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Al Mohammad B, Aldaradkeh A, Gharaibeh M, Reed W. Assessing radiologists' and radiographers' perceptions on artificial intelligence integration: opportunities and challenges. Br J Radiol 2024; 97:763-769. [PMID: 38273675 PMCID: PMC11027289 DOI: 10.1093/bjr/tqae022] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/21/2023] [Revised: 09/30/2023] [Accepted: 01/21/2024] [Indexed: 01/27/2024] Open
Abstract
OBJECTIVES The objective of this study was to evaluate radiologists' and radiographers' opinions and perspectives on artificial intelligence (AI) and its integration into the radiology department. Additionally, we investigated the most common challenges and barriers that radiologists and radiographers face when learning about AI. METHODS A nationwide, online descriptive cross-sectional survey was distributed to radiologists and radiographers working in hospitals and medical centres from May 29, 2023 to July 30, 2023. The questionnaire examined the participants' opinions, feelings, and predictions regarding AI and its applications in the radiology department. Descriptive statistics were used to report the participants' demographics and responses. Five-points Likert-scale data were reported using divergent stacked bar graphs to highlight any central tendencies. RESULTS Responses were collected from 258 participants, revealing a positive attitude towards implementing AI. Both radiologists and radiographers predicted breast imaging would be the subspecialty most impacted by the AI revolution. MRI, mammography, and CT were identified as the primary modalities with significant importance in the field of AI application. The major barrier encountered by radiologists and radiographers when learning about AI was the lack of mentorship, guidance, and support from experts. CONCLUSION Participants demonstrated a positive attitude towards learning about AI and implementing it in the radiology practice. However, radiologists and radiographers encounter several barriers when learning about AI, such as the absence of experienced professionals support and direction. ADVANCES IN KNOWLEDGE Radiologists and radiographers reported several barriers to AI learning, with the most significant being the lack of mentorship and guidance from experts, followed by the lack of funding and investment in new technologies.
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Affiliation(s)
- Badera Al Mohammad
- Department of Allied Medical Sciences, Faculty of Applied Medical Sciences, Jordan University of Science and Technology, Irbid 22110, Jordan
| | - Afnan Aldaradkeh
- Department of Allied Medical Sciences, Faculty of Applied Medical Sciences, Jordan University of Science and Technology, Irbid 22110, Jordan
| | - Monther Gharaibeh
- Department of Special Surgery, Faculty of Medicine, The Hashemite University, Zarqa 13133, Jordan
| | - Warren Reed
- Discipline of Medical Imaging Science, Faculty of Medicine and Health, University of Sydney 2006, Sydney, NSW, Australia
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Yuan K, Yao X, Liao X, Diao P, Xin X, Ma J, Li J, Orlandini LC. Comparing breath hold versus free breathing irradiation for left-sided breast radiotherapy by PlanIQ™. Radiat Oncol 2023; 18:200. [PMID: 38098106 PMCID: PMC10722777 DOI: 10.1186/s13014-023-02386-2] [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: 02/17/2023] [Accepted: 12/08/2023] [Indexed: 12/17/2023] Open
Abstract
BACKGROUND Breast cancer is the most widespread cancer in women and young women worldwide. Moving towards customised radiotherapy, balancing the use of the available technology with the best treatment modality may not be an easy task in the daily routine. This study aims to evaluate the effectiveness of introducing IQ-feasibility into clinical practice to support the decision of free-breathing (FB) versus breath-hold (BH) left-sided breast irradiations, in order to optimise the technology available and the effectiveness of the treatment. METHODS Thirty-five patients who received 3D radiotherapy treatment of the left breast in deep-inspiration BH were included in this retrospective study. Computed tomography scans in FB and BH were acquired for each patient; targets contoured in both imaging datasets by an experienced radiation oncologist, and organs at risk delineated using automatic segmentation software were exported to PlanIQ™ (Sun Nuclear Corp.) to generate feasibility dose volume histogram (FDVHs). The dosimetric parameter of BH versus FB FDVH, and BH clinical dataset versus BH FDVH were compared. RESULTS A total of 30 patients out of 35 patients analysed, presented for the BH treatments a significant reduction (p < 0.05) in the heart mean dose ([Formula: see text]), volume receiving 5 Gy ([Formula: see text]) and 20 Gy ([Formula: see text]), of 35.7%, 54.5%, and 2.1%, respectively; for the left lung, a lower reduction was registered and significant only for [Formula: see text] (21.4%, p = 0.046). For the remaining five patients, the FDVH cut-off points of heart and lung were superimposable with differences of less than 1%. Heart and left lung dosimetric parameters of the BH clinical plans are located in the difficult zone of the FDVH and differ significantly (p < 0.05) from the corresponding parameters of the FDVH curves delimiting this buffer area between the impossible and feasible zones, respectively. CONCLUSION The use of PlanIQTM as a decision-support tool for the FB versus BH treatment delivery modality allows customisation of the treatment technique using the most appropriate technology for each patient enabling accurate management of available technologies.
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Affiliation(s)
- Ke Yuan
- Department of Radiation Oncology, Sichuan Cancer Hospital & Institute, Affiliated Cancer Hospital of University and Electronic Science and Technology of China, Chengdu, China
- Sichuan Clinical Research Center for Cancer, Sichuan Cancer Center, Chengdu, China
| | - Xinghong Yao
- Department of Radiation Oncology, Sichuan Cancer Hospital & Institute, Affiliated Cancer Hospital of University and Electronic Science and Technology of China, Chengdu, China
- Sichuan Clinical Research Center for Cancer, Sichuan Cancer Center, Chengdu, China
| | - Xiongfei Liao
- Department of Radiation Oncology, Sichuan Cancer Hospital & Institute, Affiliated Cancer Hospital of University and Electronic Science and Technology of China, Chengdu, China.
