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Bando M, Chiba H, Miyazaki Y, Suda T. Current challenges in the diagnosis and management of idiopathic pulmonary fibrosis in Japan. Respir Investig 2024; 62:785-793. [PMID: 38996779 DOI: 10.1016/j.resinv.2024.06.006] [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: 03/19/2024] [Revised: 05/17/2024] [Accepted: 06/21/2024] [Indexed: 07/14/2024]
Abstract
Idiopathic pulmonary fibrosis (IPF) is the archetypal interstitial lung disease. It is a chronic progressive condition that is challenging to manage as the clinical course of the disease is often difficult to predict. The prevalence of IPF is rising globally and in Japan, where it is estimated to affect 27 individuals per 100,000 of the population. Greater patient numbers and the poor prognosis associated with IPF diagnosis mean that there is a growing need for disease management approaches that can slow or even reverse disease progression and improve survival. Considerable progress has been made in recent years, with the approval of two antifibrotic therapies for IPF (pirfenidone and nintedanib), the availability of Japanese treatment guidelines, and the creation of global and Japanese disease registries. Despite this, significant unmet needs remain with respect to the diagnosis, treatment, and management of this complex disease. Each of these challenges will be discussed in this review, including making a timely and differential diagnosis of IPF, uptake and adherence to antifibrotic therapy, patient access to pulmonary rehabilitation, lung transplantation and palliative care, and optimal strategies for monitoring and staging disease progression, with a particular focus on the status in Japan. In addition, the review will reflect upon how ongoing research, clinical trials of novel therapies, and technologic advancements (including artificial intelligence, biomarkers, and genomic classification) may help address these challenges in the future.
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Affiliation(s)
- Masashi Bando
- Division of Pulmonary Medicine, Department of Medicine, Jichi Medical University, 3311-1 Yakushiji, Shimotsuke, Tochigi, 329-0498, Japan.
| | - Hirofumi Chiba
- Department of Respiratory Medicine and Allergology, Sapporo Medical University School of Medicine, South-1 West-16, Chuo-ku, Sapporo, 060-8543, Japan
| | - Yasunari Miyazaki
- Tokyo Medical and Dental University, 1-5-45, Yushima, Bunkyo-ku, Tokyo, 113-8510, Japan
| | - Takafumi Suda
- Hamamatsu University School of Medicine, Hamamatsu, Shizuoka, 431-3192, Japan
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Park S, Kim JH, Woo JH, Park SY, Cha YK, Chung MJ. Pixel-Wise Interstitial Lung Disease Interval Change Analysis: A Quantitative Evaluation Method for Chest Radiographs Using Weakly Supervised Learning. Bioengineering (Basel) 2024; 11:562. [PMID: 38927798 PMCID: PMC11201158 DOI: 10.3390/bioengineering11060562] [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: 04/30/2024] [Revised: 05/29/2024] [Accepted: 05/30/2024] [Indexed: 06/28/2024] Open
Abstract
Interstitial lung disease (ILD) is characterized by progressive pathological changes that require timely and accurate diagnosis. The early detection and progression assessment of ILD are important for effective management. This study introduces a novel quantitative evaluation method utilizing chest radiographs to analyze pixel-wise changes in ILD. Using a weakly supervised learning framework, the approach incorporates the contrastive unpaired translation model and a newly developed ILD extent scoring algorithm for more precise and objective quantification of disease changes than conventional visual assessments. The ILD extent score calculated through this method demonstrated a classification accuracy of 92.98% between ILD and normal classes. Additionally, using an ILD follow-up dataset for interval change analysis, this method assessed disease progression with an accuracy of 85.29%. These findings validate the reliability of the ILD extent score as a tool for ILD monitoring. The results of this study suggest that the proposed quantitative method may improve the monitoring and management of ILD.
