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Alzubaidi L, Al-Dulaimi K, Salhi A, Alammar Z, Fadhel MA, Albahri AS, Alamoodi AH, Albahri OS, Hasan AF, Bai J, Gilliland L, Peng J, Branni M, Shuker T, Cutbush K, Santamaría J, Moreira C, Ouyang C, Duan Y, Manoufali M, Jomaa M, Gupta A, Abbosh A, Gu Y. Comprehensive review of deep learning in orthopaedics: Applications, challenges, trustworthiness, and fusion. Artif Intell Med 2024; 155:102935. [PMID: 39079201 DOI: 10.1016/j.artmed.2024.102935] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/01/2023] [Revised: 03/18/2024] [Accepted: 07/22/2024] [Indexed: 08/24/2024]
Abstract
Deep learning (DL) in orthopaedics has gained significant attention in recent years. Previous studies have shown that DL can be applied to a wide variety of orthopaedic tasks, including fracture detection, bone tumour diagnosis, implant recognition, and evaluation of osteoarthritis severity. The utilisation of DL is expected to increase, owing to its ability to present accurate diagnoses more efficiently than traditional methods in many scenarios. This reduces the time and cost of diagnosis for patients and orthopaedic surgeons. To our knowledge, no exclusive study has comprehensively reviewed all aspects of DL currently used in orthopaedic practice. This review addresses this knowledge gap using articles from Science Direct, Scopus, IEEE Xplore, and Web of Science between 2017 and 2023. The authors begin with the motivation for using DL in orthopaedics, including its ability to enhance diagnosis and treatment planning. The review then covers various applications of DL in orthopaedics, including fracture detection, detection of supraspinatus tears using MRI, osteoarthritis, prediction of types of arthroplasty implants, bone age assessment, and detection of joint-specific soft tissue disease. We also examine the challenges for implementing DL in orthopaedics, including the scarcity of data to train DL and the lack of interpretability, as well as possible solutions to these common pitfalls. Our work highlights the requirements to achieve trustworthiness in the outcomes generated by DL, including the need for accuracy, explainability, and fairness in the DL models. We pay particular attention to fusion techniques as one of the ways to increase trustworthiness, which have also been used to address the common multimodality in orthopaedics. Finally, we have reviewed the approval requirements set forth by the US Food and Drug Administration to enable the use of DL applications. As such, we aim to have this review function as a guide for researchers to develop a reliable DL application for orthopaedic tasks from scratch for use in the market.
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Affiliation(s)
- Laith Alzubaidi
- School of Mechanical, Medical, and Process Engineering, Queensland University of Technology, Brisbane, QLD 4000, Australia; QUASR/ARC Industrial Transformation Training Centre-Joint Biomechanics, Queensland University of Technology, Brisbane, QLD 4000, Australia; Research and Development department, Akunah Med Technology Pty Ltd Co, Brisbane, QLD 4120, Australia.
| | - Khamael Al-Dulaimi
- Computer Science Department, College of Science, Al-Nahrain University, Baghdad, Baghdad 10011, Iraq; School of Electrical Engineering and Robotics, Queensland University of Technology, Brisbane, QLD 4000, Australia
| | - Asma Salhi
- QUASR/ARC Industrial Transformation Training Centre-Joint Biomechanics, Queensland University of Technology, Brisbane, QLD 4000, Australia; Research and Development department, Akunah Med Technology Pty Ltd Co, Brisbane, QLD 4120, Australia
| | - Zaenab Alammar
- School of Computer Science, Queensland University of Technology, Brisbane, QLD 4000, Australia
| | - Mohammed A Fadhel
- Research and Development department, Akunah Med Technology Pty Ltd Co, Brisbane, QLD 4120, Australia
| | - A S Albahri
- Technical College, Imam Ja'afar Al-Sadiq University, Baghdad, Iraq
| | - A H Alamoodi
- Institute of Informatics and Computing in Energy, Universiti Tenaga Nasional, Kajang 43000, Malaysia
| | - O S Albahri
- Australian Technical and Management College, Melbourne, Australia
| | - Amjad F Hasan
- Faculty of Electrical Engineering and Computer Science, University of Missouri, Columbia, MO 65211, USA
| | - Jinshuai Bai
- School of Mechanical, Medical, and Process Engineering, Queensland University of Technology, Brisbane, QLD 4000, Australia; QUASR/ARC Industrial Transformation Training Centre-Joint Biomechanics, Queensland University of Technology, Brisbane, QLD 4000, Australia
| | - Luke Gilliland
- QUASR/ARC Industrial Transformation Training Centre-Joint Biomechanics, Queensland University of Technology, Brisbane, QLD 4000, Australia; Research and Development department, Akunah Med Technology Pty Ltd Co, Brisbane, QLD 4120, Australia
| | - Jing Peng
- Research and Development department, Akunah Med Technology Pty Ltd Co, Brisbane, QLD 4120, Australia
| | - Marco Branni
- QUASR/ARC Industrial Transformation Training Centre-Joint Biomechanics, Queensland University of Technology, Brisbane, QLD 4000, Australia; Research and Development department, Akunah Med Technology Pty Ltd Co, Brisbane, QLD 4120, Australia
| | - Tristan Shuker
- QUASR/ARC Industrial Transformation Training Centre-Joint Biomechanics, Queensland University of Technology, Brisbane, QLD 4000, Australia; St Andrew's War Memorial Hospital, Brisbane, QLD 4000, Australia
| | - Kenneth Cutbush
- QUASR/ARC Industrial Transformation Training Centre-Joint Biomechanics, Queensland University of Technology, Brisbane, QLD 4000, Australia; St Andrew's War Memorial Hospital, Brisbane, QLD 4000, Australia
| | - Jose Santamaría
- Department of Computer Science, University of Jaén, Jaén 23071, Spain
| | - Catarina Moreira
- Data Science Institute, University of Technology Sydney, Australia
| | - Chun Ouyang
- School of Information Systems, Queensland University of Technology, Brisbane, QLD 4000, Australia
| | - Ye Duan
- School of Computing, Clemson University, Clemson, 29631, SC, USA
| | - Mohamed Manoufali
- CSIRO, Kensington, WA 6151, Australia; School of Information Technology and Electrical Engineering, The University of Queensland, Brisbane, QLD 4067, Australia
| | - Mohammad Jomaa
- QUASR/ARC Industrial Transformation Training Centre-Joint Biomechanics, Queensland University of Technology, Brisbane, QLD 4000, Australia; St Andrew's War Memorial Hospital, Brisbane, QLD 4000, Australia
| | - Ashish Gupta
- School of Mechanical, Medical, and Process Engineering, Queensland University of Technology, Brisbane, QLD 4000, Australia; QUASR/ARC Industrial Transformation Training Centre-Joint Biomechanics, Queensland University of Technology, Brisbane, QLD 4000, Australia; Research and Development department, Akunah Med Technology Pty Ltd Co, Brisbane, QLD 4120, Australia
| | - Amin Abbosh
- School of Information Technology and Electrical Engineering, The University of Queensland, Brisbane, QLD 4067, Australia
| | - Yuantong Gu
- School of Mechanical, Medical, and Process Engineering, Queensland University of Technology, Brisbane, QLD 4000, Australia; QUASR/ARC Industrial Transformation Training Centre-Joint Biomechanics, Queensland University of Technology, Brisbane, QLD 4000, Australia
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Franco PN, Maino C, Mariani I, Gandola DG, Sala D, Bologna M, Talei Franzesi C, Corso R, Ippolito D. Diagnostic performance of an AI algorithm for the detection of appendicular bone fractures in pediatric patients. Eur J Radiol 2024; 178:111637. [PMID: 39053306 DOI: 10.1016/j.ejrad.2024.111637] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/30/2024] [Revised: 07/15/2024] [Accepted: 07/16/2024] [Indexed: 07/27/2024]
Abstract
PURPOSE To evaluate the diagnostic performance of an Artificial Intelligence (AI) algorithm, previously trained using both adult and pediatric patients, for the detection of acute appendicular fractures in the pediatric population on conventional X-ray radiography (CXR). MATERIALS AND METHODS In this retrospective study, anonymized extremities CXRs of pediatric patients (age <17 years), with or without fractures, were included. Six hundred CXRs (maintaining the positive-for-fracture and negative-for-fracture balance) were included, grouping them per body part (shoulder/clavicle, elbow/upper arm, hand/wrist, leg/knee, foot/ankle). Follow-up CXRs and/or second-level imaging were considered as reference standard. A deep learning algorithm interpreted CXRs for fracture detection on a per-patient, per-radiograph, and per-location level, and its diagnostic performance values were compared with the reference standard. AI diagnostic performance was computed by using cross-tables, and 95 % confidence intervals [CIs] were obtained by bootstrapping. RESULTS The final cohort included 312 male and 288 female with a mean age of 8.9±4.5 years. Three undred CXRs (50 %) were positive for fractures, according to the reference standard. For all fractures, the AI tool showed a per-patient 91.3% (95%CIs = 87.6-94.3) sensitivity, 76.7% (71.5-81.3) specificity, and 84% (82.1-86.0) accuracy. In the per-radiograph analysis the AI tool showed 85% (81.9-87.8) sensitivity, 88.5% (86.3-90.4) specificity, and 87.2% (85.7-89.6) accuracy. In the per-location analysis, the AI tool identified 606 bounding boxes: 472 (77.9 %) were correct, 110 (18.1 %) incorrect, and 24 (4.0 %) were not-overlapping. CONCLUSION The AI algorithm provides good overall diagnostic performance for detecting appendicular fractures in pediatric patients.
