Artificial Intelligence (AI) Assisted CT/MRI Image Fusion Technique in Preoperative Evaluation of a Pelvic Bone Osteosarcoma

Du, Xuehui; Hua, Wei; Li, Po; Yao, Weitao

doi:10.3389/fonc.2020.01209

Cited by 12 publications

(15 citation statements)

References 17 publications

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“…This increases the difficulty of diagnosis in children with osteosarcoma. In [ 22 ], image fusion technique based on advanced AI classifiers is proposed to diagnose osteosarcoma using the data of both CT and MRI images.…”

Section: Related Workmentioning

confidence: 99%

Clinical Application of Artificial Intelligence: Auto-Discerning the Effectiveness of Lidocaine Concentration Levels in Osteosarcoma Femoral Tumor Segment Resection

2022

Journal of Healthcare Engineering

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Adolescents and children worldwide are threatened by osteosarcoma, a tumor that predominantly affects the long bone epiphysis. Osteosarcoma is the most common and highly malignant bone tumor in youngsters. Early tumor detection is the key to effective treatment of this disease. The discovery of biomarkers and the growing understanding of molecules and their complex interactions have improved the outcome of clinical trials in osteosarcoma. This article describes biomarkers of osteosarcoma with the aim of positively influencing the progress of clinical treatment of osteosarcoma. Femoral bone tumor is a typical condition of osteosarcoma. Due to the wide range of femoral stem types, complexities in the distal femur, and tumors in the rotor part of femur, physicians following the traditional clinical approach face difficulties in removing the lesion and fixing the femur with resection of the tumor segment. In this paper, the effect of small doses of different concentrations of lidocaine anesthesia in patients undergoing lumpectomy for osteosarcoma femoral tumor segments is investigated. A computer-based artificial intelligence method for automated determination of different concentration levels of lidocaine anesthesia and amputation of osteosarcoma femoral tumor segment is proposed. Statistical analysis is carried on the empirical data including intraoperative bleeding, intraoperative and postoperative pain scores, surgical operation time, postoperative complications, patient satisfaction, and local anesthetic dose. The results showed that the patients in the study group had low intraoperative bleeding, short operation time, low postoperative hematoma formation rate, high patient satisfaction, higher dosage of anesthetic solution, and low dosage of lidocaine. Results revealed that mean arterial pressure and heart rate in extubating and intubating were significantly lower in the observation group than in the control group, and a significant difference ( P < 0.05 ) was observed between the two groups. This proves that the proposed algorithm can adequately reduce bleeding, alleviate postoperative pain, shorten operation time, reduce complications, accelerate recovery, and ensure better treatment results.

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Section: Related Workmentioning

confidence: 99%

Clinical Application of Artificial Intelligence: Auto-Discerning the Effectiveness of Lidocaine Concentration Levels in Osteosarcoma Femoral Tumor Segment Resection

2022

Journal of Healthcare Engineering

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“…For all the cases included in this study, enhanced CT and MRI data were obtained to construct the 3D fusion image model as previously reported 23 . The slice thickness of MRI was less than 2 mm, normally 1 mm based on the machine or scan methods used.…”

Section: Methodsmentioning

confidence: 99%

“…Pilot attempts have been utilized in other organs using CT and MRI fusion techniques in guiding clinical practice 20–22 . We previously reported a successful case using the CT/MRI fusion 3D model to evaluate and plan the surgery of a pelvic osteosarcoma patient 23 . No study has been reported to compare the accuracy of the CT/MRI fusion model with CT only model.…”

Section: Introductionmentioning

confidence: 99%

Experience in utilizing a novel 3D digital model with CT and MRI fusion data in sarcoma evaluation and surgical planning

Hua

Zhang

et al. 2022

Journal of Surgical Oncology

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Objective To assess sarcoma margins with more accuracy and aid surgical planning, we constructed three‐dimensional (3D) digital models with computed tomography(CT) and magnetic resonance imaging (MRI) image fusion data and validated the preciseness of the models by comparing them with 3D models constructed with CT only data. Materials and Methods We retrospectively reviewed a consecutive set of patients treated in our center who were preoperatively evaluated with the fusion image model. Models based on fusion images or CT‐only data were constructed. Volumes of both tumors were calculated and the tumors were overlapped to see the location of differences between the two models. Results A consecutive 12 cases (4 male vs. 8 female) were included in this study. Most of the tumors were located in the pelvic bone or spine. The volume of the two tumor models was different and the differences were mainly in the peripheral region of the tumor. Conclusion CT and MRI fusion image 3D models are more accurate than models with CT‐only data and can be very helpful in preoperative planning of sarcoma patients.

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“…Recent works for medical imaging fusion using deeplearning techniques have been introduced in [9], [11], [13], [23], [31], [39], [41], [69], [85]- [88].…”

Section: Introductionmentioning

confidence: 99%

A Comprehensive Survey Analysis for Present Solutions of Medical Image Fusion and Future Directions

et al. 2021

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The track of medical imaging has witnessed several advancements in the last years. Several medical imaging modalities have appeared in the last decades including X-ray, Computed Tomography (CT), Magnetic Resonance (MR), Positron Emission Tomography (PET), Single-Photon Emission Computed Tomography (SPECT) and ultrasound imaging. Generally, medical images are used for the diagnosis purpose. Each type of acquired images has some merits and limitations. To maximize medical images utilization for the purpose of diagnosis, medical imaging fusion trend has appeared as a hot research field. Different medical imaging modalities are fused to obtain new images with complementary information. This paper presents a survey study of medical imaging modalities and their characteristics. In addition, different medical image fusion approaches and their appropriate quality metrics are presented. The main aim of this comprehensive survey analysis is to contribute in the advancement of medical image approaches that can help for better diagnosis of different diseases.INDEX TERMS Medical imaging, fusion process, imaging modalities, dual-tree complex wavelet transform, curvelet transform, discrete wavelet transform, principal component analysis.

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Artificial Intelligence (AI) Assisted CT/MRI Image Fusion Technique in Preoperative Evaluation of a Pelvic Bone Osteosarcoma

Cited by 12 publications

References 17 publications

Clinical Application of Artificial Intelligence: Auto-Discerning the Effectiveness of Lidocaine Concentration Levels in Osteosarcoma Femoral Tumor Segment Resection

Clinical Application of Artificial Intelligence: Auto-Discerning the Effectiveness of Lidocaine Concentration Levels in Osteosarcoma Femoral Tumor Segment Resection

Experience in utilizing a novel 3D digital model with CT and MRI fusion data in sarcoma evaluation and surgical planning

A Comprehensive Survey Analysis for Present Solutions of Medical Image Fusion and Future Directions

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