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Radiology Radiology

Deep learning-based automatic delineation of the hippocampus by MRI: geometric and dosimetric evaluation.

In Radiation oncology (London, England)

BACKGROUND : Whole brain radiotherapy (WBRT) can impair patients' cognitive function. Hippocampal avoidance during WBRT can potentially prevent this side effect. However, manually delineating the target area is time-consuming and difficult. Here, we proposed a credible approach of automatic hippocampal delineation based on convolutional neural networks.

METHODS : Referring to the hippocampus contouring atlas proposed by RTOG 0933, we manually delineated (MD) the hippocampus on the MRI data sets (3-dimensional T1-weighted with slice thickness of 1 mm, n = 175), which were used to construct a three-dimensional convolutional neural network aiming for the hippocampus automatic delineation (AD). The performance of this AD tool was tested on three cohorts: (a) 3D T1 MRI with 1-mm slice thickness (n = 30); (b) non-3D T1-weighted MRI with 3-mm slice thickness (n = 19); (c) non-3D T1-weighted MRI with 1-mm slice thickness (n = 11). All MRIs confirmed with normal hippocampus has not been violated by any disease. Virtual radiation plans were created for AD and MD hippocampi in cohort c to evaluate the clinical feasibility of the artificial intelligence approach. Statistical analyses were performed using SPSS version 23. P < 0.05 was considered significant.

RESULTS : The Dice similarity coefficient (DSC) and Average Hausdorff Distance (AVD) between the AD and MD hippocampi are 0.86 ± 0.028 and 0.18 ± 0.050 cm in cohort a, 0.76 ± 0.035 and 0.31 ± 0.064 cm in cohort b, 0.80 ± 0.015 and 0.24 ± 0.021 cm in cohort c, respectively. The DSC and AVD in cohort a were better than those in cohorts b and c (P < 0.01). There is no significant difference between the radiotherapy plans generated using the AD and MD hippocampi.

CONCLUSION : The AD of the hippocampus based on a deep learning algorithm showed satisfying results, which could have a positive impact on improving delineation accuracy and reducing work load.

Pan Kaicheng, Zhao Lei, Gu Song, Tang Yi, Wang Jiahao, Yu Wen, Zhu Lucheng, Feng Qi, Su Ruipeng, Xu Zhiyong, Li Xiadong, Ding Zhongxiang, Fu Xiaolong, Ma Shenglin, Yan Jun, Kang Shigong, Zhou Tao, Xia Bing


Artificial intelligence, Hippocampus, MRI

General General

Deep learning encodes robust discriminative neuroimaging representations to outperform standard machine learning.

In Nature communications ; h5-index 260.0

Recent critical commentaries unfavorably compare deep learning (DL) with standard machine learning (SML) approaches for brain imaging data analysis. However, their conclusions are often based on pre-engineered features depriving DL of its main advantage - representation learning. We conduct a large-scale systematic comparison profiled in multiple classification and regression tasks on structural MRI images and show the importance of representation learning for DL. Results show that if trained following prevalent DL practices, DL methods have the potential to scale particularly well and substantially improve compared to SML methods, while also presenting a lower asymptotic complexity in relative computational time, despite being more complex. We also demonstrate that DL embeddings span comprehensible task-specific projection spectra and that DL consistently localizes task-discriminative brain biomarkers. Our findings highlight the presence of nonlinearities in neuroimaging data that DL can exploit to generate superior task-discriminative representations for characterizing the human brain.

Abrol Anees, Fu Zening, Salman Mustafa, Silva Rogers, Du Yuhui, Plis Sergey, Calhoun Vince


Surgery Surgery

Application of artificial intelligence in the dental field: A literature review.

In Journal of prosthodontic research

PURPOSE : The purpose of this study was to comprehensively review the literature regarding the application of artificial intelligence (AI) in the dental field,focusing on the evaluation criteria and architecture types.

STUDY SELECTION : Electronic databases (PubMed, Cochrane Library, Scopus) were searched. Full-text articles describing the clinical application of AI for the detection, diagnosis, and treatment of lesions and the AI method/architecture were included.

RESULTS : The primary search presented 422 studies from 1996 to 2019, and 58 studies were finally selected. Regarding the year of publication, the oldest study, which was reported in 1996, focused on "oral and maxillofacial surgery." Machine-learning architectures were employed in the selected studies, while approximately half of them (29/58) employed neural networks. Regarding the evaluation criteria, eight studies compared the results obtained by AI with the diagnoses formulated by dentists, while several studies compared two or more architectures in terms of performance. The following parameters were employed for evaluating the AI performance: accuracy, sensitivity, specificity, mean absolute error, root mean squared error, and area under the receiver operating characteristic curve.

CONCLUSIONS : Application of AI in the dental field has progressed; however, the criteria for evaluating the efficacy of AI have not been clarified. It is necessary to obtain better quality data for machine learning to achieve the effective diagnosis of lesions and suitable treatment planning.

Kishimoto Takahiro, Goto Takaharu, Matsuda Takashi, Iwawaki Yuki, Ichikawa Tetsuo


Artificial intelligence, Data mining, Dental field, Machine learning, Neural N etworks

General General

Fighting viruses with materials science: Prospects for antivirus surfaces, drug delivery systems and artificial intelligence.

In Dental materials : official publication of the Academy of Dental Materials

OBJECTIVE : Viruses on environmental surfaces, in saliva and other body fluids represent risk of contamination for general population and healthcare professionals. The development of vaccines and medicines is costly and time consuming. Thus, the development of novel materials and technologies to decrease viral availability, viability, infectivity, and to improve therapeutic outcomes can positively impact the prevention and treatment of viral diseases.