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Affiliation(s)
- Paolo Niccolò Franco
- Department of Diagnostic Radiology, Fondazione IRCCS San Gerardo dei Tintori, Via Pergolesi 33, 20900 Monza, MB, Italy
| | - Cesare Maino
- Department of Diagnostic Radiology, Fondazione IRCCS San Gerardo dei Tintori, Via Pergolesi 33, 20900 Monza, MB, Italy.
| | - Ilaria Mariani
- Department of Diagnostic Radiology, Fondazione IRCCS San Gerardo dei Tintori, Via Pergolesi 33, 20900 Monza, MB, Italy
| | - Davide Giacomo Gandola
- Department of Diagnostic Radiology, Fondazione IRCCS San Gerardo dei Tintori, Via Pergolesi 33, 20900 Monza, MB, Italy
| | - Davide Sala
- Synbrain The Human-Machine Cooperation - AI/ML solutions, Corso Milano 23, 20900 Monza, MB, Italy
| | - Marco Bologna
- Synbrain The Human-Machine Cooperation - AI/ML solutions, Corso Milano 23, 20900 Monza, MB, Italy
| | - Cammillo Talei Franzesi
- Department of Diagnostic Radiology, Fondazione IRCCS San Gerardo dei Tintori, Via Pergolesi 33, 20900 Monza, MB, Italy
| | - Rocco Corso
- Department of Diagnostic Radiology, Fondazione IRCCS San Gerardo dei Tintori, Via Pergolesi 33, 20900 Monza, MB, Italy
| | - Davide Ippolito
- Department of Diagnostic Radiology, Fondazione IRCCS San Gerardo dei Tintori, Via Pergolesi 33, 20900 Monza, MB, Italy; Department of Medicine and Surgery - University of Milano Bicocca, Via Cadore 33, 20090 Monza, MB, Italy
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3
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Liu Y, Gan K, Li J, Sun D, Qiu H, Liu D. [Study on automatic and rapid diagnosis of distal radius fracture by X-ray]. SHENG WU YI XUE GONG CHENG XUE ZA ZHI = JOURNAL OF BIOMEDICAL ENGINEERING = SHENGWU YIXUE GONGCHENGXUE ZAZHI 2024; 41:798-806. [PMID: 39218607 PMCID: PMC11366454 DOI: 10.7507/1001-5515.202309050] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Subscribe] [Scholar Register] [Indexed: 09/04/2024]
Abstract
This article aims to combine deep learning with image analysis technology and propose an effective classification method for distal radius fracture types. Firstly, an extended U-Net three-layer cascaded segmentation network was used to accurately segment the most important joint surface and non joint surface areas for identifying fractures. Then, the images of the joint surface area and non joint surface area separately were classified and trained to distinguish fractures. Finally, based on the classification results of the two images, the normal or ABC fracture classification results could be comprehensively determined. The accuracy rates of normal, A-type, B-type, and C-type fracture on the test set were 0.99, 0.92, 0.91, and 0.82, respectively. For orthopedic medical experts, the average recognition accuracy rates were 0.98, 0.90, 0.87, and 0.81, respectively. The proposed automatic recognition method is generally better than experts, and can be used for preliminary auxiliary diagnosis of distal radius fractures in scenarios without expert participation.
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Affiliation(s)
- Yunpeng Liu
- Information and Computing Science Department, International Exchange College, Ningbo University of Technology, Ningbo, Zhejiang 315000, P. R. China
| | - Kaifeng Gan
- Orthopedics, Lihuili Hospital Affiliated to Ningbo University, Ningbo, Zhejiang 3151000, P. R. China
| | - Jin Li
- Zhejiang Wanli University, Ningbo, Zhejiang 315000, P. R. China
| | - Dechao Sun
- College of Digital Technology and Engineering, Ningbo University of Finance & Economics, Ningbo, Zhejiang 315000, P. R. China
| | - Hong Qiu
- Zhejiang Wanli University, Ningbo, Zhejiang 315000, P. R. China
| | - Dongquan Liu
- Radiology Department, Ninghai First Hospital, Ningbo, Zhejiang 315000, P. R. China
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Tahir A, Saadia A, Khan K, Gul A, Qahmash A, Akram RN. Enhancing diagnosis: ensemble deep-learning model for fracture detection using X-ray images. Clin Radiol 2024:S0009-9260(24)00419-7. [PMID: 39256151 DOI: 10.1016/j.crad.2024.08.006] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/22/2024] [Revised: 08/05/2024] [Accepted: 08/06/2024] [Indexed: 09/12/2024]
Abstract
AIM Orthopedic trauma results in the injury of bone joints and tendons of the body. A radiologist reviews and monitors large numbers of radiographs daily, which can lead to the diagnostic error. Therefore, there is a need to automate the detection of bone fractures in X-ray images, particularly humerus bone fractures. In this paper, we have proposed an ensemble model that can detect the fracture in an x-ray image. MATERIALS AND METHODS In this paper, we proposed an ensemble model designed for fracture detection in X-ray images. An ensemble model combines multiple diverse models to improve predictive accuracy and robustness by aggregating their individual predictions. The model leverages MobileNetV2, Vgg16, InceptionV3, and ResNet50, using histogram equalization for preprocessing and a Global Average Pooling layer for feature extraction. The entire humerus from the public Mura-v1.1 dataset is utilized for analysis, utilizing a single training-validation split. The dataset is divided into a ratio of 80:20 for experiments for the training and validation datasets. RESULTS The proposed model outperformed the modified deep-learning models and achieved 92.96%, 91.62%, and 92.14% accuracy, recall, and F1 scores, respectively. CONCLUSION The ensemble model presented effectively automates bone fracture detection in X-ray images of the humerus, demonstrating superior performance compared to modified deep-learning models. A comparison has been made between a novel ensemble model and state-of-the-art models, bench-marking their performance. These findings underscore its potential for enhancing diagnostic accuracy and efficiency in orthopedic radiology.
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Affiliation(s)
- A Tahir
- Institute of Avionics & Aeronautics, Air University, Islamabad, Pakistan
| | - A Saadia
- Faculty of Computing and Artificial Intelligence, Air University, Islamabad, Pakistan.
| | - K Khan
- Ghulam Ishaq Khan Institute of Engineering Sciences and Technology, Topi, Pakistan
| | - A Gul
- Faculty of Computing, Engineering and Built Environment (CEBE)Birmingham City University, Birmingham, UK
| | - A Qahmash
- King Khalid University, Abha, Saudi Arabia.
| | - R N Akram
- Department of Computer Science, University of Aberdeen, Aberdeen AB24 3FX, UK
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5
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Oettl FC, Pareek A, Winkler PW, Zsidai B, Pruneski JA, Senorski EH, Kopf S, Ley C, Herbst E, Oeding JF, Grassi A, Hirschmann MT, Musahl V, Samuelsson K, Tischer T, Feldt R. A practical guide to the implementation of AI in orthopaedic research, Part 6: How to evaluate the performance of AI research? J Exp Orthop 2024; 11:e12039. [PMID: 38826500 PMCID: PMC11141501 DOI: 10.1002/jeo2.12039] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 02/18/2024] [Revised: 03/13/2024] [Accepted: 04/19/2024] [Indexed: 06/04/2024] Open
Abstract
Artificial intelligence's (AI) accelerating progress demands rigorous evaluation standards to ensure safe, effective integration into healthcare's high-stakes decisions. As AI increasingly enables prediction, analysis and judgement capabilities relevant to medicine, proper evaluation and interpretation are indispensable. Erroneous AI could endanger patients; thus, developing, validating and deploying medical AI demands adhering to strict, transparent standards centred on safety, ethics and responsible oversight. Core considerations include assessing performance on diverse real-world data, collaborating with domain experts, confirming model reliability and limitations, and advancing interpretability. Thoughtful selection of evaluation metrics suited to the clinical context along with testing on diverse data sets representing different populations improves generalisability. Partnering software engineers, data scientists and medical practitioners ground assessment in real needs. Journals must uphold reporting standards matching AI's societal impacts. With rigorous, holistic evaluation frameworks, AI can progress towards expanding healthcare access and quality. Level of Evidence Level V.