- Sichuan Clinical Research Center for Cancer, Sichuan Cancer Center, Chengdu, China.
| | - Pen Diao
- Department of Radiation Oncology, Sichuan Cancer Hospital & Institute, Affiliated Cancer Hospital of University and Electronic Science and Technology of China, Chengdu, China
- Sichuan Clinical Research Center for Cancer, Sichuan Cancer Center, Chengdu, China
| | - Xin Xin
- Department of Radiation Oncology, Sichuan Cancer Hospital & Institute, Affiliated Cancer Hospital of University and Electronic Science and Technology of China, Chengdu, China
- Sichuan Clinical Research Center for Cancer, Sichuan Cancer Center, Chengdu, China
| | - Jiabao Ma
- Department of Radiation Oncology, Sichuan Cancer Hospital & Institute, Affiliated Cancer Hospital of University and Electronic Science and Technology of China, Chengdu, China
- Sichuan Clinical Research Center for Cancer, Sichuan Cancer Center, Chengdu, China
| | - Jie Li
- Department of Radiation Oncology, Sichuan Cancer Hospital & Institute, Affiliated Cancer Hospital of University and Electronic Science and Technology of China, Chengdu, China
- Sichuan Clinical Research Center for Cancer, Sichuan Cancer Center, Chengdu, China
| | - Lucia Clara Orlandini
- Department of Radiation Oncology, Sichuan Cancer Hospital & Institute, Affiliated Cancer Hospital of University and Electronic Science and Technology of China, Chengdu, China
- Sichuan Clinical Research Center for Cancer, Sichuan Cancer Center, Chengdu, China
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3
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Hoque SMH, Pirrone G, Matrone F, Donofrio A, Fanetti G, Caroli A, Rista RS, Bortolus R, Avanzo M, Drigo A, Chiovati P. Clinical Use of a Commercial Artificial Intelligence-Based Software for Autocontouring in Radiation Therapy: Geometric Performance and Dosimetric Impact. Cancers (Basel) 2023; 15:5735. [PMID: 38136281 PMCID: PMC10741804 DOI: 10.3390/cancers15245735] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/11/2023] [Revised: 11/30/2023] [Accepted: 12/01/2023] [Indexed: 12/24/2023] Open
Abstract
PURPOSE When autocontouring based on artificial intelligence (AI) is used in the radiotherapy (RT) workflow, the contours are reviewed and eventually adjusted by a radiation oncologist before an RT treatment plan is generated, with the purpose of improving dosimetry and reducing both interobserver variability and time for contouring. The purpose of this study was to evaluate the results of application of a commercial AI-based autocontouring for RT, assessing both geometric accuracies and the influence on optimized dose from automatically generated contours after review by human operator. MATERIALS AND METHODS A commercial autocontouring system was applied to a retrospective database of 40 patients, of which 20 were treated with radiotherapy for prostate cancer (PCa) and 20 for head and neck cancer (HNC). Contours resulting from AI were compared against AI contours reviewed by human operator and human-only contours using Dice similarity coefficient (DSC), Hausdorff distance (HD), and relative volume difference (RVD). Dosimetric indices such as Dmean, D0.03cc, and normalized plan quality metrics were used to compare dose distributions from RT plans generated from structure sets contoured by humans assisted by AI against plans from manual contours. The reduction in contouring time obtained by using automated tools was also assessed. A Wilcoxon rank sum test was computed to assess the significance of differences. Interobserver variability of the comparison of manual vs. AI-assisted contours was also assessed among two radiation oncologists for PCa. RESULTS For PCa, AI-assisted segmentation showed good agreement with expert radiation oncologist structures with average DSC among patients ≥ 0.7 for all structures, and minimal radiation oncology adjustment of structures (DSC of adjusted versus AI structures ≥ 0.91). For HNC, results of comparison between manual and AI contouring varied considerably e.g., 0.77 for oral cavity and 0.11-0.13 for brachial plexus, but again, adjustment was generally minimal (DSC of adjusted against AI contours 0.97 for oral cavity, 0.92-0.93 for brachial plexus). The difference in dose for the target and organs at risk were not statistically significant between human and AI-assisted, with the only exceptions of D0.03cc to the anal canal and Dmean to the brachial plexus. The observed average differences in plan quality for PCa and HNC cases were 8% and 6.7%, respectively. The dose parameter changes due to interobserver variability in PCa were small, with the exception of the anal canal, where large dose variations were observed. The reduction in time required for contouring was 72% for PCa and 84% for HNC. CONCLUSIONS When an autocontouring system is used in combination with human review, the time of the RT workflow is significantly reduced without affecting dose distribution and plan quality.
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Affiliation(s)
- S M Hasibul Hoque
- Medical Physics Department, Centro di Riferimento Oncologico di Aviano (CRO) IRCCS, 33081 Aviano, Italy; (S.M.H.H.); (G.P.); (R.S.R.); (M.A.); (A.D.)
| | - Giovanni Pirrone
- Medical Physics Department, Centro di Riferimento Oncologico di Aviano (CRO) IRCCS, 33081 Aviano, Italy; (S.M.H.H.); (G.P.); (R.S.R.); (M.A.); (A.D.)
| | - Fabio Matrone
- Radiation Oncology Department, Centro di Riferimento Oncologico di Aviano (CRO) IRCCS, 33081 Aviano, Italy; (F.M.); (A.D.); (G.F.); (A.C.); (R.B.)
| | - Alessandra Donofrio
- Radiation Oncology Department, Centro di Riferimento Oncologico di Aviano (CRO) IRCCS, 33081 Aviano, Italy; (F.M.); (A.D.); (G.F.); (A.C.); (R.B.)
| | - Giuseppe Fanetti
- Radiation Oncology Department, Centro di Riferimento Oncologico di Aviano (CRO) IRCCS, 33081 Aviano, Italy; (F.M.); (A.D.); (G.F.); (A.C.); (R.B.)