METHODS : Herein, we discuss (a) interaction mechanisms between viruses and materials, (b) novel strategies to develop materials with antiviral properties and oral antiviral delivery systems, and (c) the potential of artificial intelligence to design and optimize preventive measures and therapeutic regimen.

RESULTS : The mechanisms of viral adsorption on surfaces are well characterized but no major breakthrough has become clinically available. Materials with fine-tuned physical and chemical properties have the potential to compromise viral availability and stability. Emerging strategies using oral antiviral delivery systems and artificial intelligence can decrease infectivity and improve antiviral therapies.

SIGNIFICANCE : Emerging viral infections are concerning due to risk of mortality, as well as psychological and economic impacts. Materials science emerges for the development of novel materials and technologies to diminish viral availability, infectivity, and to enable enhanced preventive and therapeutic strategies, for the safety and well-being of humankind.

Rosa Vinicius, Ho Dean, Sabino-Silva Robinson, Siqueira Walter L, Silikas Nikolaos


COVID-19, Coating, Coronavirus, Diagnostic, Infection, Nanomaterial, Nanotechnology, Pandemic, Saliva, Vaccine

Public Health Public Health

Patient Journey Map to Improve the Home Isolation Experience of Persons with Mild COVID-19 Symptoms: Design Research for Service Touchpoints of Artificial Intelligence in eHealth.

In JMIR medical informatics ; h5-index 23.0

BACKGROUND : In the context of COVID-19 outbreak, 80% of the persons are those with mild symptoms who are required to self-recover at home. They have a strong demand for remote healthcare that despite the great potential of artificial intelligence are not met in the current (e)-health services. Understanding the real needs of these persons is lacking.

OBJECTIVE : The aim of this paper is to contribute with a fine grained understanding of the home isolation experience of persons with mild COVID-19 symptoms, in order to enhance AI in eHealth services.

METHODS : Design research in which a qualitative approach was used to map the patient journey. Data on the home isolation experiences of persons with mild COVID-19 symptoms was collected from top viewed personal video stories on YouTube and their additional comment threads. For the analysis this data was transcribed, coded and mapped into the patient journey map.

RESULTS : The key findings on the home isolation experience of persons with mild COVID-19 symptoms concern: (a) Considerable awareness period before testing positive and home-recovery period; (b) Less generic but more personal symptoms experiences; (c) Negative mood experience curve; (d) Inadequate home healthcare service support for mild COVID-19 patients through all stages. (e) Benefits and drawbacks of Social media support for mild COVID-19 patients; (f) Several touchpoint needs for home healthcare interaction with AI.

CONCLUSIONS : The design of the patient journey map and underlying insights on the home isolation experience of persons with mild COVID-19 symptoms serves Health - and IT professionals to more effectively apply AI technology into eHealth services for mild Covid-19 patients, for which three main service concepts are proposed: (I) Trustful public health information to release stress; (II) Personal Covid-19 health monitoring. (III) Community Support.

He Qian, Du Fei, Simonse Lianne W L


Radiology Radiology

Preserving image texture while reducing radiation dose with a deep learning image reconstruction algorithm in chest CT: A phantom study.

In Physica medica : PM : an international journal devoted to the applications of physics to medicine and biology : official journal of the Italian Association of Biomedical Physics (AIFB)

PURPOSE : To assess whether a deep learning image reconstruction algorithm (TrueFidelity) can preserve the image texture of conventional filtered back projection (FBP) at reduced dose levels attained by ASIR-V in chest CT.

METHODS : Phantom images were acquired using a clinical chest protocol (7.6 mGy) and two levels of dose reduction (60% and 80%). Images were reconstructed with FBP, ASIR-V (50% and 100% blending) and TrueFidelity (low (DL-L), medium (DL-M) and high (DL-H) strength). Noise (SD), noise power spectrum (NPS) and task-based transfer function (TTF) were calculated. Noise texture was quantitatively compared by computing root-mean-square deviations (RMSD) of NPS with respect to FBP. Four experienced readers performed a contrast-detail evaluation. The dose reducing potential of TrueFidelity compared to ASIR-V was assessed by fitting SD and contrast-detail as a function of dose.

RESULTS : DL-M and DL-H reduced noise and NPS area compared to FBP and 50% ASIR-V, at all dose levels. At 7.6 mGy, NPS of ASIR-V 50/100% was shifted towards lower frequencies (fpeak = 0.22/0.13 mm-1, RMSD = 0.14/0.38), with respect to FBP (fpeak = 0.30 mm-1). Marginal difference was observed for TrueFidelity: fpeak = 0.33/0.30/0.30 mm-1 and RMSD = 0.03/0.04/0.07 for L/M/H strength. Values of TTF50% were independent of DL strength and higher compared to FBP and ASIR-V, at all dose and contrast levels. Contrast-detail was highest for DL-H at all doses. Compared to 50% ASIR-V, DL-H had an estimated dose reducing potential of 50% on average, without impairing noise, texture and detectability.

CONCLUSIONS : TrueFidelity preserves the image texture of FBP, while outperforming ASIR-V in terms of noise, spatial resolution and detectability at lower doses.

Franck Caro, Zhang Guozhi, Deak Paul, Zanca Federica


Chest, Computed tomography, Contrast-detail evaluation, Deep learning image reconstruction, Dosimetry, Image quality, Iterative reconstruction