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Affiliation(s)
- Felix C. Oettl
- Hospital for Special SurgeryNew YorkNew YorkUSA
- Schulthess KlinikZurichSwitzerland
| | - Ayoosh Pareek
- Sports Medicine and Shoulder Institute, Hospital for Special SurgeryNew YorkNew YorkUSA
| | - Philipp W. Winkler
- Department for Orthopaedics and Traumatology, Kepler University Hospital GmbHJohannes Kepler University LinzLinzAustria
- Department of Orthopaedics, Institute of Clinical Sciences, Sahlgrenska AcademyUniversity of GothenburgGothenburgSweden
- Sahlgrenska Sports Medicine CenterGöteborgSweden
| | - Bálint Zsidai
- Department of Orthopaedics, Institute of Clinical Sciences, Sahlgrenska AcademyUniversity of GothenburgGothenburgSweden
- Sahlgrenska Sports Medicine CenterGöteborgSweden
| | - James A. Pruneski
- Department of Orthopaedic SurgeryTripler Army Medical CenterHonoluluHawaiiUSA
| | - Eric Hamrin Senorski
- Sahlgrenska Sports Medicine CenterGöteborgSweden
- Department of Health and Rehabilitation, Institute of Neuroscience and Physiology, Sahlgrenska AcademyUniversity of GothenburgGothenburgSweden
| | - Sebastian Kopf
- Center of Orthopaedics and Traumatology, University Hospital Brandenburg an der Havel, Brandenburg Medical School Theodor FontaneGermany
| | - Christophe Ley
- Department of MathematicsUniversity of LuxembourgEsch‐sur‐AlzetteLuxembourg
| | - Elmar Herbst
- Department of Trauma, Hand and Reconstructive SurgeryUniversity Hospital MuensterMuensterGermany
| | - Jacob F. Oeding
- Department of Orthopaedics, Institute of Clinical Sciences, Sahlgrenska AcademyUniversity of GothenburgGothenburgSweden
- Mayo Clinic Alix School of Medicine, Mayo ClinicRochesterMinnesotaUSA
| | - Alberto Grassi
- IIa Clinica Ortopedica e Traumatologica, IRCCS Istituto Ortopedico RizzoliBolognaItaly
| | - Michael T. Hirschmann
- Department of Orthopaedic Surgery and TraumatologyKantonsspital BasellandBruderholzSwitzerland
- University of BaselBaselSwitzerland
| | - Volker Musahl
- Department of Orthopaedic Surgery, UPMC Freddie Fu Sports Medicine CenterUniversity of PittsburghPittsburghPennsylvaniaUSA
| | - Kristian Samuelsson
- Department of Orthopaedics, Institute of Clinical Sciences, Sahlgrenska AcademyUniversity of GothenburgGothenburgSweden
- Sahlgrenska Sports Medicine CenterGöteborgSweden
- Department of OrthopaedicsSahlgrenska University HospitalMölndalSweden
| | - Thomas Tischer
- Department of Orthopaedic SurgeryUniversitymedicine RostockRostockGermany
- Department of Orthopaedic and Trauma SurgeryMalteser Waldkrankenhaus ErlangenErlangenGermany
| | - Robert Feldt
- Department of Computer Science and EngineeringChalmers University of TechnologyGothenburgSweden
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Usuff R, Kothandapani S, Rangan R, Dhatchnamurthy S. Enhancing radiographic image interpretation: WARES-PRS model for knee bone tumour detection. NETWORK (BRISTOL, ENGLAND) 2024:1-31. [PMID: 38932464 DOI: 10.1080/0954898x.2024.2357660] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 03/05/2024] [Accepted: 05/15/2024] [Indexed: 06/28/2024]
Abstract
The early diagnosis of tumour is significant in biomedical research field to lower the severity level and restrict the process extension from cancer. Moreover, the detection of early sign of cancer is undertaken with extensive research efforts that dedicated to the disclosure and recognition of tumours. However, the limited data size as well as diverse appearance of images lowered the detection performance and failed to detect complex stage of tumour. So to solve these issues, a Weighted Adaptive Random Ensemble Support Vector-based Partial Reinforcement Search (WARES-PRS) algorithm is proposed that detected bone lesions accurately and also predicted the severity level stage efficiently. Further, the detection is performed with varied stages to diminish the presence of noise and undertaken effective classification. The performance is validated with CNUH dataset that enhanced image pre-processing tasks. Despite the proposed method uncover the mutual relationships between each pixel's local texture and the overall image's global context. The detection and classification efficiency is validated with various measures and the experimental results revealed that the detection accuracy is enhanced for the proposed approach by 98.5%. The outcomes of our study have exhibited a substantial contribution to assisting physicians in the detection of knee bone tumours.
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Affiliation(s)
- Rahamathunnisa Usuff
- School of Computer Science Engineering and Information Systems, Vellore Institute of Technology, Vellore, Tamil Nadu, India
| | - Sudhakar Kothandapani
- Department of Computer Science & Engineering, Madanapalle Institute of Technology & Science, Madanapalle, Andhra Pradesh, India
| | - Rajesh Rangan
- Department of Computer Science and Engineering, School of Computing, Vel Tech Rangarajan Dr.Sagunthala R&D Institute of Science and Technology, Avadi, Chennai, India
| | - Saravanan Dhatchnamurthy
- Department of Computer Science and Engineering, School of Computing, Sathyabama Institute of Science and Technology, Chennai, India
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7
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Xu C, Liu X, Bao B, Liu C, Li R, Yang T, Wu Y, Zhang Y, Tang J. Two-Stage Deep Learning Model for Diagnosis of Lumbar Spondylolisthesis Based on Lateral X-Ray Images. World Neurosurg 2024; 186:e652-e661. [PMID: 38608811 DOI: 10.1016/j.wneu.2024.04.025] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/03/2024] [Accepted: 04/04/2024] [Indexed: 04/14/2024]
Abstract
BACKGROUND Diagnosing early lumbar spondylolisthesis is challenging for many doctors because of the lack of obvious symptoms. Using deep learning (DL) models to improve the accuracy of X-ray diagnoses can effectively reduce missed and misdiagnoses in clinical practice. This study aimed to use a two-stage deep learning model, the Res-SE-Net model with the YOLOv8 algorithm, to facilitate efficient and reliable diagnosis of early lumbar spondylolisthesis based on lateral X-ray image identification. METHODS A total of 2424 lumbar lateral radiographs of patients treated in the Beijing Tongren Hospital between January 2021 and September 2023 were obtained. The data were labeled and mutually identified by 3 orthopedic surgeons after reshuffling in a random order and divided into a training set, validation set, and test set in a ratio of 7:2:1. We trained 2 models for automatic detection of spondylolisthesis. YOLOv8 model was used to detect the position of lumbar spondylolisthesis, and the Res-SE-Net classification method was designed to classify the clipped area and determine whether it was lumbar spondylolisthesis. The model performance was evaluated using a test set and an external dataset from Beijing Haidian Hospital. Finally, we compared model validation results with professional clinicians' evaluation. RESULTS The model achieved promising results, with a high diagnostic accuracy of 92.3%, precision of 93.5%, and recall of 93.1% for spondylolisthesis detection on the test set, the area under the curve (AUC) value was 0.934. CONCLUSIONS Our two-stage deep learning model provides doctors with a reference basis for the better diagnosis and treatment of early lumbar spondylolisthesis.
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Affiliation(s)
- Chunyang Xu
- Department of orthopedics, Beijing Tongren Hospital, Capital Medical University, Beijing, China
| | - Xingyu Liu
- School of Life Sciences, Tsinghua University, Beijing, China; Institute of Biomedical and Health Engineering (iBHE), Tsinghua Shenzhen International Graduate School, Shenzhen, China; Department of Biomedical Engineering, School of Medicine, Tsinghua University, Beijing, China; Longwood Valley Medical Technology Co Ltd, Beijing, China
| | - Beixi Bao
- Department of orthopedics, Beijing Tongren Hospital, Capital Medical University, Beijing, China
| | - Chang Liu
- Department of Minimally Invasive Spine Surgery, Beijing Haidian Hospital, Peking University, China
| | - Runchao Li
- Longwood Valley Medical Technology Co Ltd, Beijing, China
| | - Tianci Yang
- Department of orthopedics, Beijing Tongren Hospital, Capital Medical University, Beijing, China
| | - Yukan Wu
- Department of orthopedics, Beijing Tongren Hospital, Capital Medical University, Beijing, China
| | - Yiling Zhang
- Department of Biomedical Engineering, School of Medicine, Tsinghua University, Beijing, China; Longwood Valley Medical Technology Co Ltd, Beijing, China
| | - Jiaguang Tang
- Department of orthopedics, Beijing Tongren Hospital, Capital Medical University, Beijing, China.
