@article{Bradshaw2022,

title = {Nuclear Medicine and Artificial Intelligence: Best Practices for Algorithm Development },

author = {Tyler J. Bradshaw and Ronald Boellaard and Joyita Dutta and Abhinav K. Jha and Paul Jacobs and Quanzheng Li and Chi Liu and Arkadiusz Sitek and Babak Saboury and Peter J.H. Scott and Piotr J. Slomka and John J. Sunderland and Richard L. Wahl and Fereshteh Yousefirizi and Sven Zuehlsdorff and Arman Rahmim and Ir\`{e}ne Buvat

},

url = {https://doi.org/10.2967/jnumed.121.262567},

doi = {https://doi.org/10.2967/jnumed.121.262567},

year  = {2022},

date = {2022-04-01},

journal = {Journal of Nuclear Medicine},

abstract = {The nuclear medicine field has seen a rapid expansion of academic and commercial interest in developing artificial intelligence (AI) algorithms. Users and developers can avoid some of the pitfalls of AI by recognizing and following best practices in AI algorithm development. In this article, recommendations on technical best practices for developing AI algorithms in nuclear medicine are provided, beginning with general recommendations and then continuing with descriptions of how one might practice these principles for specific topics within nuclear medicine. This report was produced by the AI Task Force of the Society of Nuclear Medicine and Molecular Imaging.



},

keywords = {Health Informatics},

pubstate = {published},

tppubtype = {article}

}

@article{nokey,

title = {Towards efficient GPGPU Cellular Automata model implementation using persistent active cells},

author = {Pawe\l Renc and Tomasz P\k{e}cak and Alessio De Rango and William Spataro and Giuseppe Mendicino and Jaros\law W\k{a}s

},

url = {https://www.sciencedirect.com/science/article/abs/pii/S1877750321001964?via%3Dihub},

doi = {https://doi.org/10.1016/j.jocs.2021.101538},

year  = {2022},

date = {2022-01-10},

journal = {Journal of Computational Science},

abstract = {Natural complex phenomena simulation relies on the application of advanced numerical models. Nevertheless, due to their inherent temporal and spatial computational complexity, efficient parallel computing algorithms are required in order to speed up simulation execution times. In this paper, we apply the Nvidia CUDA architecture to the simulation of a groundwater hydrological model based on the Cellular Automata formalism. Different implementations, using different memory access patterns and optimizations, regarding the application of persistent active cells (i.e., once a cell is activated, it remains such throughout a simulation), are presented and evaluated. The obtained results have demonstrated the full suitability of the approach in speeding up simulation times, thus resulting in a valid support for complex system modeling.



},

keywords = {Health Informatics},

pubstate = {published},

tppubtype = {article}

}

@article{nokey,

title = {Deep learning fetal ultrasound video model match human observers in biometric measurements},

author = {Szymon P\lotka and Adam Klasa and Aneta Lisowska and Joanna Seliga-Siwecka and Micha\l Lipa and Tomasz Trzci\'{n}ski and Arkadiusz Sitek

},

url = {https://iopscience.iop.org/article/10.1088/1361-6560/ac4d85/pdf},

doi = {https://doi.org/10.1088/1361-6560/ac4d85 },

year  = {2022},

date = {2022-02-16},

urldate = {2022-02-16},

journal = {Physics in Medicine \& Biology},

abstract = {Objective. This work investigates the use of deep convolutional neural networks (CNN) to automatically perform measurements of fetal body parts, including head circumference, biparietal diameter, abdominal circumference and femur length, and to estimate gestational age and fetal weight using fetal ultrasound videos. Approach. We developed a novel multi-task CNN-based spatio-temporal fetal US feature extraction and standard plane detection algorithm (called FUVAI) and evaluated the method on 50 freehand fetal US video scans. We compared FUVAI fetal biometric measurements with measurements made by five experienced sonographers at two time points separated by at least two weeks. Intra- and inter-observer variabilities were estimated. Main results. We found that automated fetal biometric measurements obtained by FUVAI were comparable to the measurements performed by experienced sonographers The observed differences in measurement values were within the range of inter- and intra-observer variability. Moreover, analysis has shown that these differences were not statistically significant when comparing any individual medical expert to our model. Significance. We argue that FUVAI has the potential to assist sonographers who perform fetal biometric measurements in clinical settings by providing them with suggestions regarding the best measuring frames, along with automated measurements. Moreover, FUVAI is able perform these tasks in just a few seconds, which is a huge difference compared to the average of six minutes taken by sonographers. This is significant, given the shortage of medical experts capable of interpreting fetal ultrasound images in numerous countries.