| | - Angela Caroli
- Radiation Oncology Department, Centro di Riferimento Oncologico di Aviano (CRO) IRCCS, 33081 Aviano, Italy; (F.M.); (A.D.); (G.F.); (A.C.); (R.B.)
| | - Rahnuma Shahrin Rista
- Medical Physics Department, Centro di Riferimento Oncologico di Aviano (CRO) IRCCS, 33081 Aviano, Italy; (S.M.H.H.); (G.P.); (R.S.R.); (M.A.); (A.D.)
| | - Roberto Bortolus
- Radiation Oncology Department, Centro di Riferimento Oncologico di Aviano (CRO) IRCCS, 33081 Aviano, Italy; (F.M.); (A.D.); (G.F.); (A.C.); (R.B.)
| | - Michele Avanzo
- Medical Physics Department, Centro di Riferimento Oncologico di Aviano (CRO) IRCCS, 33081 Aviano, Italy; (S.M.H.H.); (G.P.); (R.S.R.); (M.A.); (A.D.)
| | - Annalisa Drigo
- Medical Physics Department, Centro di Riferimento Oncologico di Aviano (CRO) IRCCS, 33081 Aviano, Italy; (S.M.H.H.); (G.P.); (R.S.R.); (M.A.); (A.D.)
| | - Paola Chiovati
- Medical Physics Department, Centro di Riferimento Oncologico di Aviano (CRO) IRCCS, 33081 Aviano, Italy; (S.M.H.H.); (G.P.); (R.S.R.); (M.A.); (A.D.)
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Fiagbedzi E, Hasford F, Tagoe SN. The influence of artificial intelligence on the work of the medical physicist in radiotherapy practice: a short review. BJR Open 2023; 5:20230003. [PMID: 37942499 PMCID: PMC10630976 DOI: 10.1259/bjro.20230003] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/31/2022] [Revised: 04/11/2023] [Accepted: 08/02/2023] [Indexed: 11/10/2023] Open
Abstract
There have been many applications and influences of Artificial intelligence (AI) in many sectors and its professionals, that of radiotherapy and the medical physicist is no different. AI and technological advances have necessitated changing roles of medical physicists due to the development of modernized technology with image-guided accessories for the radiotherapy treatment of cancer patients. Given the changing role of medical physicists in ensuring patient safety and optimal care, AI can reshape radiotherapy practice now and in some years to come. Medical physicists' roles in radiotherapy practice have evolved to meet technology for the management of better patient care in the age of modern radiotherapy. This short review provides an insight into the influence of AI on the changing role of medical physicists in each specific chain of the workflow in radiotherapy in which they are involved.
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Affiliation(s)
| | - Francis Hasford
- Department of Medical Physics, Accra-Ghana, University of Ghana, Accra, Ghana
| | - Samuel Nii Tagoe
- Department of Medical Physics, Accra-Ghana, University of Ghana, Accra, Ghana
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5
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Manson EN, Hasford F, Trauernicht C, Ige TA, Inkoom S, Inyang S, Samba O, Khelassi-Toutaoui N, Lazarus G, Sosu EK, Pokoo-Aikins M, Stoeva M. Africa's readiness for artificial intelligence in clinical radiotherapy delivery: Medical physicists to lead the way. Phys Med 2023; 113:102653. [PMID: 37586146 DOI: 10.1016/j.ejmp.2023.102653] [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: 05/12/2023] [Revised: 07/30/2023] [Accepted: 08/05/2023] [Indexed: 08/18/2023] Open
Abstract
BACKGROUND There have been several proposals by researchers for the introduction of Artificial Intelligence (AI) technology due to its promising role in radiotherapy practice. However, prior to the introduction of the technology, there are certain general recommendations that must be achieved. Also, the current challenges of AI must be addressed. In this review, we assess how Africa is prepared for the integration of AI technology into radiotherapy service delivery. METHODS To assess the readiness of Africa for integration of AI in radiotherapy services delivery, a narrative review of the available literature from PubMed, Science Direct, Google Scholar, and Scopus was conducted in the English language using search terms such as Artificial Intelligence, Radiotherapy in Africa, Machine Learning, Deep Learning, and Quality Assurance. RESULTS We identified a number of issues that could limit the successful integration of AI technology into radiotherapy practice. The major issues include insufficient data for training and validation of AI models, lack of educational curriculum for AI radiotherapy-related courses, no/limited AI teaching professionals, funding, and lack of AI technology and resources. Solutions identified to facilitate smooth implementation of the technology into radiotherapy practices within the region include: creating an accessible national data bank, integrating AI radiotherapy training programs into Africa's educational curriculum, investing in AI technology and resources such as electronic health records and cloud storage, and creation of legal laws and policies to support the use of the technology. These identified solutions need to be implemented on the background of creating awareness among health workers within the radiotherapy space. CONCLUSION The challenges identified in this review are common among all the geographical regions in the African continent. Therefore, all institutions offering radiotherapy education and training programs, management of the medical centers for radiotherapy and oncology, national and regional professional bodies for medical physics, ministries of health, governments, and relevant stakeholders must take keen interest and work together to achieve this goal.
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Affiliation(s)
| | | | | | | | | | | | - Odette Samba
- General Hospital of Yaoundé and University of Yaoundé I, Cameroon.
| | | | - Graeme Lazarus
- Inkosi Albert Luthuli Central Hospital, Durban, South Africa.
| | - Edem Kwabla Sosu
- School of Nuclear and Allied Sciences, University of Ghana, Ghana.