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8
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Kuhn S, Knitza J. [Orthopedics and trauma surgery in the digital age]. ORTHOPADIE (HEIDELBERG, GERMANY) 2024; 53:327-335. [PMID: 38538858 DOI: 10.1007/s00132-024-04496-5] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Accepted: 03/06/2024] [Indexed: 05/01/2024]
Abstract
BACKGROUND Digital transformation is shaping the future of orthopedics and trauma surgery. Telemedicine, digital health applications, electronic patient records and artificial intelligence play a central role in this. These technologies have the potential to improve medical care, enable individualized patient treatment plans and reduce the burden on the treatment process. However, there are currently challenges in the areas of infrastructure, regulation, reimbursement and data protection. REALISING THE TRANSFORMATION Effective transformation requires a deep understanding of both technology and clinical practice. Orthopedic and trauma surgeons need to take a leadership role by actively engaging with new technologies, designing new treatment processes and enhancing their medical skills with digital and AI competencies. The integration of digital skills into medical education and specialist training will be crucial for actively shaping the digital transformation and exploiting its full potential.
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Affiliation(s)
- Sebastian Kuhn
- Institut für Digitale Medizin, Philipps Universität Marburg und Universitätsklinikum Gießen und Marburg, 35042, Marburg, Deutschland.
| | - Johannes Knitza
- Institut für Digitale Medizin, Philipps Universität Marburg und Universitätsklinikum Gießen und Marburg, 35042, Marburg, Deutschland
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9
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Youssef Y, De Wet D, Back DA, Scherer J. Digitalization in orthopaedics: a narrative review. Front Surg 2024; 10:1325423. [PMID: 38274350 PMCID: PMC10808497 DOI: 10.3389/fsurg.2023.1325423] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/21/2023] [Accepted: 12/27/2023] [Indexed: 01/27/2024] Open
Abstract
Advances in technology and digital tools like the Internet of Things (IoT), artificial intelligence (AI), and sensors are shaping the field of orthopaedic surgery on all levels, from patient care to research and facilitation of logistic processes. Especially the COVID-19 pandemic, with the associated contact restrictions was an accelerator for the development and introduction of telemedical applications and digital alternatives to classical in-person patient care. Digital applications already used in orthopaedic surgery include telemedical support, online video consultations, monitoring of patients using wearables, smart devices, surgical navigation, robotic-assisted surgery, and applications of artificial intelligence in forms of medical image processing, three-dimensional (3D)-modelling, and simulations. In addition to that immersive technologies like virtual, augmented, and mixed reality are increasingly used in training but also rehabilitative and surgical settings. Digital advances can therefore increase the accessibility, efficiency and capabilities of orthopaedic services and facilitate more data-driven, personalized patient care, strengthening the self-responsibility of patients and supporting interdisciplinary healthcare providers to offer for the optimal care for their patients.
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Affiliation(s)
- Yasmin Youssef
- Department of Orthopaedics, Trauma and Plastic Surgery, University Hospital of Leipzig, Leipzig, Germany
| | - Deana De Wet
- Orthopaedic Research Unit, University of Cape Town, Cape Town, South Africa
| | - David A. Back
- Center for Musculoskeletal Surgery, Charité University Medicine Berlin, Berlin, Germany
| | - Julian Scherer
- Orthopaedic Research Unit, University of Cape Town, Cape Town, South Africa
- Department of Traumatology, University Hospital of Zurich, Zurich, Switzerland
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Pham TD, Holmes SB, Coulthard P. A review on artificial intelligence for the diagnosis of fractures in facial trauma imaging. Front Artif Intell 2024; 6:1278529. [PMID: 38249794 PMCID: PMC10797131 DOI: 10.3389/frai.2023.1278529] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/16/2023] [Accepted: 12/11/2023] [Indexed: 01/23/2024] Open
Abstract
Patients with facial trauma may suffer from injuries such as broken bones, bleeding, swelling, bruising, lacerations, burns, and deformity in the face. Common causes of facial-bone fractures are the results of road accidents, violence, and sports injuries. Surgery is needed if the trauma patient would be deprived of normal functioning or subject to facial deformity based on findings from radiology. Although the image reading by radiologists is useful for evaluating suspected facial fractures, there are certain challenges in human-based diagnostics. Artificial intelligence (AI) is making a quantum leap in radiology, producing significant improvements of reports and workflows. Here, an updated literature review is presented on the impact of AI in facial trauma with a special reference to fracture detection in radiology. The purpose is to gain insights into the current development and demand for future research in facial trauma. This review also discusses limitations to be overcome and current important issues for investigation in order to make AI applications to the trauma more effective and realistic in practical settings. The publications selected for review were based on their clinical significance, journal metrics, and journal indexing.
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Affiliation(s)
- Tuan D. Pham
- Barts and The London School of Medicine and Dentistry, Queen Mary University of London, London, United Kingdom
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11
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Jeon YD, Kang MJ, Kuh SU, Cha HY, Kim MS, You JY, Kim HJ, Shin SH, Chung YG, Yoon DK. Deep Learning Model Based on You Only Look Once Algorithm for Detection and Visualization of Fracture Areas in Three-Dimensional Skeletal Images. Diagnostics (Basel) 2023; 14:11. [PMID: 38201320 PMCID: PMC10802847 DOI: 10.3390/diagnostics14010011] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/02/2023] [Revised: 11/30/2023] [Accepted: 12/10/2023] [Indexed: 01/12/2024] Open
Abstract
Utilizing "You only look once" (YOLO) v4 AI offers valuable support in fracture detection and diagnostic decision-making. The purpose of this study was to help doctors to detect and diagnose fractures more accurately and intuitively, with fewer errors. The data accepted into the backbone are diversified through CSPDarkNet-53. Feature maps are extracted using Spatial Pyramid Pooling and a Path Aggregation Network in the neck part. The head part aggregates and generates the final output. All bounding boxes by the YOLO v4 are mapped onto the 3D reconstructed bone images after being resized to match the same region as shown in the 2D CT images. The YOLO v4-based AI model was evaluated through precision-recall (PR) curves and the intersection over union (IoU). Our proposed system facilitated an intuitive display of the fractured area through a distinctive red mask overlaid on the 3D reconstructed bone images. The high average precision values (>0.60) were reported as 0.71 and 0.81 from the PR curves of the tibia and elbow, respectively. The IoU values were calculated as 0.6327 (tibia) and 0.6638 (elbow). When utilized by orthopedic surgeons in real clinical scenarios, this AI-powered 3D diagnosis support system could enable a quick and accurate trauma diagnosis.
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Affiliation(s)
- Young-Dae Jeon
- Department of Orthopedic Surgery, University of Ulsan College of Medicine, Ulsan University Hospital, Ulsan 44033, Republic of Korea
| | - Min-Jun Kang
- Department of Integrative Medicine, College of Medicine, Yonsei University of Korea, Seoul 03722, Republic of Korea;
| | - Sung-Uk Kuh
- Department of Integrative Medicine, College of Medicine, Yonsei University of Korea, Seoul 03722, Republic of Korea;
| | - Ha-Yeong Cha
- Industrial R&D Center, KAVILAB Co., Ltd., Seoul 06675, Republic of Korea; (H.-Y.C.); (M.-S.K.); (J.-Y.Y.); (H.-J.K.)
| | - Moo-Sub Kim
- Industrial R&D Center, KAVILAB Co., Ltd., Seoul 06675, Republic of Korea; (H.-Y.C.); (M.-S.K.); (J.-Y.Y.); (H.-J.K.)
| | - Ju-Yeon You
- Industrial R&D Center, KAVILAB Co., Ltd., Seoul 06675, Republic of Korea; (H.-Y.C.); (M.-S.K.); (J.-Y.Y.); (H.-J.K.)
| | - Hyeon-Joo Kim
- Industrial R&D Center, KAVILAB Co., Ltd., Seoul 06675, Republic of Korea; (H.-Y.C.); (M.-S.K.); (J.-Y.Y.); (H.-J.K.)
| | - Seung-Han Shin
- Department of Orthopedic Surgery, Seoul St. Mary’s Hospital, College of Medicine, The Catholic University of Korea, Seoul 06591, Republic of Korea; (S.-H.S.); (Y.-G.C.)
| | - Yang-Guk Chung
- Department of Orthopedic Surgery, Seoul St. Mary’s Hospital, College of Medicine, The Catholic University of Korea, Seoul 06591, Republic of Korea; (S.-H.S.); (Y.-G.C.)
| | - Do-Kun Yoon
- Industrial R&D Center, KAVILAB Co., Ltd., Seoul 06675, Republic of Korea; (H.-Y.C.); (M.-S.K.); (J.-Y.Y.); (H.-J.K.)