},

keywords = {Health Informatics},

pubstate = {published},

tppubtype = {article}

}

@article{nokey,

title = {Multi-institutional evaluation of a deep learning model for fully automated detection of aortic aneurysms in contrast and non-contrast CT},

author = {Xie Y and Graf B and Farzam P and Baker B and Lamoureux C and Sitek A

},

url = {https://spie.org/medical-imaging/presentation/Multi-institutional-evaluation-of-a-deep-learning-model-for-fully/12033-135},

year  = {2022},

date = {2022-01-01},

journal = {SPIE Medical Imaging},

abstract = {We developed and validated a research-only deep learning (DL) based automatic algorithm to detect thoracic and abdominal aortic aneurysms on contrast and non-contrast CT images and compared its performance with assessments obtained from retrospective radiology reports. The DL algorithm was developed using 556 CT scans. Manual annotations of aorta centerlines and cross-sectional aorta boundaries were created to train the algorithm. Aorta segmentation and aneurysm detection performances were evaluated on 2263 retrospective CT scans (154 thoracic and 176 abdominal aneurysms). Evaluation was performed by comparing the automatically detected aneurysm status to the aneurysm status reported in the radiology reports and the AUC was reported. In addition, a quantitative evaluation was performed to compare the automatically measured aortic diameters to manual diameters on a subset of 59 CT scans. Pearson correlation coefficient was used. For aneurysm detection, the AUC was 0.95 for thoracic aneurysm detection (95% confidence region [0.93, 0.97]) and 0.94 for abdominal aneurysm detection (95% confidence region [0.92, 0.96]). For aortic diameter measurement, the Pearson correlation coefficient was 0.973 (p\<0.001).

},

keywords = {Health Informatics},

pubstate = {published},

tppubtype = {article}

}

@article{nokey,

title = {Justifying Short-Term Load Forecasts Obtained with the Use of Neural Models},

author = {Tadeusz A. Grzeszczyk and Michal K. Grzeszczyk 

},

url = {https://www.mdpi.com/1996-1073/15/5/1852/pdf},

doi = {https://doi.org/10.3390/en15051852},

year  = {2022},

date = {2022-03-02},

journal = {Energies 2022, 15(5), 1852;},

abstract = {There is a lot of research on the neural models used for short-term load forecasting (STLF), which is crucial for improving the sustainable operation of energy systems with increasing technical, economic, and environmental requirements. Neural networks are computationally powerful; however, the lack of clear, readable and trustworthy justification of STLF obtained using such models is a serious problem that needs to be tackled. The article proposes an approach based on the local interpretable model-agnostic explanations (LIME) method that supports reliable premises justifying and explaining the forecasts. The use of the proposed approach makes it possible to improve the reliability of heuristic and experimental neural modeling processes, the results of which are difficult to interpret. Explaining the forecasting may facilitate the justification of the selection and the improvement of neural models for STLF, while contributing to a better understanding of the obtained results and broadening the knowledge and experience supporting the enhancement of energy systems security based on reliable forecasts and simplifying dispatch decisions.

},

keywords = {Health Informatics},

pubstate = {published},

tppubtype = {article}

}

@article{nokey,

title = {COVID-19 Drug Repurposing: a Network-based Framework for Exploring BioMedical Literature and Clinical Trials for Possible Treatments},

author = {Ahmed Abdeen Hamed and Tamer E. Fandy and Karolina L. Tkaczuk and Byung Suk Lee

},

url = {https://doi.org/10.3390/pharmaceutics14030567

},

doi = {https://doi.org/10.3390/pharmaceutics14030567},

year  = {2022},

date = {2022-03-04},

urldate = {2022-03-04},

journal = {Pharmaceutics },

abstract = {Background: With the Coronavirus becoming a new reality of our world, global efforts continue to seek answers to many questions regarding the spread, variants, vaccinations, and medications. Particularly, with the emergence of several strains (e.g., Delta, Omicron), vaccines will need further development to offer complete protection against the new variants. It is critical to identify antiviral treatments while the development of vaccines continues. In this regard, the repurposing of already FDA-approved drugs remains a major effort. In this paper, we investigate the hypothesis that a combination of FDA-approved drugs may be considered as a candidate for COVID-19 treatment if (1) there exists an evidence in the COVID-19 biomedical literature that suggests such a combination, and (2) there is match in the clinical trials space that validates this drug combination. Methods: We present a computational framework that is designed for detecting drug combinations, using the following components (a) a Text-mining module: to extract drug names from the abstract section of the biomedical publications and the intervention/treatment sections of clinical trial records. (b) a network model constructed from the drug names and their associations, (c) a clique similarity algorithm to identify candidate drug treatments. Result and Conclusions: Our framework has identified treatments in the form of two, three, or four drug combinations (e.g., hydroxychloroquine, doxycycline, and azithromycin). The identifications of the various treatment candidates provided sufficient evidence that supports the trustworthiness of our hypothesis. View Full-Text

},

keywords = {Clinical Data Science},

pubstate = {published},

tppubtype = {article}

}