The Experts below are selected from a list of 525369 Experts worldwide ranked by ideXlab platform

Mario Coccia - One of the best experts on this subject based on the ideXlab platform.

  • deep Learning Technology for improving cancer care in society new directions in cancer imaging driven by artificial intelligence
    Technology in Society, 2020
    Co-Authors: Mario Coccia
    Abstract:

    Abstract The goal of this study is to show emerging applications of deep Learning Technology in cancer imaging. Deep Learning Technology is a family of computational methods that allow an algorithm to program itself by Learning from a large set of examples that demonstrate the desired behavior. Applications of deep Learning Technology to cancer imaging can assist pathologists in the detection and classification of cancer in the early stages of its development to allow patients to have appropriate treatments that can increase their survival. Statistical analyses and other analytical approaches, based on data of ScienceDirect (a source for scientific research), suggest that the sharp increase of the studies of deep Learning Technology in cancer imaging seems to be driven by high rates of mortality of some types of cancer (e.g., lung and breast) in order to solve consequential problems of a more accurate detection and characterization of cancer types to apply efficient anti-cancer therapies. Moreover, this study also shows sources of the trajectories of deep Learning Technology in cancer imaging at level of scientific subject areas, universities and countries with the highest scientific production in these research fields. This new Technology, in accordance with Amara's law, can generate a shift of technological paradigm for diagnostic assessment of any cancer type and disease. This new Technology can also generate socioeconomic benefits for poor regions because they can send digital images to labs of other developed regions to have diagnosis of cancer types, reducing as far as possible current gap in healthcare sector among different regions.

  • deep Learning Technology for improving cancer care in society new directions in cancer imaging driven by artificial intelligence
    Social Science Research Network, 2019
    Co-Authors: Mario Coccia
    Abstract:

    The goal of this study is to show emerging applications of deep Learning Technology in cancer imaging. Deep Learning Technology is a family of computational methods that allow an algorithm to program itself by Learning from a large set of examples that demonstrate the desired behavior. Applications of deep Learning Technology to cancer imaging can assist pathologists in the detection and classification of cancer in the early stages of its development to allow patients to have appropriate treatments that can increase survival or recovery of patients. Statistical analyses and other analytical approaches, based on data of ScienceDirect (a source for scientific research), suggest that, since the late 1990s, the sharp increase of the studies of deep Learning Technology in cancer imaging seems to be driven by high rates of mortality of some types of cancer (e.g., lung and breast) in order to solve consequential problems of a more accurate detection and characterization of cancer types to apply efficient anti-cancer therapies. Moreover, this study also shows sources of the trajectories of deep Learning Technology in cancer imaging at level of scientific subject areas, universities and countries with the highest scientific production in these research fields. This new Technology, in accordance with Amara’s law, can generate a shift of technological paradigm for diagnostic assessment of any cancer type and disease. This new Technology can also generate benefits for poor regions because they can send digital images to labs of other developed regions to have diagnosis of cancer types, reducing as far as possible current gap in healthcare among different regions.

Naofumi Ishitobi - One of the best experts on this subject based on the ideXlab platform.

  • accuracy of deep Learning a machine Learning Technology using ultra wide field fundus ophthalmoscopy for detecting rhegmatogenous retinal detachment
    Scientific Reports, 2017
    Co-Authors: Hideharu Ohsugi, Hitoshi Tabuchi, Hiroki Enno, Naofumi Ishitobi
    Abstract:

    Rhegmatogenous retinal detachment (RRD) is a serious condition that can lead to blindness; however, it is highly treatable with timely and appropriate treatment. Thus, early diagnosis and treatment of RRD is crucial. In this study, we applied deep Learning, a machine-Learning Technology, to detect RRD using ultra-wide-field fundus images and investigated its performance. In total, 411 images (329 for training and 82 for grading) from 407 RRD patients and 420 images (336 for training and 84 for grading) from 238 non-RRD patients were used in this study. The deep Learning model demonstrated a high sensitivity of 97.6% [95% confidence interval (CI), 94.2-100%] and a high specificity of 96.5% (95% CI, 90.2-100%), and the area under the curve was 0.988 (95% CI, 0.981-0.995). This model can improve medical care in remote areas where eye clinics are not available by using ultra-wide-field fundus ophthalmoscopy for the accurate diagnosis of RRD. Early diagnosis of RRD can prevent blindness.