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Affiliation(s)
- Subin Park
- Department of Health Sciences es and Technology, SAIHST, Sungkyunkwan University, Seoul 06351, Republic of Korea; (S.P.)
| | - Jong Hee Kim
- Department of Radiology, Samsung Medical Center, Sungkyunkwan University School of Medicine, Seoul 0631, Republic of Korea; (J.H.K.)
| | - Jung Han Woo
- Department of Radiology, Samsung Medical Center, Sungkyunkwan University School of Medicine, Seoul 0631, Republic of Korea; (J.H.K.)
| | - So Young Park
- Department of Health Sciences es and Technology, SAIHST, Sungkyunkwan University, Seoul 06351, Republic of Korea; (S.P.)
| | - Yoon Ki Cha
- Department of Radiology, Samsung Medical Center, Sungkyunkwan University School of Medicine, Seoul 0631, Republic of Korea; (J.H.K.)
| | - Myung Jin Chung
- Department of Radiology, Samsung Medical Center, Sungkyunkwan University School of Medicine, Seoul 0631, Republic of Korea; (J.H.K.)
- Medical AI Research Center, Research Institute for Future Medicine, Samsung Medical Center, Seoul 06351, Republic of Korea
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3
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Al-Anazi S, Al-Omari A, Alanazi S, Marar A, Asad M, Alawaji F, Alwateid S. Artificial intelligence in respiratory care: Current scenario and future perspective. Ann Thorac Med 2024; 19:117-130. [PMID: 38766378 PMCID: PMC11100474 DOI: 10.4103/atm.atm_192_23] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 08/13/2023] [Revised: 09/08/2023] [Accepted: 09/11/2023] [Indexed: 05/22/2024] Open
Abstract
BACKGROUND This narrative review aims to explore the current state and future perspective of artificial intelligence (AI) in respiratory care. The objective is to provide insights into the potential impact of AI in this field. METHODS A comprehensive analysis of relevant literature and research studies was conducted to examine the applications of AI in respiratory care and identify areas of advancement. The analysis included studies on remote monitoring, early detection, smart ventilation systems, and collaborative decision-making. RESULTS The obtained results highlight the transformative potential of AI in respiratory care. AI algorithms have shown promising capabilities in enabling tailored treatment plans based on patient-specific data. Remote monitoring using AI-powered devices allows for real-time feedback to health-care providers, enhancing patient care. AI algorithms have also demonstrated the ability to detect respiratory conditions at an early stage, leading to timely interventions and improved outcomes. Moreover, AI can optimize mechanical ventilation through continuous monitoring, enhancing patient comfort and reducing complications. Collaborative AI systems have the potential to augment the expertise of health-care professionals, leading to more accurate diagnoses and effective treatment strategies. CONCLUSION By improving diagnosis, AI has the potential to revolutionize respiratory care, treatment planning, and patient monitoring. While challenges and ethical considerations remain, the transformative impact of AI in this domain cannot be overstated. By leveraging the advancements and insights from this narrative review, health-care professionals and researchers can continue to harness the power of AI to improve patient outcomes and enhance respiratory care practices. IMPROVEMENTS Based on the findings, future research should focus on refining AI algorithms to enhance their accuracy, reliability, and interpretability. In addition, attention should be given to addressing ethical considerations, ensuring data privacy, and establishing regulatory frameworks to govern the responsible implementation of AI in respiratory care.