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7
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Mancosu P, Lambri N, Castiglioni I, Dei D, Iori M, Loiacono D, Russo S, Talamonti C, Villaggi E, Scorsetti M, Avanzo M. Applications of artificial intelligence in stereotactic body radiation therapy. Phys Med Biol 2022; 67. [DOI: 10.1088/1361-6560/ac7e18] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/01/2022] [Accepted: 07/04/2022] [Indexed: 11/12/2022]
Abstract
Abstract
This topical review focuses on the applications of artificial intelligence (AI) tools to stereotactic body radiation therapy (SBRT). The high dose per fraction and the limited number of fractions in SBRT require stricter accuracy than standard radiation therapy. The intent of this review is to describe the development and evaluate the possible benefit of AI tools integration into the radiation oncology workflow for SBRT automation. The selected papers were subdivided into four sections, representative of the whole radiotherapy process: ‘AI in SBRT target and organs at risk contouring’, ‘AI in SBRT planning’, ‘AI during the SBRT delivery’, and ‘AI for outcome prediction after SBRT’. Each section summarises the challenges, as well as limits and needs for improvement to achieve better integration of AI tools in the clinical workflow.
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8
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Simpson-Page E, Coogan P, Kron T, Lowther N, Murray R, Noble C, Smith I, Wilks R, Crowe SB. Webinar and survey on quality management principles within the Australian and New Zealand ACPSEM Workforce. Phys Eng Sci Med 2022; 45:679-685. [PMID: 35834171 DOI: 10.1007/s13246-022-01160-0] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/28/2022]
Abstract
Healthcare relies upon the accurate and safe delivery of patient care. This is only achievable when systems are developed to ensure high quality, robust outcomes, for instance quality management systems. The concept of quality management can take on a different meaning depending on the context in which it is found. To add complication, the amount of education required for quality management will vary depending on one's exposure to the implementation of quality systems. In part to address these issues, the Australasian College of Physical Scientists and Engineers in Medicine (ACPSEM) Queensland Branch held a quality management webinar for members and non-members across Australia and New Zealand. The purpose of the webinar was to educate and facilitate discussion regarding the application of quality management principles for the ACPSEM profession. In conjunction, a pre- and post-webinar survey was conducted to gain an insight into existing knowledge and attitudes within the professions governed by the ACPSEM and students undertaking related studies. This paper authored by the webinar speakers reintroduces the quality management principles that were discussed in webinar, exemplifies the importance of quality management skills within the ACPSEM professions and presents the results of the surveys, promoting the need for more educational resources on quality management tools.
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Affiliation(s)
- Emily Simpson-Page
- Cancer Care Services, Royal Brisbane and Women's Hospital, Brisbane, Australia.
| | - Paul Coogan
- Q-TRaCE, Department of Nuclear Medicine & Specialised PET Services Queensland, Royal Brisbane and Women's Hospital, Brisbane, Australia
| | - Tomas Kron
- Physical Sciences Department, Peter MacCallum Cancer Centre, Melbourne, Australia
| | - Nicholas Lowther
- Wellington Blood & Cancer Centre, Wellington Hospital, Wellington, New Zealand
| | - Rebecca Murray
- Herston Biofabrication Institute, Metro North Hospital and Health Service, Brisbane, Australia
| | - Christopher Noble
- Department of Radiation Oncology, Princess Alexandra Hospital, Brisbane, Australia
| | - Ian Smith
- St. Andrews War Memorial Hospital, Brisbane, Australia
| | - Rachael Wilks
- Cancer Care Services, Royal Brisbane and Women's Hospital, Brisbane, Australia.,Herston Biofabrication Institute, Metro North Hospital and Health Service, Brisbane, Australia.,School of Information Technology and Electrical Engineering, University of Queensland, Brisbane, Australia
| | - Scott B Crowe
- Cancer Care Services, Royal Brisbane and Women's Hospital, Brisbane, Australia.,Herston Biofabrication Institute, Metro North Hospital and Health Service, Brisbane, Australia.,School of Information Technology and Electrical Engineering, University of Queensland, Brisbane, Australia.,School of Chemistry and Physics, Queensland University of Technology, Brisbane, Australia
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9
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A Synopsis of Machine and Deep Learning in Medical Physics and Radiology. JOURNAL OF BASIC AND CLINICAL HEALTH SCIENCES 2022. [DOI: 10.30621/jbachs.960154] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/18/2022]
Abstract
Machine learning (ML) and deep learning (DL) technologies introduced in the fields of medical physics, radiology, and oncology have made great strides in the past few years. A good many applications have proven to be an efficacious automated diagnosis and radiotherapy system. This paper outlines DL's general concepts and principles, key computational methods, and resources, as well as the implementation of automated models in diagnostic radiology and radiation oncology research. In addition, the potential challenges and solutions of DL technology are also discussed.
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Giansanti D. Comment on Patel, B.; Makaryus, A.N. Artificial Intelligence Advances in the World of Cardiovascular Imaging. Healthcare 2022, 10, 154. Healthcare (Basel) 2022; 10:727. [PMID: 35455904 PMCID: PMC9032641 DOI: 10.3390/healthcare10040727] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/23/2022] [Revised: 04/01/2022] [Accepted: 04/07/2022] [Indexed: 02/05/2023] Open
Abstract
Regarding Dr. Makaryus's interesting review study [...].