Hideharu Ohsugi - One of the best experts on this subject based on the ideXlab platform.

  • Accuracy of deep Learning, a machine Learning Technology, using ultra-wide-field fundus ophthalmoscopy for detecting idiopathic macular holes
    PeerJ Inc., 2018
    Co-Authors: Toshihiko Nagasawa, Hitoshi Tabuchi, Hideharu Ohsugi, Hiroki Enno, Hiroki Masumoto, Masanori Niki, Yoshinori Mitamura
    Abstract:

    We aimed to investigate the detection of idiopathic macular holes (MHs) using ultra-wide-field fundus images (Optos) with deep Learning, which is a machine Learning Technology. The study included 910 Optos color images (715 normal images, 195 MH images). Of these 910 images, 637 were Learning images (501 normal images, 136 MH images) and 273 were test images (214 normal images and 59 MH images). We conducted training with a deep convolutional neural network (CNN) using the images and constructed a deep-Learning model. The CNN exhibited high sensitivity of 100% (95% confidence interval CI [93.5–100%]) and high specificity of 99.5% (95% CI [97.1–99.9%]). The area under the curve was 0.9993 (95% CI [0.9993–0.9994]). Our findings suggest that MHs could be diagnosed using an approach involving wide angle camera images and deep Learning

  • accuracy of deep Learning a machine Learning Technology using ultra wide field fundus ophthalmoscopy for detecting rhegmatogenous retinal detachment
    Scientific Reports, 2017
    Co-Authors: Hideharu Ohsugi, Hitoshi Tabuchi, Hiroki Enno, Naofumi Ishitobi
    Abstract:

    Rhegmatogenous retinal detachment (RRD) is a serious condition that can lead to blindness; however, it is highly treatable with timely and appropriate treatment. Thus, early diagnosis and treatment of RRD is crucial. In this study, we applied deep Learning, a machine-Learning Technology, to detect RRD using ultra-wide-field fundus images and investigated its performance. In total, 411 images (329 for training and 82 for grading) from 407 RRD patients and 420 images (336 for training and 84 for grading) from 238 non-RRD patients were used in this study. The deep Learning model demonstrated a high sensitivity of 97.6% [95% confidence interval (CI), 94.2-100%] and a high specificity of 96.5% (95% CI, 90.2-100%), and the area under the curve was 0.988 (95% CI, 0.981-0.995). This model can improve medical care in remote areas where eye clinics are not available by using ultra-wide-field fundus ophthalmoscopy for the accurate diagnosis of RRD. Early diagnosis of RRD can prevent blindness.

Hiroki Enno - One of the best experts on this subject based on the ideXlab platform.

  • Accuracy of deep Learning, a machine Learning Technology, using ultra-wide-field fundus ophthalmoscopy for detecting idiopathic macular holes
    PeerJ Inc., 2018
    Co-Authors: Toshihiko Nagasawa, Hitoshi Tabuchi, Hideharu Ohsugi, Hiroki Enno, Hiroki Masumoto, Masanori Niki, Yoshinori Mitamura
    Abstract:

    We aimed to investigate the detection of idiopathic macular holes (MHs) using ultra-wide-field fundus images (Optos) with deep Learning, which is a machine Learning Technology. The study included 910 Optos color images (715 normal images, 195 MH images). Of these 910 images, 637 were Learning images (501 normal images, 136 MH images) and 273 were test images (214 normal images and 59 MH images). We conducted training with a deep convolutional neural network (CNN) using the images and constructed a deep-Learning model. The CNN exhibited high sensitivity of 100% (95% confidence interval CI [93.5–100%]) and high specificity of 99.5% (95% CI [97.1–99.9%]). The area under the curve was 0.9993 (95% CI [0.9993–0.9994]). Our findings suggest that MHs could be diagnosed using an approach involving wide angle camera images and deep Learning