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Affiliation(s)
- Saad Al-Anazi
- Lead Clincial Appliaction AzeerTrade (Lowenstein Medical Company), Riyadh, Saudi Arabia
| | - Awad Al-Omari
- Department of Intensive Care, Dr. Sulaiman Al-Habib Group Hospitals, Riyadh, Saudi Arabia
| | - Safug Alanazi
- Intensivist, Al Hammadi Hospital, Riyadh, Saudi Arabia
| | - Aqeelah Marar
- Respiratory Care Administration, King Fahad Medical City, Riyadh, Saudi Arabia
| | - Mohammed Asad
- Department of Emergency, Dr. Sulaiman Al-Habib Group Hospitals, Riyadh, Saudi Arabia
| | - Fadi Alawaji
- Ar Rass General Hospital, Qassim Health Cluster, Senior Laboratory Specialist, Rass Region, Qassim City, Saudi Arabia
| | - Salman Alwateid
- Respiratory Care Administration, King Fahad Medical City, Riyadh, Saudi Arabia
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Sindhu A, Jadhav U, Ghewade B, Bhanushali J, Yadav P. Revolutionizing Pulmonary Diagnostics: A Narrative Review of Artificial Intelligence Applications in Lung Imaging. Cureus 2024; 16:e57657. [PMID: 38707160 PMCID: PMC11070215 DOI: 10.7759/cureus.57657] [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: 03/18/2024] [Accepted: 04/04/2024] [Indexed: 05/07/2024] Open
Abstract
Artificial intelligence (AI) has emerged as a transformative force in healthcare, particularly in pulmonary diagnostics. This comprehensive review explores the impact of AI on revolutionizing lung imaging, focusing on its applications in detecting abnormalities, diagnosing pulmonary conditions, and predicting disease prognosis. We provide an overview of traditional pulmonary diagnostic methods and highlight the importance of accurate and efficient lung imaging for early intervention and improved patient outcomes. Through the lens of AI, we examine machine learning algorithms, deep learning techniques, and natural language processing for analyzing radiology reports. Case studies and examples showcase the successful implementation of AI in pulmonary diagnostics, alongside challenges faced and lessons learned. Finally, we discuss future directions, including integrating AI into clinical workflows, ethical considerations, and the need for further research and collaboration in this rapidly evolving field. This review underscores the transformative potential of AI in enhancing the accuracy, efficiency, and accessibility of pulmonary healthcare.
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Affiliation(s)
- Arman Sindhu
- Respiratory Medicine, Jawaharlal Nehru Medical College, Datta Meghe Institute of Higher Education and Research, Wardha, IND
| | - Ulhas Jadhav
- Respiratory Medicine, Jawaharlal Nehru Medical College, Datta Meghe Institute of Higher Education and Research, Wardha, IND
| | - Babaji Ghewade
- Respiratory Medicine, Jawaharlal Nehru Medical College, Datta Meghe Institute of Higher Education and Research, Wardha, IND
| | - Jay Bhanushali
- Respiratory Medicine, Jawaharlal Nehru Medical College, Datta Meghe Institute of Higher Education and Research, Wardha, IND
| | - Pallavi Yadav
- Obstetrics and Gynecology, Jawaharlal Nehru Medical College, Datta Meghe Institute of Higher Education and Research, Wardha, IND
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Wanika L, Evans ND, Johnson M, Tomkinson H, Chappell MJ. In vitro PK/PD modeling of tyrosine kinase inhibitors in non-small cell lung cancer cell lines. Clin Transl Sci 2024; 17:e13714. [PMID: 38477045 PMCID: PMC10933606 DOI: 10.1111/cts.13714] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/08/2023] [Revised: 11/22/2023] [Accepted: 12/14/2023] [Indexed: 03/14/2024] Open
Abstract
Tyrosine kinase inhibitors (TKIs) are routinely prescribed for the treatment of non-small cell lung cancer (NSCLC). As with all medications, patients can experience adverse events due to TKIs. Unfortunately, the relationship between many TKIs and the occurrence of certain adverse events remains unclear. There are limited in vivo studies which focus on TKIs and their effects on different regulation pathways. Many in vitro studies, however, that investigate the effects of TKIs observe additional changes, such as changes in gene activations or protein expressions. These studies could potentially help to gain greater understanding of the mechanisms for TKI induced adverse events. However, in order to utilize these pathways in a pharmacokinetic/pharmacodynamic (PK/PD) framework, an in vitro PK/PD model needs to be developed, in order to characterize the effects of TKIs in NSCLC cell lines. Through the use of ordinary differential equations, cell viability data and nonlinear mixed effects modeling, an in vitro TKI PK/PD model was developed with estimated PK and PD parameter values for the TKIs alectinib, crizotinib, erlotinib, and gefitinib. The relative standard errors for the population parameters are all less than 25%. The inclusion of random effects enabled the model to predict individual parameter values which provided a closer fit to the observed response. It is hoped that this model can be extended to include in vitro data of certain pathways that may potentially be linked with adverse events and provide a better understanding of TKI-induced adverse events.