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Affiliation(s)
- Daniele Giansanti
- Centro Nazionale Tecnologie Innovative in Sanità Pubblica, Istituto Superiore di Sanità, 00161 Rome, Italy
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Mortality Prediction of COVID-19 Patients Using Radiomic and Neural Network Features Extracted from a Wide Chest X-ray Sample Size: A Robust Approach for Different Medical Imbalanced Scenarios. APPLIED SCIENCES-BASEL 2022. [DOI: 10.3390/app12083903] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/20/2022]
Abstract
Aim: The aim of this study was to develop robust prognostic models for mortality prediction of COVID-19 patients, applicable to different sets of real scenarios, using radiomic and neural network features extracted from chest X-rays (CXRs) with a certified and commercially available software. Methods: 1816 patients from 5 different hospitals in the Province of Reggio Emilia were included in the study. Overall, 201 radiomic features and 16 neural network features were extracted from each COVID-19 patient’s radiography. The initial dataset was balanced to train the classifiers with the same number of dead and survived patients, randomly selected. The pipeline had three main parts: balancing procedure; three-step feature selection; and mortality prediction with radiomic features through three machine learning (ML) classification models: AdaBoost (ADA), Quadratic Discriminant Analysis (QDA) and Random Forest (RF). Five evaluation metrics were computed on the test samples. The performance for death prediction was validated on both a balanced dataset (Case 1) and an imbalanced dataset (Case 2). Results: accuracy (ACC), area under the ROC-curve (AUC) and sensitivity (SENS) for the best classifier were, respectively, 0.72 ± 0.01, 0.82 ± 0.02 and 0.84 ± 0.04 for Case 1 and 0.70 ± 0.04, 0.79 ± 0.03 and 0.76 ± 0.06 for Case 2. These results show that the prediction of COVID-19 mortality is robust in a different set of scenarios. Conclusions: Our large and varied dataset made it possible to train ML algorithms to predict COVID-19 mortality using radiomic and neural network features of CXRs.
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AI and Clinical Decision Making: The Limitations and Risks of Computational Reductionism in Bowel Cancer Screening. APPLIED SCIENCES-BASEL 2022. [DOI: 10.3390/app12073341] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/09/2022]
Abstract
Advances in artificial intelligence in healthcare are frequently promoted as ‘solutions’ to improve the accuracy, safety, and quality of clinical decisions, treatments, and care. Despite some diagnostic success, however, AI systems rely on forms of reductive reasoning and computational determinism that embed problematic assumptions about clinical decision-making and clinical practice. Clinician autonomy, experience, and judgement are reduced to inputs and outputs framed as binary or multi-class classification problems benchmarked against a clinician’s capacity to identify or predict disease states. This paper examines this reductive reasoning in AI systems for colorectal cancer (CRC) to highlight their limitations and risks: (1) in AI systems themselves due to inherent biases in (a) retrospective training datasets and (b) embedded assumptions in underlying AI architectures and algorithms; (2) in the problematic and limited evaluations being conducted on AI systems prior to system integration in clinical practice; and (3) in marginalising socio-technical factors in the context-dependent interactions between clinicians, their patients, and the broader health system. The paper argues that to optimise benefits from AI systems and to avoid negative unintended consequences for clinical decision-making and patient care, there is a need for more nuanced and balanced approaches to AI system deployment and evaluation in CRC.
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Applications of Medical Physics. APPLIED SCIENCES-BASEL 2022. [DOI: 10.3390/app12041852] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/16/2022]
Abstract
Since the discovery of X-rays, the use of the principles and methods of physics in medicine has contributed to the improvement of human health [...]
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Danieli R, Milano A, Gallo S, Veronese I, Lascialfari A, Indovina L, Botta F, Ferrari M, Cicchetti A, Raspanti D, Cremonesi M. Personalized Dosimetry in Targeted Radiation Therapy: A Look to Methods, Tools and Critical Aspects. J Pers Med 2022; 12:205. [PMID: 35207693 PMCID: PMC8874397 DOI: 10.3390/jpm12020205] [Citation(s) in RCA: 11] [Impact Index Per Article: 5.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/13/2021] [Revised: 01/13/2022] [Accepted: 01/14/2022] [Indexed: 12/10/2022] Open
Abstract
Targeted radiation therapy (TRT) is a strategy increasingly adopted for the treatment of different types of cancer. The urge for optimization, as stated by the European Council Directive (2013/59/EURATOM), requires the implementation of a personalized dosimetric approach, similar to what already happens in external beam radiation therapy (EBRT). The purpose of this paper is to provide a thorough introduction to the field of personalized dosimetry in TRT, explaining its rationale in the context of optimization and describing the currently available methodologies. After listing the main therapies currently employed, the clinical workflow for the absorbed dose calculation is described, based on works of the most experienced authors in the literature and recent guidelines. Moreover, the widespread software packages for internal dosimetry are presented and critical aspects discussed. Overall, a selection of the most important and recent articles about this topic is provided.
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Affiliation(s)
- Rachele Danieli
- Dipartimento di Fisica, Università degli Studi di Pavia, Via Bassi 6, 27100 Pavia, Italy;
| | - Alessia Milano
- Fondazione Policlinico Universitario A. Gemelli IRCCS, Largo F. Vito 1, 00168 Roma, Italy;
- Dipartimento di Diagnostica per Immagini, Radioterapia Oncologica ed Ematologia, Università Cattolica del Sacro Cuore, Largo F. Vito 1, 00168 Roma, Italy
| | - Salvatore Gallo
- Dipartimento di Fisica “Aldo Pontremoli”, Università degli Studi di Milano, Via Celoria 16, 20133 Milano, Italy; (S.G.); (I.V.)
- INFN Sezione di Milano, Via Celoria 16, 20133 Milano, Italy
| | - Ivan Veronese
- Dipartimento di Fisica “Aldo Pontremoli”, Università degli Studi di Milano, Via Celoria 16, 20133 Milano, Italy; (S.G.); (I.V.)