  • accuracy of deep Learning a machine Learning Technology using ultra wide field fundus ophthalmoscopy for detecting rhegmatogenous retinal detachment
    Scientific Reports, 2017
    Co-Authors: Hideharu Ohsugi, Hitoshi Tabuchi, Hiroki Enno, Naofumi Ishitobi
    Abstract:

    Rhegmatogenous retinal detachment (RRD) is a serious condition that can lead to blindness; however, it is highly treatable with timely and appropriate treatment. Thus, early diagnosis and treatment of RRD is crucial. In this study, we applied deep Learning, a machine-Learning Technology, to detect RRD using ultra-wide-field fundus images and investigated its performance. In total, 411 images (329 for training and 82 for grading) from 407 RRD patients and 420 images (336 for training and 84 for grading) from 238 non-RRD patients were used in this study. The deep Learning model demonstrated a high sensitivity of 97.6% [95% confidence interval (CI), 94.2-100%] and a high specificity of 96.5% (95% CI, 90.2-100%), and the area under the curve was 0.988 (95% CI, 0.981-0.995). This model can improve medical care in remote areas where eye clinics are not available by using ultra-wide-field fundus ophthalmoscopy for the accurate diagnosis of RRD. Early diagnosis of RRD can prevent blindness.

Hitoshi Tabuchi - One of the best experts on this subject based on the ideXlab platform.

  • Accuracy of deep Learning, a machine Learning Technology, using ultra-wide-field fundus ophthalmoscopy for detecting idiopathic macular holes
    PeerJ Inc., 2018
    Co-Authors: Toshihiko Nagasawa, Hitoshi Tabuchi, Hideharu Ohsugi, Hiroki Enno, Hiroki Masumoto, Masanori Niki, Yoshinori Mitamura
    Abstract:

    We aimed to investigate the detection of idiopathic macular holes (MHs) using ultra-wide-field fundus images (Optos) with deep Learning, which is a machine Learning Technology. The study included 910 Optos color images (715 normal images, 195 MH images). Of these 910 images, 637 were Learning images (501 normal images, 136 MH images) and 273 were test images (214 normal images and 59 MH images). We conducted training with a deep convolutional neural network (CNN) using the images and constructed a deep-Learning model. The CNN exhibited high sensitivity of 100% (95% confidence interval CI [93.5–100%]) and high specificity of 99.5% (95% CI [97.1–99.9%]). The area under the curve was 0.9993 (95% CI [0.9993–0.9994]). Our findings suggest that MHs could be diagnosed using an approach involving wide angle camera images and deep Learning

  • accuracy of deep Learning a machine Learning Technology using ultra wide field fundus ophthalmoscopy for detecting rhegmatogenous retinal detachment
    Scientific Reports, 2017
    Co-Authors: Hideharu Ohsugi, Hitoshi Tabuchi, Hiroki Enno, Naofumi Ishitobi
    Abstract:

    Rhegmatogenous retinal detachment (RRD) is a serious condition that can lead to blindness; however, it is highly treatable with timely and appropriate treatment. Thus, early diagnosis and treatment of RRD is crucial. In this study, we applied deep Learning, a machine-Learning Technology, to detect RRD using ultra-wide-field fundus images and investigated its performance. In total, 411 images (329 for training and 82 for grading) from 407 RRD patients and 420 images (336 for training and 84 for grading) from 238 non-RRD patients were used in this study. The deep Learning model demonstrated a high sensitivity of 97.6% [95% confidence interval (CI), 94.2-100%] and a high specificity of 96.5% (95% CI, 90.2-100%), and the area under the curve was 0.988 (95% CI, 0.981-0.995). This model can improve medical care in remote areas where eye clinics are not available by using ultra-wide-field fundus ophthalmoscopy for the accurate diagnosis of RRD. Early diagnosis of RRD can prevent blindness.