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Affiliation(s)
- Linda Wanika
- School of EngineeringUniversity of WarwickCoventryUK
| | - Neil D. Evans
- School of EngineeringUniversity of WarwickCoventryUK
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Rea G, Bocchino M, Lieto R, Ledda RE, D’Alto M, Sperandeo M, Lucci R, Pasquinelli P, Sanduzzi Zamparelli S, Bocchini G, Valente T, Sica G. The Unveiled Triad: Clinical, Radiological and Pathological Insights into Hypersensitivity Pneumonitis. J Clin Med 2024; 13:797. [PMID: 38337490 PMCID: PMC10856167 DOI: 10.3390/jcm13030797] [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: 11/28/2023] [Revised: 01/10/2024] [Accepted: 01/25/2024] [Indexed: 02/12/2024] Open
Abstract
Hypersensitivity pneumonitis (HP) is a diffuse parenchymal lung disease (DLPD) characterized by complex interstitial lung damage with polymorphic and protean inflammatory aspects affecting lung tissue targets including small airways, the interstitium, alveolar compartments and vascular structures. HP shares clinical and often radiological features with other lung diseases in acute or chronic forms. In its natural temporal evolution, if specific therapy is not initiated promptly, HP leads to progressive fibrotic damage with reduced lung volumes and impaired gas exchange. The prevalence of HP varies considerably worldwide, influenced by factors like imprecise disease classification, diagnostic method limitations for obtaining a confident diagnosis, diagnostic limitations in the correct processing of high-resolution computed tomography (HRCT) radiological parameters, unreliable medical history, diverse geographical conditions, heterogeneous agricultural and industrial practices and occasionally ineffective individual protections regarding occupational exposures and host risk factors. The aim of this review is to present an accurate and detailed 360-degree analysis of HP considering HRCT patterns and the role of the broncho-alveolar lavage (BAL), without neglecting biopsy and anatomopathological aspects and future technological developments that could make the diagnosis of this disease less challenging.
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Affiliation(s)
- Gaetano Rea
- Department of Radiology, Monaldi Hospital, Azienda Ospedaliera dei Colli, 80131 Naples, Italy; (G.R.); (R.L.); (G.B.); (T.V.)
| | - Marialuisa Bocchino
- Department of Clinical Medicine and Surgery, Section of Respiratory Diseases, University Federico II, Monaldi Hospital, Azienda Ospedaliera dei Colli, 80131 Naples, Italy;
| | - Roberta Lieto
- Department of Radiology, Monaldi Hospital, Azienda Ospedaliera dei Colli, 80131 Naples, Italy; (G.R.); (R.L.); (G.B.); (T.V.)
| | - Roberta Eufrasia Ledda
- Section of Radiology, Unit of Surgical Science, Department of Medicine and Surgery (DiMeC), University of Parma, 43121 Parma, Italy;
| | - Michele D’Alto
- Department of Cardiology, University “L. Vanvitelli”, Monaldi Hospital, 80131 Naples, Italy;
| | - Marco Sperandeo
- Interventional Ultrasound Unit, Department of Internal Medicine, IRCCS “Casa Sollievo Della Sofferenza” Hospital, San Giovanni Rotondo, 71013 Foggia, Italy;
| | - Raffaella Lucci
- Department of Pathology, Monaldi Hospital, Azienda Ospedaliera dei Colli, 80131 Naples, Italy;
| | - Patrizio Pasquinelli
- Italian Federation of Pulmonary Fibrosis and Rare Pulmonary Diseases “FIMARP”, 00185 Rome, Italy;
- Department of Pulmonary Diseases, San Camillo-Forlanini Hospital, 00152 Rome, Italy
| | | | - Giorgio Bocchini
- Department of Radiology, Monaldi Hospital, Azienda Ospedaliera dei Colli, 80131 Naples, Italy; (G.R.); (R.L.); (G.B.); (T.V.)
| | - Tullio Valente
- Department of Radiology, Monaldi Hospital, Azienda Ospedaliera dei Colli, 80131 Naples, Italy; (G.R.); (R.L.); (G.B.); (T.V.)
| | - Giacomo Sica
- Department of Radiology, Monaldi Hospital, Azienda Ospedaliera dei Colli, 80131 Naples, Italy; (G.R.); (R.L.); (G.B.); (T.V.)