- INFN Sezione di Milano, Via Celoria 16, 20133 Milano, Italy
| | - Alessandro Lascialfari
- INFN-Pavia Unit, Department of Physics, University of Pavia, Via Bassi 6, 27100 Pavia, Italy;
| | - Luca Indovina
- Fondazione Policlinico Universitario A. Gemelli IRCCS, Largo F. Vito 1, 00168 Roma, Italy;
| | - Francesca Botta
- Medical Physics Unit, European Institute of Oncology IRCCS, Via Giuseppe Ripamonti 435, 20141 Milano, Italy; (F.B.); (M.F.)
| | - Mahila Ferrari
- Medical Physics Unit, European Institute of Oncology IRCCS, Via Giuseppe Ripamonti 435, 20141 Milano, Italy; (F.B.); (M.F.)
| | - Alessandro Cicchetti
- Prostate Cancer Program, Fondazione IRCCS Istituto Nazionale dei Tumori, Via Giacomo Venezian, 1, 20133 Milano, Italy;
| | - Davide Raspanti
- Temasinergie S.p.A., Via Marcello Malpighi 120, 48018 Faenza, Italy;
| | - Marta Cremonesi
- Radiation Research Unit, European Institute of Oncology IRCCS, Via Giuseppe Ripamonti 435, 20141 Milano, Italy;
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15
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Di Basilio F, Esposisto G, Monoscalco L, Giansanti D. The Artificial Intelligence in Digital Radiology: Part 2: Towards an Investigation of acceptance and consensus on the Insiders. Healthcare (Basel) 2022; 10:153. [PMID: 35052316 PMCID: PMC8775988 DOI: 10.3390/healthcare10010153] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/09/2021] [Revised: 12/19/2021] [Accepted: 01/10/2022] [Indexed: 02/04/2023] Open
Abstract
Background. The study deals with the introduction of the artificial intelligence in digital radiology. There is a growing interest in this area of scientific research in acceptance and consensus studies involving both insiders and the public, based on surveys focused mainly on single professionals. Purpose. The goal of the study is to perform a contemporary investigation on the acceptance and the consensus of the three key professional figures approaching in this field of application: (1) Medical specialists in image diagnostics: the medical specialists (MS)s; (2) experts in physical imaging processes: the medical physicists (MP)s; (3) AI designers: specialists of applied sciences (SAS)s. Methods. Participants (MSs = 92: 48 males/44 females, averaged age 37.9; MPs = 91: 43 males/48 females, averaged age 36.1; SAS = 90: 47 males/43 females, averaged age 37.3) were properly recruited based on specific training. An electronic survey was designed and submitted to the participants with a wide range questions starting from the training and background up to the different applications of the AI and the environment of application. Results. The results show that generally, the three professionals show (a) a high degree of encouraging agreement on the introduction of AI both in imaging and in non-imaging applications using both standalone applications and/or mHealth/eHealth, and (b) a different consent on AI use depending on the training background. Conclusions. The study highlights the usefulness of focusing on both the three key professionals and the usefulness of the investigation schemes facing a wide range of issues. The study also suggests the importance of different methods of administration to improve the adhesion and the need to continue these investigations both with federated and specific initiatives.
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Affiliation(s)
- Francesco Di Basilio
- Facoltà di Medicina e Psicologia, Sapienza University, Piazzale Aldo Moro, 00185 Rome, Italy; (F.D.B.); (G.E.)
| | - Gianluca Esposisto
- Facoltà di Medicina e Psicologia, Sapienza University, Piazzale Aldo Moro, 00185 Rome, Italy; (F.D.B.); (G.E.)
| | - Lisa Monoscalco
- Faculty of Engineering, Tor Vergata University, 00133 Rome, Italy;
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16
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Prime Time for Artificial Intelligence in Interventional Radiology. Cardiovasc Intervent Radiol 2022; 45:283-289. [PMID: 35031822 PMCID: PMC8921296 DOI: 10.1007/s00270-021-03044-4] [Citation(s) in RCA: 8] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 09/05/2021] [Accepted: 11/28/2021] [Indexed: 12/16/2022]
Abstract
Machine learning techniques, also known as artificial intelligence (AI), is about to dramatically change workflow and diagnostic capabilities in diagnostic radiology. The interest in AI in Interventional Radiology is rapidly gathering pace. With this early interest in AI in procedural medicine, IR could lead the way to AI research and clinical applications for all interventional medical fields. This review will address an overview of machine learning, radiomics and AI in the field of interventional radiology, enumerating the possible applications of such techniques, while also describing techniques to overcome the challenge of limited data when applying these techniques in interventional radiology. Lastly, this review will address common errors in research in this field and suggest pathways for those interested in learning and becoming involved about AI.
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17
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Retico A, Avanzo M, Boccali T, Bonacorsi D, Botta F, Cuttone G, Martelli B, Salomoni D, Spiga D, Trianni A, Stasi M, Iori M, Talamonti C. Enhancing the impact of Artificial Intelligence in Medicine: A joint AIFM-INFN Italian initiative for a dedicated cloud-based computing infrastructure. Phys Med 2021; 91:140-150. [PMID: 34801873 DOI: 10.1016/j.ejmp.2021.10.005] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 05/26/2021] [Revised: 10/04/2021] [Accepted: 10/05/2021] [Indexed: 12/23/2022] Open
Abstract
Artificial Intelligence (AI) techniques have been implemented in the field of Medical Imaging for more than forty years. Medical Physicists, Clinicians and Computer Scientists have been collaborating since the beginning to realize software solutions to enhance the informative content of medical images, including AI-based support systems for image interpretation. Despite the recent massive progress in this field due to the current emphasis on Radiomics, Machine Learning and Deep Learning, there are still some barriers to overcome before these tools are fully integrated into the clinical workflows to finally enable a precision medicine approach to patients' care. Nowadays, as Medical Imaging has entered the Big Data era, innovative solutions to efficiently deal with huge amounts of data and to exploit large and distributed computing resources are urgently needed. In the framework of a collaboration agreement between the Italian Association of Medical Physicists (AIFM) and the National Institute for Nuclear Physics (INFN), we propose a model of an intensive computing infrastructure, especially suited for training AI models, equipped with secure storage systems, compliant with data protection regulation, which will accelerate the development and extensive validation of AI-based solutions in the Medical Imaging field of research. This solution can be developed and made operational by Physicists and Computer Scientists working on complementary fields of research in Physics, such as High Energy Physics and Medical Physics, who have all the necessary skills to tailor the AI-technology to the needs of the Medical Imaging community and to shorten the pathway towards the clinical applicability of AI-based decision support systems.