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12
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田 楚, 陈 翔, 朱 桓, 秦 晟, 石 柳, 芮 云. [Application and prospect of machine learning in orthopaedic trauma]. ZHONGGUO XIU FU CHONG JIAN WAI KE ZA ZHI = ZHONGGUO XIUFU CHONGJIAN WAIKE ZAZHI = CHINESE JOURNAL OF REPARATIVE AND RECONSTRUCTIVE SURGERY 2023; 37:1562-1568. [PMID: 38130202 PMCID: PMC10739668 DOI: 10.7507/1002-1892.202308064] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Subscribe] [Scholar Register] [Received: 08/27/2023] [Revised: 10/13/2023] [Accepted: 10/19/2023] [Indexed: 12/23/2023]
Abstract
Objective To review the current applications of machine learning in orthopaedic trauma and anticipate its future role in clinical practice. Methods A comprehensive literature review was conducted to assess the status of machine learning algorithms in orthopaedic trauma research, both nationally and internationally. Results The rapid advancement of computer data processing and the growing convergence of medicine and industry have led to the widespread utilization of artificial intelligence in healthcare. Currently, machine learning plays a significant role in orthopaedic trauma, demonstrating high performance and accuracy in various areas including fracture image recognition, diagnosis stratification, clinical decision-making, evaluation, perioperative considerations, and prognostic risk prediction. Nevertheless, challenges persist in the development and clinical implementation of machine learning. These include limited database samples, model interpretation difficulties, and universality and individualisation variations. Conclusion The expansion of clinical sample sizes and enhancements in algorithm performance hold significant promise for the extensive application of machine learning in supporting orthopaedic trauma diagnosis, guiding decision-making, devising individualized medical strategies, and optimizing the allocation of clinical resources.
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Affiliation(s)
- 楚伟 田
- 东南大学附属中大医院骨科(南京 210009)Department of Orthopedics, Zhongda Hospital Affiliated to Southeast University, Nanjing Jiangsu, 210009, P. R. China
- 东南大学医学院(南京 210009)School of Medicine, Southeast University, Nanjing Jiangsu, 210009, P. R. China
| | - 翔溆 陈
- 东南大学附属中大医院骨科(南京 210009)Department of Orthopedics, Zhongda Hospital Affiliated to Southeast University, Nanjing Jiangsu, 210009, P. R. China
| | - 桓毅 朱
- 东南大学附属中大医院骨科(南京 210009)Department of Orthopedics, Zhongda Hospital Affiliated to Southeast University, Nanjing Jiangsu, 210009, P. R. China
- 东南大学医学院(南京 210009)School of Medicine, Southeast University, Nanjing Jiangsu, 210009, P. R. China
| | - 晟博 秦
- 东南大学附属中大医院骨科(南京 210009)Department of Orthopedics, Zhongda Hospital Affiliated to Southeast University, Nanjing Jiangsu, 210009, P. R. China
| | - 柳 石
- 东南大学附属中大医院骨科(南京 210009)Department of Orthopedics, Zhongda Hospital Affiliated to Southeast University, Nanjing Jiangsu, 210009, P. R. China
- 东南大学医学院(南京 210009)School of Medicine, Southeast University, Nanjing Jiangsu, 210009, P. R. China
- 东南大学附属中大医院创伤救治中心(南京 210009)Trauma Center, Zhongda Hospital Affiliated to Southeast University, Nanjing Jiangsu, 210009, P. R. China
| | - 云峰 芮
- 东南大学附属中大医院骨科(南京 210009)Department of Orthopedics, Zhongda Hospital Affiliated to Southeast University, Nanjing Jiangsu, 210009, P. R. China
- 东南大学医学院(南京 210009)School of Medicine, Southeast University, Nanjing Jiangsu, 210009, P. R. China
- 东南大学附属中大医院创伤救治中心(南京 210009)Trauma Center, Zhongda Hospital Affiliated to Southeast University, Nanjing Jiangsu, 210009, P. R. China
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13
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Li M, Jiang Y, Zhang Y, Zhu H. Medical image analysis using deep learning algorithms. Front Public Health 2023; 11:1273253. [PMID: 38026291 PMCID: PMC10662291 DOI: 10.3389/fpubh.2023.1273253] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/08/2023] [Accepted: 10/05/2023] [Indexed: 12/01/2023] Open
Abstract
In the field of medical image analysis within deep learning (DL), the importance of employing advanced DL techniques cannot be overstated. DL has achieved impressive results in various areas, making it particularly noteworthy for medical image analysis in healthcare. The integration of DL with medical image analysis enables real-time analysis of vast and intricate datasets, yielding insights that significantly enhance healthcare outcomes and operational efficiency in the industry. This extensive review of existing literature conducts a thorough examination of the most recent deep learning (DL) approaches designed to address the difficulties faced in medical healthcare, particularly focusing on the use of deep learning algorithms in medical image analysis. Falling all the investigated papers into five different categories in terms of their techniques, we have assessed them according to some critical parameters. Through a systematic categorization of state-of-the-art DL techniques, such as Convolutional Neural Networks (CNNs), Recurrent Neural Networks (RNNs), Generative Adversarial Networks (GANs), Long Short-term Memory (LSTM) models, and hybrid models, this study explores their underlying principles, advantages, limitations, methodologies, simulation environments, and datasets. Based on our results, Python was the most frequent programming language used for implementing the proposed methods in the investigated papers. Notably, the majority of the scrutinized papers were published in 2021, underscoring the contemporaneous nature of the research. Moreover, this review accentuates the forefront advancements in DL techniques and their practical applications within the realm of medical image analysis, while simultaneously addressing the challenges that hinder the widespread implementation of DL in image analysis within the medical healthcare domains. These discerned insights serve as compelling impetuses for future studies aimed at the progressive advancement of image analysis in medical healthcare research. The evaluation metrics employed across the reviewed articles encompass a broad spectrum of features, encompassing accuracy, sensitivity, specificity, F-score, robustness, computational complexity, and generalizability.
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Affiliation(s)
- Mengfang Li
- The First Affiliated Hospital of Wenzhou Medical University, Wenzhou, China
| | - Yuanyuan Jiang
- Department of Cardiovascular Medicine, The First Affiliated Hospital of Zhengzhou University, Zhengzhou, China
| | - Yanzhou Zhang
- Department of Cardiovascular Medicine, The First Affiliated Hospital of Zhengzhou University, Zhengzhou, China
| | - Haisheng Zhu
- Department of Cardiovascular Medicine, Wencheng People’s Hospital, Wencheng, China
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Diba SF, Sari DCR, Supriatna Y, Ardiyanto I, Bintoro BS. Artificial intelligence in detecting dentomaxillofacial fractures in diagnostic imaging: a scoping review protocol. BMJ Open 2023; 13:e071324. [PMID: 37553193 PMCID: PMC10414106 DOI: 10.1136/bmjopen-2022-071324] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 12/24/2022] [Accepted: 07/18/2023] [Indexed: 08/10/2023] Open
Abstract
INTRODUCTION The dentomaxillofacial (DMF) area, which includes the teeth, maxilla, mandible, zygomaticum, orbits and midface, plays a crucial role in the maintenance of the physiological functions despite its susceptibility to fractures, which are mostly caused by mechanical trauma. As a diagnostic tool, radiographic imaging helps clinicians establish a diagnosis and determine a treatment plan; however, the presence of human factors in image interpretation can result in missed detection of fractures. Therefore, an artificial intelligence (AI) computing system with the potential to help detect abnormalities on radiographic images is currently being developed. This scoping review summarises the literature and assesses the current status of AI in DMF fracture detection in diagnostic imaging. METHODS AND ANALYSIS This proposed scoping review will be conducted using the framework of Arksey and O'Malley, with each step incorporating the recommendations of Levac et al. By using relevant keywords based on the research questions. PubMed, Science Direct, Scopus, Cochrane Library, Springerlink, Institute of Electrical and Electronics Engineers, and ProQuest will be the databases used in this study. The included studies are published in English between 1 January 2000 and 30 June 2023. Two independent reviewers will screen titles and abstracts, followed by full-text screening and data extraction, which will comprise three components: research study characteristics, comparator and AI characteristics. ETHICS AND DISSEMINATION This study does not require ethical approval because it analyses primary research articles. The research findings will be distributed through international conferences and peer-reviewed publications.
