% accuracy, RCNN achieved 92.53% accuracy, and MobileNet achieved 90.23% in identifying carotid plaque from MRI scans for stroke risk assessment. Our approach will prevent incorrect diagnoses brought on by poor image quality and personal experience.

CONCLUSION : The evaluations in this work have demonstrated that this methodology produces acceptable results for classifying magnetic resonance imaging (MRI) data.

Chen Ya-Fang, Chen Zhen-Jie, Lin You-Yu, Lin Zhi-Qiang, Chen Chun-Nuan, Yang Mei-Li, Zhang Jin-Yin, Li Yuan-Zhe, Wang Yi, Huang Yin-Hui

2023

MRI carotid plaque, YOLO V3, deep learning, stroke risk, transfer learning

In Frontiers in cardiovascular medicine

INTRODUCTION : Artificial intelligence can recognize complex patterns in large datasets. It is a promising technology to advance heart failure practice, as many decisions rely on expert opinions in the absence of high-quality data-driven evidence.

METHODS : We searched Embase, Web of Science, and PubMed databases for articles containing "artificial intelligence," "machine learning," or "deep learning" and any of the phrases "heart transplantation," "ventricular assist device," or "cardiogenic shock" from inception until August 2022. We only included original research addressing post heart transplantation (HTx) or mechanical circulatory support (MCS) clinical care. Review and data extraction were performed in accordance with PRISMA-Scr guidelines.

RESULTS : Of 584 unique publications detected, 31 met the inclusion criteria. The majority focused on outcome prediction post HTx (n = 13) and post durable MCS (n = 7), as well as post HTx and MCS management (n = 7, n = 3, respectively). One study addressed temporary mechanical circulatory support. Most studies advocated for rapid integration of AI into clinical practice, acknowledging potential improvements in management guidance and reliability of outcomes prediction. There was a notable paucity of external data validation and integration of multiple data modalities.

CONCLUSION : Our review showed mounting innovation in AI application in management of MCS and HTx, with the largest evidence showing improved mortality outcome prediction.

Al-Ani Mohammad A, Bai Chen, Hashky Amal, Parker Alex M, Vilaro Juan R, Aranda Juan M, Shickel Benjamin, Rashidi Parisa, Bihorac Azra, Ahmed Mustafa M, Mardini Mamoun T

2023

LVAD, artificial intelligence, deep learning, heart transplantation, machine learning, mechanical circulatory support

In Journal of orthopaedics

BACKGROUND : Lumbar disc degeneration (LDD) is considered as one of the main causes of low back pain. For clinical diagnosis of LDD, magnetic resonance imaging (MRI) is commonly used. Schmorl's node, high intensity zone (HIZ), Modic changes, and other MRI biomarkers of intervertebral disc (IVD) degeneration are also associated with low back pain. However, the progression and natural history of these features are unclear and there is limited predictive capacity with MRI.

PURPOSE : We aim to establish and validate a deep learning pipeline, EDPP-Flow, for the 5-year progression prediction of Schmorl's node, HIZ, and Modic changes, based on clinical MRIs.

MATERIALS AND METHODS : An MRI dataset developed on 1152 volunteers was used in this study. For each volunteer, two MRI scans, at baseline and 5-year follow-up, were collected and pathology labels were annotated as present or absent (with/without pathology) by two specialists with over 10 years of clinical experience. Our pipeline contained the published MRI-SegFlow and state-of-the-art convolutional neural network for progression prediction of endplate defects. The label distribution of the dataset is unbalanced, where the number of present samples was much smaller than absent samples. The resampling and data augmentation strategies were adopted to increase the number of present samples in the training process and balance the influence of different samples on the model, which can improve the prediction accuracy.

RESULTS : Our pipeline achieved high weighted accuracy, sensitivity, and specificity for progression prediction of Schmorl's node (89.46 ± 3.71%, 89.19 ± 2.70%, 89.72 ± 2.42%), HIZ (91.75 ± 2.48%, 93.07 ± 3.96%, 90.43 ± 2.51%), and Modic changes (87.51 ± 2.23%, 87.93 ± 1.72%, 87.10 ± 1.99%), on the unbalanced dataset (present sample's percentages of the 3 pathologies above were 4.3%, 11.7%, and 6.7%).

CONCLUSION : We developed and validated a deep learning pipeline, for the progression prediction of endplate defects, which showed high prediction accuracy on unbalanced data. The method has significant potential for clinical implementation.