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Affiliation(s)
- Alessandra Retico
- National Institute for Nuclear Physics (INFN), Pisa Division, 56127 Pisa, Italy
| | - Michele Avanzo
- Medical Physics Department, Centro di Riferimento Oncologico di Aviano (CRO) IRCCS, 33081 Aviano, Italy
| | - Tommaso Boccali
- National Institute for Nuclear Physics (INFN), Pisa Division, 56127 Pisa, Italy
| | - Daniele Bonacorsi
- University of Bologna, 40126 Bologna, Italy; INFN, Bologna Division, 40126 Bologna, Italy
| | - Francesca Botta
- Medical Physics Unit, Istituto Europeo di oncologia IRCCS, 20141 Milan, Italy
| | - Giacomo Cuttone
- INFN, Southern National Laboratory (LNS), 95123 Catania, Italy
| | | | | | | | - Annalisa Trianni
- Medical Physics Unit, Ospedale Santa Chiara APSS, 38122 Trento, Italy
| | - Michele Stasi
- Medical Physics Unit, A.O. Ordine Mauriziano di Torino, 10128 Torino, Italy
| | - Mauro Iori
- Medical Physics Unit, Azienda USL-IRCCS di Reggio Emilia, 42122 Reggio Emilia, Italy.
| | - Cinzia Talamonti
- Department Biomedical Experimental and Clinical Science "Mario Serio", University of Florence, 50134 Florence, Italy; INFN, Florence Division, 50134 Florence, Italy
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18
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Giovagnoli MR, Ciucciarelli S, Castrichella L, Giansanti D. Artificial Intelligence in Digital Pathology: What Is the Future? Part 2: An Investigation on the Insiders. Healthcare (Basel) 2021; 9:healthcare9101347. [PMID: 34683027 PMCID: PMC8544344 DOI: 10.3390/healthcare9101347] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/15/2021] [Revised: 10/08/2021] [Accepted: 10/09/2021] [Indexed: 01/01/2023] Open
Abstract
Motivation: This study deals with the introduction of artificial intelligence (AI) in digital pathology (DP). The study starts from the highlights of a companion paper. Objective: The aim was to investigate the consensus and acceptance of the insiders on this issue. Procedure: An electronic survey based on the standardized package Microsoft Forms (Microsoft, Redmond, WA, USA) was proposed to a sample of biomedical laboratory technicians (149 admitted in the study, 76 males, 73 females, mean age 44.2 years). Results: The survey showed no criticality. It highlighted (a) the good perception of the basic training on both groups, and (b) a uniformly low perceived knowledge of AI (as arisen from the graded questions). Expectations, perceived general impact, perceived changes in the work-flow, and worries clearly emerged in the study. Conclusions: The of AI in DP is an unstoppable process, as well as the increase of the digitalization in the health domain. Stakeholders must not look with suspicion towards AI, which can represent an important resource, but should invest in monitoring and consensus training initiatives based also on electronic surveys.
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Affiliation(s)
- Maria Rosaria Giovagnoli
- Facoltà di Medicina e Psicologia, Università Sapienza Roma, Piazzale Aldo Moro, 00185 Rome, Italy; (M.R.G.); (S.C.); (L.C.)
| | - Sara Ciucciarelli
- Facoltà di Medicina e Psicologia, Università Sapienza Roma, Piazzale Aldo Moro, 00185 Rome, Italy; (M.R.G.); (S.C.); (L.C.)
| | - Livia Castrichella
- Facoltà di Medicina e Psicologia, Università Sapienza Roma, Piazzale Aldo Moro, 00185 Rome, Italy; (M.R.G.); (S.C.); (L.C.)
| | - Daniele Giansanti
- Centre Tisp, Istituto Superiore di Sanità, 00161 Rome, Italy
- Correspondence: ; Tel.: +39-06-49902701
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19
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A Review on Recent Progress in Machine Learning and Deep Learning Methods for Cancer Classification on Gene Expression Data. Processes (Basel) 2021. [DOI: 10.3390/pr9081466] [Citation(s) in RCA: 11] [Impact Index Per Article: 3.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/14/2022] Open
Abstract
Data-driven model with predictive ability are important to be used in medical and healthcare. However, the most challenging task in predictive modeling is to construct a prediction model, which can be addressed using machine learning (ML) methods. The methods are used to learn and trained the model using a gene expression dataset without being programmed explicitly. Due to the vast amount of gene expression data, this task becomes complex and time consuming. This paper provides a recent review on recent progress in ML and deep learning (DL) for cancer classification, which has received increasing attention in bioinformatics and computational biology. The development of cancer classification methods based on ML and DL is mostly focused on this review. Although many methods have been applied to the cancer classification problem, recent progress shows that most of the successful techniques are those based on supervised and DL methods. In addition, the sources of the healthcare dataset are also described. The development of many machine learning methods for insight analysis in cancer classification has brought a lot of improvement in healthcare. Currently, it seems that there is highly demanded further development of efficient classification methods to address the expansion of healthcare applications.
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20
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Santos JC, Wong JHD, Pallath V, Ng KH. The perceptions of medical physicists towards relevance and impact of artificial intelligence. Phys Eng Sci Med 2021; 44:833-841. [PMID: 34283393 DOI: 10.1007/s13246-021-01036-9] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/21/2021] [Accepted: 07/13/2021] [Indexed: 01/04/2023]
Abstract
Artificial intelligence (AI) is an innovative tool with the potential to impact medical physicists' clinical practices, research, and the profession. The relevance of AI and its impact on the clinical practice and routine of professionals in medical physics were evaluated by medical physicists and researchers in this field. An online survey questionnaire was designed for distribution to professionals and students in medical physics around the world. In addition to demographics questions, we surveyed opinions on the role of AI in medical physicists' practices, the possibility of AI threatening/disrupting the medical physicists' practices and career, the need for medical physicists to acquire knowledge on AI, and the need for teaching AI in postgraduate medical physics programmes. The level of knowledge of medical physicists on AI was also consulted. A total of 1019 respondents from 94 countries participated. More than 85% of the respondents agreed that AI would play an essential role in medical physicists' practices. AI should be taught in the postgraduate medical physics programmes, and that more applications such as quality control (QC), treatment planning would be performed by AI. Half of the respondents thought AI would not threaten/disrupt the medical physicists' practices. AI knowledge was mainly acquired through self-taught and work-related activities. Nonetheless, many (40%) reported that they have no skill in AI. The general perception of medical physicists was that AI is here to stay, influencing our practices. Medical physicists should be prepared with education and training for this new reality.