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Affiliation(s)
- Silviana Farrah Diba
- Doctorate Program of Medical and Health Science, Gadjah Mada University Faculty of Medicine Public Health and Nursing, Yogyakarta, Indonesia
- Department of Dentomaxillofacial Radiology, Gadjah Mada University Faculty of Dentistry, Yogyakarta, Indonesia
| | - Dwi Cahyani Ratna Sari
- Department of Anatomy, Gadjah Mada University Faculty of Medicine Public Health and Nursing, Yogyakarta, Indonesia
| | - Yana Supriatna
- Department of Radiology, Gadjah Mada University Faculty of Medicine Public Health and Nursing, Yogyakarta, Indonesia
- Radiological Installation, Public Hospital Dr Sardjito, Yogyakarta, Indonesia
| | - Igi Ardiyanto
- Department of Electrical Engineering and Information Technology, Gadjah Mada University Faculty of Engineering, Yogyakarta, Indonesia
| | - Bagas Suryo Bintoro
- Department of Health Behaviour, Environment, and Social Medicine, Gadjah Mada University Faculty of Medicine Public Health and Nursing, Yogyakarta, Indonesia
- Center of Health Behavior and Promotion, Gadjah Mada University Faculty of Medicine Public Health and Nursing, Yogyakarta, Indonesia
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15
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Hussain A, Fareed A, Taseen S. Bone fracture detection-Can artificial intelligence replace doctors in orthopedic radiography analysis? Front Artif Intell 2023; 6:1223909. [PMID: 37593091 PMCID: PMC10427856 DOI: 10.3389/frai.2023.1223909] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/16/2023] [Accepted: 07/20/2023] [Indexed: 08/19/2023] Open
Affiliation(s)
- Aariz Hussain
- Karachi Medical and Dental College and Abbasi Shaheed Hospital, Karachi, Sindh, Pakistan
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16
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Kim T, Moon NH, Goh TS, Jung ID. Detection of incomplete atypical femoral fracture on anteroposterior radiographs via explainable artificial intelligence. Sci Rep 2023; 13:10415. [PMID: 37369833 DOI: 10.1038/s41598-023-37560-9] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/05/2022] [Accepted: 06/23/2023] [Indexed: 06/29/2023] Open
Abstract
One of the key aspects of the diagnosis and treatment of atypical femoral fractures is the early detection of incomplete fractures and the prevention of their progression to complete fractures. However, an incomplete atypical femoral fracture can be misdiagnosed as a normal lesion by both primary care physicians and orthopedic surgeons; expert consultation is needed for accurate diagnosis. To overcome this limitation, we developed a transfer learning-based ensemble model to detect and localize fractures. A total of 1050 radiographs, including 100 incomplete fractures, were preprocessed by applying a Sobel filter. Six models (EfficientNet B5, B6, B7, DenseNet 121, MobileNet V1, and V2) were selected for transfer learning. We then composed two ensemble models; the first was based on the three models having the highest accuracy, and the second was based on the five models having the highest accuracy. The area under the curve (AUC) of the case that used the three most accurate models was the highest at 0.998. This study demonstrates that an ensemble of transfer-learning-based models can accurately classify and detect fractures, even in an imbalanced dataset. This artificial intelligence (AI)-assisted diagnostic application could support decision-making and reduce the workload of clinicians with its high speed and accuracy.
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Affiliation(s)
- Taekyeong Kim
- Department of Mechanical Engineering, Ulsan National Institute of Science and Technology, Ulsan, 44919, Republic of Korea
| | - Nam Hoon Moon
- Department of Orthopaedic Surgery, Biomedical Research Institute, Pusan National University Hospital, Pusan National University School of Medicine, Busan, 49241, Republic of Korea
| | - Tae Sik Goh
- Department of Orthopaedic Surgery, Biomedical Research Institute, Pusan National University Hospital, Pusan National University School of Medicine, Busan, 49241, Republic of Korea
| | - Im Doo Jung
- Department of Mechanical Engineering, Ulsan National Institute of Science and Technology, Ulsan, 44919, Republic of Korea.
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Apivanichkul K, Phasukkit P, Dankulchai P, Sittiwong W, Jitwatcharakomol T. Enhanced Deep-Learning-Based Automatic Left-Femur Segmentation Scheme with Attribute Augmentation. SENSORS (BASEL, SWITZERLAND) 2023; 23:5720. [PMID: 37420884 PMCID: PMC10305208 DOI: 10.3390/s23125720] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 04/25/2023] [Revised: 05/27/2023] [Accepted: 06/14/2023] [Indexed: 07/09/2023]
Abstract
This research proposes augmenting cropped computed tomography (CT) slices with data attributes to enhance the performance of a deep-learning-based automatic left-femur segmentation scheme. The data attribute is the lying position for the left-femur model. In the study, the deep-learning-based automatic left-femur segmentation scheme was trained, validated, and tested using eight categories of CT input datasets for the left femur (F-I-F-VIII). The segmentation performance was assessed by Dice similarity coefficient (DSC) and intersection over union (IoU); and the similarity between the predicted 3D reconstruction images and ground-truth images was determined by spectral angle mapper (SAM) and structural similarity index measure (SSIM). The left-femur segmentation model achieved the highest DSC (88.25%) and IoU (80.85%) under category F-IV (using cropped and augmented CT input datasets with large feature coefficients), with an SAM and SSIM of 0.117-0.215 and 0.701-0.732. The novelty of this research lies in the use of attribute augmentation in medical image preprocessing to enhance the performance of the deep-learning-based automatic left-femur segmentation scheme.
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Affiliation(s)
- Kamonchat Apivanichkul
- School of Engineering, King Mongkut’s Institute of Technology Ladkrabang, Bangkok 10520, Thailand;
| | - Pattarapong Phasukkit
- School of Engineering, King Mongkut’s Institute of Technology Ladkrabang, Bangkok 10520, Thailand;
- King Mongkut Chaokhunthahan Hospital (KMCH), King Mongkut’s Institute of Technology Ladkrabang, Bangkok 10520, Thailand
| | - Pittaya Dankulchai
- Division of Radiation Oncology, Department of Radiology, Faculty of Medicine Siriraj Hospital, Mahidol University, Bangkok 10700, Thailand; (P.D.); (W.S.); (T.J.)
| | - Wiwatchai Sittiwong
- Division of Radiation Oncology, Department of Radiology, Faculty of Medicine Siriraj Hospital, Mahidol University, Bangkok 10700, Thailand; (P.D.); (W.S.); (T.J.)
| | - Tanun Jitwatcharakomol
- Division of Radiation Oncology, Department of Radiology, Faculty of Medicine Siriraj Hospital, Mahidol University, Bangkok 10700, Thailand; (P.D.); (W.S.); (T.J.)
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Anwar T, Anwar H. LSNet: a novel CNN architecture to identify wrist fracture from a small X-ray dataset. INTERNATIONAL JOURNAL OF INFORMATION TECHNOLOGY 2023; 15:2469-2477. [DOI: 10.1007/s41870-023-01311-w] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Received: 12/09/2022] [Accepted: 05/25/2023] [Indexed: 09/01/2023]
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Kim MS, Cho RK, Yang SC, Hur JH, In Y. Machine Learning for Detecting Total Knee Arthroplasty Implant Loosening on Plain Radiographs. Bioengineering (Basel) 2023; 10:632. [PMID: 37370563 DOI: 10.3390/bioengineering10060632] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/04/2023] [Revised: 05/15/2023] [Accepted: 05/22/2023] [Indexed: 06/29/2023] Open
Abstract
(1) Background: The purpose of this study was to investigate whether the loosening of total knee arthroplasty (TKA) implants could be detected accurately on plain radiographs using a deep convolution neural network (CNN). (2) Methods: We analyzed data for 100 patients who underwent revision TKA due to prosthetic loosening at a single institution from 2012 to 2020. We extracted 100 patients who underwent primary TKA without loosening through a propensity score, matching for age, gender, body mass index, operation side, and American Society of Anesthesiologists class. Transfer learning was used to prepare a detection model using a pre-trained Visual Geometry Group (VGG) 19. For transfer learning, two methods were used. First, the fully connected layer was removed, and a new fully connected layer was added to construct a new model. The convolutional layer was frozen without training, and only the fully connected layer was trained (transfer learning model 1). Second, a new model was constructed by adding a fully connected layer and varying the range of freezing for the convolutional layer (transfer learning model 2). (3) Results: The transfer learning model 1 gradually increased in accuracy and ultimately reached 87.5%. After processing through the confusion matrix, the sensitivity was 90% and the specificity was 100%. Transfer learning model 2, which was trained on the convolutional layer, gradually increased in accuracy and ultimately reached 97.5%, which represented a better improvement than for model 1. Processing through the confusion matrix affirmed that the sensitivity was 100% and the specificity was 97.5%. (4) Conclusions: The CNN algorithm, through transfer learning, shows high accuracy for detecting the loosening of TKA implants on plain radiographs.