Cheung Jason Pui Yin, Kuang Xihe, Zhang Teng, Wang Kun, Yang Cao

2023-Apr

Convolutional neural network, Deep learning, Disease progression prediction, Lumbar disc degeneration, Unbalanced data

In Frontiers in medicine

INTRODUCTION : State of the art artificial intelligence (AI) models have the potential to become a "one-stop shop" to improve diagnosis and prognosis in several oncological settings. The external validation of AI models on independent cohorts is essential to evaluate their generalization ability, hence their potential utility in clinical practice. In this study we tested on a large, separate cohort a recently proposed state-of-the-art convolutional neural network for the automatic segmentation of intraprostatic cancer lesions on PSMA PET images.

METHODS : Eighty-five biopsy proven prostate cancer patients who underwent ⁶⁸Ga PSMA PET for staging purposes were enrolled in this study. Images were acquired with either fully hybrid PET/MRI (N = 46) or PET/CT (N = 39); all participants showed at least one intraprostatic pathological finding on PET images that was independently segmented by two Nuclear Medicine physicians. The trained model was available at https://gitlab.com/dejankostyszyn/prostate-gtv-segmentation and data processing has been done in agreement with the reference work.

RESULTS : When compared to the manual contouring, the AI model yielded a median dice score = 0.74, therefore showing a moderately good performance. Results were robust to the modality used to acquire images (PET/CT or PET/MRI) and to the ground truth labels (no significant difference between the model's performance when compared to reader 1 or reader 2 manual contouring).

DISCUSSION : In conclusion, this AI model could be used to automatically segment intraprostatic cancer lesions for research purposes, as instance to define the volume of interest for radiomics or deep learning analysis. However, more robust performance is needed for the generation of AI-based decision support technologies to be proposed in clinical practice.

Ghezzo Samuele, Mongardi Sofia, Bezzi Carolina, Samanes Gajate Ana Maria, Preza Erik, Gotuzzo Irene, Baldassi Francesco, Jonghi-Lavarini Lorenzo, Neri Ilaria, Russo Tommaso, Brembilla Giorgio, De Cobelli Francesco, Scifo Paola, Mapelli Paola, Picchio Maria

2023

PSMA, convolutional neural network, external validation, prostate cancer, segmentation

In Frontiers in medicine

INTRODUCTION : It is critical to identify the stroke onset time of patients with acute ischemic stroke (AIS) for the treatment of endovascular thrombectomy (EVT). However, it is challenging to accurately ascertain this time for patients with wake-up stroke (WUS). The current study aimed to construct a deep learning approach based on computed tomography perfusion (CTP) or perfusion weighted imaging (PWI) to identify a 6-h window for patients with AIS for the treatment of EVT.

METHODS : We collected data from 377 patients with AIS, who were examined by CTP or PWI before making a treatment decision. Cerebral blood flow (CBF), time to maximum peak (Tmax), and a region of interest (ROI) mask were preprocessed from the CTP and PWI. We constructed the classifier based on a convolutional neural network (CNN), which was trained by CBF, Tmax, and ROI masks to identify patients with AIS within a 6-h window for the treatment of EVT. We compared the classification performance among a CNN, support vector machine (SVM), and random forest (RF) when trained by five different types of ROI masks. To assess the adaptability of the classifier of CNN for CTP and PWI, which were processed respectively from CTP and PWI groups.

RESULTS : Our results showed that the CNN classifier had a higher performance with an area under the curve (AUC) of 0.935, which was significantly higher than that of support vector machine (SVM) and random forest (RF) (p = 0.001 and p = 0.001, respectively). For the CNN classifier trained by different ROI masks, the best performance was trained by CBF, Tmax, and ROI masks of Tmax > 6 s. No significant difference was detected in the classification performance of the CNN between CTP and PWI (0.902 vs. 0.928; p = 0.557).

DISCUSSION : The CNN classifier trained by CBF, Tmax, and ROI masks of Tmax > 6 s had good performance in identifying patients with AIS within a 6-h window for the treatment of EVT. The current study indicates that the CNN model has potential to be used to accurately estimate the stroke onset time of patients with WUS.

Gao Hongyu, Bian Yueyan, Cheng Gen, Yu Huan, Cao Yuze, Zhang Huixue, Wang Jianjian, Li Qian, Yang Qi, Wang Lihua

2023

acute ischemic stroke, deep learning, endovascular thrombectomy, perfusion imaging, stroke onset time