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Affiliation(s)
- Josilene C Santos
- Department of Nuclear Physics, Federal University of Rio de Janeiro, Rio de Janeiro, Brazil
| | - Jeannie Hsiu Ding Wong
- Department of Biomedical Imaging, Faculty of Medicine, Universiti Malaya, Kuala Lumpur, Malaysia.
| | - Vinod Pallath
- Medical Education and Research Development Unit, Faculty of Medicine, Universiti Malaya, Kuala Lumpur, Malaysia
| | - Kwan Hoong Ng
- Department of Biomedical Imaging, Faculty of Medicine, Universiti Malaya, Kuala Lumpur, Malaysia
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21
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Giovagnoli MR, Giansanti D. Artificial Intelligence in Digital Pathology: What Is the Future? Part 1: From the Digital Slide Onwards. Healthcare (Basel) 2021; 9:healthcare9070858. [PMID: 34356236 PMCID: PMC8304979 DOI: 10.3390/healthcare9070858] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/14/2021] [Revised: 07/01/2021] [Accepted: 07/04/2021] [Indexed: 12/13/2022] Open
Abstract
This commentary aims to address the field of Artificial intelligence (AI) in Digital Pathology (DP) both in terms of the global situation and research perspectives. It has four polarities. First, it revisits the evolutions of digital pathology with particular care to the two fields of the digital cytology and the digital histology. Second, it illustrates the main fields in the employment of AI in DP. Third, it looks at the future directions of the research challenges from both a clinical and technological point of view. Fourth, it discusses the transversal problems among these challenges and implications and introduces the immediate work to implement.
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Affiliation(s)
| | - Daniele Giansanti
- Centre Tisp, Istituto Superiore di Sanità, 00161 Roma, Italy
- Correspondence: ; Tel.: +39-06-49902701
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22
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Seah J, Brady Z, Ewert K, Law M. Artificial intelligence in medical imaging: implications for patient radiation safety. Br J Radiol 2021; 94:20210406. [PMID: 33989035 DOI: 10.1259/bjr.20210406] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/21/2022] Open
Abstract
Artificial intelligence, including deep learning, is currently revolutionising the field of medical imaging, with far reaching implications for almost every facet of diagnostic imaging, including patient radiation safety. This paper introduces basic concepts in deep learning and provides an overview of its recent history and its application in tomographic reconstruction as well as other applications in medical imaging to reduce patient radiation dose, as well as a brief description of previous tomographic reconstruction techniques. This review also describes the commonly used deep learning techniques as applied to tomographic reconstruction and draws parallels to current reconstruction techniques. Finally, this paper reviews some of the estimated dose reductions in CT and positron emission tomography in the recent literature enabled by deep learning, as well as some of the potential problems that may be encountered such as the obscuration of pathology, and highlights the need for additional clinical reader studies from the imaging community.
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Affiliation(s)
- Jarrel Seah
- Department of Radiology, Alfred Health, Melbourne, Australia.,Department of Neuroscience, Monash University, Melbourne, Australia.,Annalise.AI, Sydney, Australia
| | - Zoe Brady
- Department of Radiology, Alfred Health, Melbourne, Australia.,Department of Neuroscience, Monash University, Melbourne, Australia
| | - Kyle Ewert
- Department of Radiology, Alfred Health, Melbourne, Australia
| | - Meng Law
- Department of Radiology, Alfred Health, Melbourne, Australia.,Department of Neuroscience, Monash University, Melbourne, Australia.,Department of Electrical and Computer Systems Engineering, Monash University, Melbourne, Australia
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23
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A Roadmap towards Breast Cancer Therapies Supported by Explainable Artificial Intelligence. APPLIED SCIENCES-BASEL 2021. [DOI: 10.3390/app11114881] [Citation(s) in RCA: 9] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/24/2022]
Abstract
In recent years personalized medicine reached an increasing importance, especially in the design of oncological therapies. In particular, the development of patients’ profiling strategies suggests the possibility of promising rewards. In this work, we present an explainable artificial intelligence (XAI) framework based on an adaptive dimensional reduction which (i) outlines the most important clinical features for oncological patients’ profiling and (ii), based on these features, determines the profile, i.e., the cluster a patient belongs to. For these purposes, we collected a cohort of 267 breast cancer patients. The adopted dimensional reduction method determines the relevant subspace where distances among patients are used by a hierarchical clustering procedure to identify the corresponding optimal categories. Our results demonstrate how the molecular subtype is the most important feature for clustering. Then, we assessed the robustness of current therapies and guidelines; our findings show a striking correspondence between available patients’ profiles determined in an unsupervised way and either molecular subtypes or therapies chosen according to guidelines, which guarantees the interpretability characterizing explainable approaches to machine learning techniques. Accordingly, our work suggests the possibility to design data-driven therapies to emphasize the differences observed among the patients.
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24
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Artificial intelligence applications in medical imaging: A review of the medical physics research in Italy. Phys Med 2021; 83:221-241. [DOI: 10.1016/j.ejmp.2021.04.010] [Citation(s) in RCA: 23] [Impact Index Per Article: 7.7] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 12/05/2020] [Revised: 03/31/2021] [Accepted: 04/03/2021] [Indexed: 02/06/2023] Open
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