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Affiliation(s)
- Man-Soo Kim
- Department of Orthopaedic Surgery, Seoul St. Mary's Hospital, College of Medicine, The Catholic University of Korea, 222, Banpo-daero, Seocho-gu, Seoul 06591, Republic of Korea
| | - Ryu-Kyoung Cho
- Department of Orthopaedic Surgery, Seoul St. Mary's Hospital, College of Medicine, The Catholic University of Korea, 222, Banpo-daero, Seocho-gu, Seoul 06591, Republic of Korea
| | - Sung-Cheol Yang
- Department of Orthopaedic Surgery, Seoul St. Mary's Hospital, College of Medicine, The Catholic University of Korea, 222, Banpo-daero, Seocho-gu, Seoul 06591, Republic of Korea
| | - Jae-Hyeong Hur
- Department of Orthopaedic Surgery, Seoul St. Mary's Hospital, College of Medicine, The Catholic University of Korea, 222, Banpo-daero, Seocho-gu, Seoul 06591, Republic of Korea
| | - Yong In
- Department of Orthopaedic Surgery, Seoul St. Mary's Hospital, College of Medicine, The Catholic University of Korea, 222, Banpo-daero, Seocho-gu, Seoul 06591, Republic of Korea
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Khalid A, Senan EM, Al-Wagih K, Ali Al-Azzam MM, Alkhraisha ZM. Hybrid Techniques of X-ray Analysis to Predict Knee Osteoarthritis Grades Based on Fusion Features of CNN and Handcrafted. Diagnostics (Basel) 2023; 13:diagnostics13091609. [PMID: 37175000 PMCID: PMC10178472 DOI: 10.3390/diagnostics13091609] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/25/2023] [Revised: 04/25/2023] [Accepted: 04/28/2023] [Indexed: 05/15/2023] Open
Abstract
Knee osteoarthritis (KOA) is a chronic disease that impedes movement, especially in the elderly, affecting more than 5% of people worldwide. KOA goes through many stages, from the mild grade that can be treated to the severe grade in which the knee must be replaced. Therefore, early diagnosis of KOA is essential to avoid its development to the advanced stages. X-rays are one of the vital techniques for the early detection of knee infections, which requires highly experienced doctors and radiologists to distinguish Kellgren-Lawrence (KL) grading. Thus, artificial intelligence techniques solve the shortcomings of manual diagnosis. This study developed three methodologies for the X-ray analysis of both the Osteoporosis Initiative (OAI) and Rani Channamma University (RCU) datasets for diagnosing KOA and discrimination between KL grades. In all methodologies, the Principal Component Analysis (PCA) algorithm was applied after the CNN models to delete the unimportant and redundant features and keep the essential features. The first methodology for analyzing x-rays and diagnosing the degree of knee inflammation uses the VGG-19 -FFNN and ResNet-101 -FFNN systems. The second methodology of X-ray analysis and diagnosis of KOA grade by Feed Forward Neural Network (FFNN) is based on the combined features of VGG-19 and ResNet-101 before and after PCA. The third methodology for X-ray analysis and diagnosis of KOA grade by FFNN is based on the fusion features of VGG-19 and handcrafted features, and fusion features of ResNet-101 and handcrafted features. For an OAI dataset with fusion features of VGG-19 and handcrafted features, FFNN obtained an AUC of 99.25%, an accuracy of 99.1%, a sensitivity of 98.81%, a specificity of 100%, and a precision of 98.24%. For the RCU dataset with the fusion features of VGG-19 and the handcrafted features, FFNN obtained an AUC of 99.07%, an accuracy of 98.20%, a sensitivity of 98.16%, a specificity of 99.73%, and a precision of 98.08%.
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Affiliation(s)
- Ahmed Khalid
- Computer Department, Applied College, Najran University, Najran 66462, Saudi Arabia
| | - Ebrahim Mohammed Senan
- Department of Artificial Intelligence, Faculty of Computer Science and Information Technology, Alrazi University, Sana'a, Yemen
| | - Khalil Al-Wagih
- Department of Artificial Intelligence, Faculty of Computer Science and Information Technology, Alrazi University, Sana'a, Yemen
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Mohammed AS, Hasanaath AA, Latif G, Bashar A. Knee Osteoarthritis Detection and Severity Classification Using Residual Neural Networks on Preprocessed X-ray Images. Diagnostics (Basel) 2023; 13:diagnostics13081380. [PMID: 37189481 DOI: 10.3390/diagnostics13081380] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/16/2023] [Revised: 03/19/2023] [Accepted: 04/06/2023] [Indexed: 05/17/2023] Open
Abstract
One of the most common and challenging medical conditions to deal with in old-aged people is the occurrence of knee osteoarthritis (KOA). Manual diagnosis of this disease involves observing X-ray images of the knee area and classifying it under five grades using the Kellgren-Lawrence (KL) system. This requires the physician's expertise, suitable experience, and a lot of time, and even after that the diagnosis can be prone to errors. Therefore, researchers in the ML/DL domain have employed the capabilities of deep neural network (DNN) models to identify and classify KOA images in an automated, faster, and accurate manner. To this end, we propose the application of six pretrained DNN models, namely, VGG16, VGG19, ResNet101, MobileNetV2, InceptionResNetV2, and DenseNet121 for KOA diagnosis using images obtained from the Osteoarthritis Initiative (OAI) dataset. More specifically, we perform two types of classification, namely, a binary classification, which detects the presence or absence of KOA and secondly, classifying the severity of KOA in a three-class classification. For a comparative analysis, we experiment on three datasets (Dataset I, Dataset II, and Dataset III) with five, two, and three classes of KOA images, respectively. We achieved maximum classification accuracies of 69%, 83%, and 89%, respectively, with the ResNet101 DNN model. Our results show an improved performance from the existing work in the literature.
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Affiliation(s)
- Abdul Sami Mohammed
- Computer Engineering Department, Prince Mohammad Bin Fahd University, Al-Khobar 31952, Saudi Arabia
| | - Ahmed Abul Hasanaath
- Computer Science Department, Prince Mohammad Bin Fahd University, Al-Khobar 31952, Saudi Arabia
| | - Ghazanfar Latif
- Computer Science Department, Prince Mohammad Bin Fahd University, Al-Khobar 31952, Saudi Arabia
| | - Abul Bashar
- Computer Engineering Department, Prince Mohammad Bin Fahd University, Al-Khobar 31952, Saudi Arabia
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22
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Cheng CT, Hsu CP, Ooyang CH, Chou CY, Lin NY, Lin JY, Ku YK, Lin HS, Kao SK, Chen HW, Wu YT, Liao CH. Evaluation of ensemble strategy on the development of multiple view ankle fracture detection algorithm. Br J Radiol 2023; 96:20220924. [PMID: 36930721 PMCID: PMC10161902 DOI: 10.1259/bjr.20220924] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 03/19/2023] Open
Abstract
OBJECTIVE To identify the feasibility and efficiency of deep convolutional neural networks (DCNNs) in the detection of ankle fractures and to explore ensemble strategies that applied multiple projections of radiographs.Ankle radiographs (AXRs) are the primary tool used to diagnose ankle fractures. Applying DCNN algorithms on AXRs can potentially improve the diagnostic accuracy and efficiency of detecting ankle fractures. METHODS A DCNN was trained using a trauma image registry, including 3102 AXRs. We separately trained the DCNN on anteroposterior (AP) and lateral (Lat) AXRs. Different ensemble methods, such as "sum-up," "severance-OR," and "severance-Both," were evaluated to incorporate the results of the model using different projections of view. RESULTS The AP/Lat model's individual sensitivity, specificity, positive-predictive value, accuracy, and F1 score were 79%/84%, 90%/86%, 88%/86%, 83%/85%, and 0.816/0.850, respectively. Furthermore, the area under the receiver operating characteristic curve (AUROC) of the AP/Lat model was 0.890/0.894 (95% CI: 0.826-0.954/0.831-0.953). The sum-up method generated balanced results by applying both models and obtained an AUROC of 0.917 (95% CI: 0.863-0.972) with 87% accuracy. The severance-OR method resulted in a better sensitivity of 90%, and the severance-Both method obtained a high specificity of 94%. CONCLUSION Ankle fracture in the AXR could be identified by the trained DCNN algorithm. The selection of ensemble methods can depend on the clinical situation which might help clinicians detect ankle fractures efficiently without interrupting the current clinical pathway. ADVANCES IN KNOWLEDGE This study demonstrated different ensemble strategies of AI algorithms on multiple view AXRs to optimize the performance in various clinical needs.
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Affiliation(s)
- Chi-Tung Cheng
- Department of Trauma and Emergency Surgery, Chang Gung Memorial Hospital, Linkou ; Chang Gung University, Taoyuan, Taiwan
| | - Chih-Po Hsu
- Department of Trauma and Emergency Surgery, Chang Gung Memorial Hospital, Linkou ; Chang Gung University, Taoyuan, Taiwan
| | - Chun-Hsiang Ooyang
- Department of Trauma and Emergency Surgery, Chang Gung Memorial Hospital, Linkou ; Chang Gung University, Taoyuan, Taiwan
| | - Chia-Yi Chou
- Department of Trauma and Emergency Surgery, Chang Gung Memorial Hospital, Linkou ; Chang Gung University, Taoyuan, Taiwan
| | - Nai-Yu Lin
- Department of Surgery, Chang Gung Memorial Hospital, Linkou; Chang Gung University, Taoyuan, Taiwan
| | - Jia-Yen Lin
- School of Medicine, Chang Gung University, Taoyuan, Taiwan
| | - Yi-Kang Ku
- Department of Medical Imaging and Intervention, New Taipei Municipal TuCheng Hospital, Chang Gung Medical Foundation, New Taipei, Taiwan
| | - Hou-Shian Lin
- Department of Trauma and Emergency Surgery, Chang Gung Memorial Hospital, Linkou ; Chang Gung University, Taoyuan, Taiwan
| | - Shao-Ku Kao
- Department of Electrical Engineering and Green Technology Research Center, School of Electrical and Computer Engineering, College of Engineering, Chang Gung University, Taoyuan, Taiwan
| | - Huan-Wu Chen
- Department of Medical Imaging and Intervention, Chang Gung Memorial Hospital, Linkou, Taoyuan, Taiwan
| | - Yu-Tung Wu
- Department of Trauma and Emergency Surgery, Chang Gung Memorial Hospital, Linkou ; Chang Gung University, Taoyuan, Taiwan
| | - Chien-Hung Liao
- Department of Trauma and Emergency Surgery, Chang Gung Memorial Hospital, Linkou ; Chang Gung University, Taoyuan, Taiwan
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Zhang D, Wang H, Deng J, Wang T, Shen C, Feng J. CAMS-Net: An attention-guided feature selection network for rib segmentation in chest X-rays. Comput Biol Med 2023. [DOI: 10.1016/j.compbiomed.2023.106702] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/24/2023]
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24
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A Novel Autoencoder-Based Feature Selection Method for Drug-Target Interaction Prediction with Human-Interpretable Feature Weights. Symmetry (Basel) 2023. [DOI: 10.3390/sym15010192] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/11/2023] Open
Abstract
Drug-target interaction prediction provides important information that could be exploited for drug discovery, drug design, and drug repurposing. Chemogenomic approaches for predicting drug-target interaction assume that similar receptors bind to similar ligands. Capturing this similarity in so-called “fingerprints” and combining the target and ligand fingerprints provide an efficient way to search for protein-ligand pairs that are more likely to interact. In this study, we constructed drug and target fingerprints by employing features extracted from the DrugBank. However, the number of extracted features is quite large, necessitating an effective feature selection mechanism since some features can be redundant or irrelevant to drug-target interaction prediction problems. Although such feature selection methods are readily available in the literature, usually they act as black boxes and do not provide any quantitative information about why a specific feature is preferred over another. To alleviate this lack of human interpretability, we proposed a novel feature selection method in which we used an autoencoder as a symmetric learning method and compared the proposed method to some popular feature selection algorithms, such as Kbest, Variance Threshold, and Decision Tree. The results of a detailed performance study, in which we trained six Multi-Layer Perceptron (MLP) Networks of different sizes and configurations for prediction, demonstrate that the proposed method yields superior results compared to the aforementioned methods.
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25
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Integrated Deep Learning and Supervised Machine Learning Model for Predictive Fetal Monitoring. Diagnostics (Basel) 2022; 12:diagnostics12112843. [PMID: 36428902 PMCID: PMC9689398 DOI: 10.3390/diagnostics12112843] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/13/2022] [Revised: 11/06/2022] [Accepted: 11/14/2022] [Indexed: 11/19/2022] Open
Abstract
Asphyxiation associated with metabolic acidosis is one of the common causes of fetal deaths. The paper aims to develop a feature extraction and prediction algorithm capable of identifying most of the features in the SISPORTO software package and late and variable decelerations. The resulting features were used for classification based on umbilical cord pH data. The algorithms developed here were used to predict cord pH levels. The prediction system assists the obstetricians in assessing the state of the fetus better than the category methods, as only about 30% of the patients in the pathological category suffer from acidosis, while the majority of acidotic babies were in the suspect category, which is considered lower risk. By predicting the direct indicator of acidosis, umbilical cord pH, this work demonstrates a methodology, which uses fetal heart rate and uterine activity, to identify acidosis. This paper introduces a forecasting model based on deep learning to predict heart rate and uterine contractions, integrated with the classification algorithm, resulting in a robust tool for predictive fetal monitoring. The hybrid algorithm resulted in a model capable of providing future conditions of the fetus, which obstetricians can use for diagnosis and planning interventions. The ensemble classification algorithm had a test accuracy of 85% (n = 24) in predicting fetal acidosis on the features extracted from the cardiotocography data. When integrated with the classification model, the results from the prediction model (long short-term memory network) can effectively identify fetal acidosis 2 or 4 min in the future.
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Narasimha Raju AS, Jayavel K, Rajalakshmi T. ColoRectalCADx: Expeditious Recognition of Colorectal Cancer with Integrated Convolutional Neural Networks and Visual Explanations Using Mixed Dataset Evidence. COMPUTATIONAL AND MATHEMATICAL METHODS IN MEDICINE 2022; 2022:8723957. [PMID: 36404909 PMCID: PMC9671728 DOI: 10.1155/2022/8723957] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 09/27/2022] [Accepted: 10/27/2022] [Indexed: 12/07/2023]
Abstract
Colorectal cancer typically affects the gastrointestinal tract within the human body. Colonoscopy is one of the most accurate methods of detecting cancer. The current system facilitates the identification of cancer by computer-assisted diagnosis (CADx) systems with a limited number of deep learning methods. It does not imply the depiction of mixed datasets for the functioning of the system. The proposed system, called ColoRectalCADx, is supported by deep learning (DL) models suitable for cancer research. The CADx system comprises five stages: convolutional neural networks (CNN), support vector machine (SVM), long short-term memory (LSTM), visual explanation such as gradient-weighted class activation mapping (Grad-CAM), and semantic segmentation phases. Here, the key components of the CADx system are equipped with 9 individual and 12 integrated CNNs, implying that the system consists mainly of investigational experiments with a total of 21 CNNs. In the subsequent phase, the CADx has a combination of CNNs of concatenated transfer learning functions associated with the machine SVM classification. Additional classification is applied to ensure effective transfer of results from CNN to LSTM. The system is mainly made up of a combination of CVC Clinic DB, Kvasir2, and Hyper Kvasir input as a mixed dataset. After CNN and LSTM, in advanced stage, malignancies are detected by using a better polyp recognition technique with Grad-CAM and semantic segmentation using U-Net. CADx results have been stored on Google Cloud for record retention. In these experiments, among all the CNNs, the individual CNN DenseNet-201 (87.1% training and 84.7% testing accuracies) and the integrated CNN ADaDR-22 (84.61% training and 82.17% testing accuracies) were the most efficient for cancer detection with the CNN+LSTM model. ColoRectalCADx accurately identifies cancer through individual CNN DesnseNet-201 and integrated CNN ADaDR-22. In Grad-CAM's visual explanations, CNN DenseNet-201 displays precise visualization of polyps, and CNN U-Net provides precise malignant polyps.
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Affiliation(s)
- Akella S. Narasimha Raju
- Department of Networking and Communications, School of Computing, SRM Institute of Science and Technology, Kattankulathur, 603203 Chennai, India
| | - Kayalvizhi Jayavel
- Department of Networking and Communications, School of Computing, SRM Institute of Science and Technology, Kattankulathur, 603203 Chennai, India
| | - T. Rajalakshmi
- Department of Electronics and Communication Engineering, School of Electrical and Electronics Engineering, SRM Institute of Science and Technology, Kattankulathur, 603203 Chennai, India
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27
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Roy S, Meena T, Lim SJ. Demystifying Supervised Learning in Healthcare 4.0: A New Reality of Transforming Diagnostic Medicine. Diagnostics (Basel) 2022; 12:2549. [PMID: 36292238 PMCID: PMC9601517 DOI: 10.3390/diagnostics12102549] [Citation(s) in RCA: 26] [Impact Index Per Article: 13.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/14/2022] [Revised: 10/17/2022] [Accepted: 10/18/2022] [Indexed: 11/17/2022] Open
Abstract
The global healthcare sector continues to grow rapidly and is reflected as one of the fastest-growing sectors in the fourth industrial revolution (4.0). The majority of the healthcare industry still uses labor-intensive, time-consuming, and error-prone traditional, manual, and manpower-based methods. This review addresses the current paradigm, the potential for new scientific discoveries, the technological state of preparation, the potential for supervised machine learning (SML) prospects in various healthcare sectors, and ethical issues. The effectiveness and potential for innovation of disease diagnosis, personalized medicine, clinical trials, non-invasive image analysis, drug discovery, patient care services, remote patient monitoring, hospital data, and nanotechnology in various learning-based automation in healthcare along with the requirement for explainable artificial intelligence (AI) in healthcare are evaluated. In order to understand the potential architecture of non-invasive treatment, a thorough study of medical imaging analysis from a technical point of view is presented. This study also represents new thinking and developments that will push the boundaries and increase the opportunity for healthcare through AI and SML in the near future. Nowadays, SML-based applications require a lot of data quality awareness as healthcare is data-heavy, and knowledge management is paramount. Nowadays, SML in biomedical and healthcare developments needs skills, quality data consciousness for data-intensive study, and a knowledge-centric health management system. As a result, the merits, demerits, and precautions need to take ethics and the other effects of AI and SML into consideration. The overall insight in this paper will help researchers in academia and industry to understand and address the future research that needs to be discussed on SML in the healthcare and biomedical sectors.
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Affiliation(s)
- Sudipta Roy
- Artificial Intelligence & Data Science, Jio Institute, Navi Mumbai 410206, India
| | - Tanushree Meena
- Artificial Intelligence & Data Science, Jio Institute, Navi Mumbai 410206, India
| | - Se-Jung Lim
- Division of Convergence, Honam University, 120, Honamdae-gil, Gwangsan-gu, Gwangju 62399, Korea
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