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Yanina IY, Genin VD, Genina EA, Mudrak DA, Navolokin NA, Bucharskaya AB, Kistenev YV, Tuchin VV. Multimodal Diagnostics of Changes in Rat Lungs after Vaping. Diagnostics (Basel) 2023; 13:3340. [PMID: 37958237 PMCID: PMC10650729 DOI: 10.3390/diagnostics13213340] [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: 07/20/2023] [Revised: 10/09/2023] [Accepted: 10/13/2023] [Indexed: 11/15/2023] Open
Abstract
(1) Background: The use of electronic cigarettes has become widespread in recent years. The use of e-cigarettes leads to milder pathological conditions compared to traditional cigarette smoking. Nevertheless, e-liquid vaping can cause morphological changes in lung tissue, which affects and impairs gas exchange. This work studied the changes in morphological and optical properties of lung tissue under the action of an e-liquid aerosol. To do this, we implemented the "passive smoking" model and created the specified concentration of aerosol of the glycerol/propylene glycol mixture in the chamber with the animal. (2) Methods: In ex vivo studies, the lungs of Wistar rats are placed in the e-liquid for 1 h. For in vivo studies, Wistar rats were exposed to the e-liquid vapor in an aerosol administration chamber. After that, lung tissue samples were examined ex vivo using optical coherence tomography (OCT) and spectrometry with an integrating sphere. Absorption and reduced scattering coefficients were estimated for the control and experimental groups. Histological sections were made according to the standard protocol, followed by hematoxylin and eosin staining. (3) Results: Exposure to e-liquid in ex vivo and aerosol in in vivo studies was found to result in the optical clearing of lung tissue. Histological examination of the lung samples showed areas of emphysematous expansion of the alveoli, thickening of the alveolar septa, and the phenomenon of plasma permeation, which is less pronounced in in vivo studies than for the exposure of e-liquid ex vivo. E-liquid aerosol application allows for an increased resolution and improved imaging of lung tissues using OCT. Spectral studies showed significant differences between the control group and the ex vivo group in the spectral range of water absorption. It can be associated with dehydration of lung tissue owing to the hyperosmotic properties of glycerol and propylene glycol, which are the main components of e-liquids. (4) Conclusions: A decrease in the volume of air in lung tissue and higher packing of its structure under e-liquid vaping causes a better contrast of OCT images compared to intact lung tissue.
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Affiliation(s)
- Irina Yu. Yanina
- Institution of Physics, Saratov State University, 410012 Saratov, Russia; (V.D.G.); (E.A.G.); (V.V.T.)
- Laboratory of Laser Molecular Imaging and Machine Learning, Tomsk State University, 634050 Tomsk, Russia; (A.B.B.); (Y.V.K.)
| | - Vadim D. Genin
- Institution of Physics, Saratov State University, 410012 Saratov, Russia; (V.D.G.); (E.A.G.); (V.V.T.)
- Laboratory of Laser Molecular Imaging and Machine Learning, Tomsk State University, 634050 Tomsk, Russia; (A.B.B.); (Y.V.K.)
- Science Medical Center, Saratov State University, 410012 Saratov, Russia
| | - Elina A. Genina
- Institution of Physics, Saratov State University, 410012 Saratov, Russia; (V.D.G.); (E.A.G.); (V.V.T.)
- Laboratory of Laser Molecular Imaging and Machine Learning, Tomsk State University, 634050 Tomsk, Russia; (A.B.B.); (Y.V.K.)
- Science Medical Center, Saratov State University, 410012 Saratov, Russia
| | - Dmitry A. Mudrak
- Department of Pathological Anatomy, Saratov State Medical University, 410012 Saratov, Russia; (D.A.M.); (N.A.N.)
| | - Nikita A. Navolokin
- Department of Pathological Anatomy, Saratov State Medical University, 410012 Saratov, Russia; (D.A.M.); (N.A.N.)
- Experimental Department, Center for Collective Use of Experimental Oncology, Saratov State Medical University, 410012 Saratov, Russia
- State Healthcare Institution, Saratov City Clinical Hospital No. 1 Named after Yu.Ya. Gordeev, 410017 Saratov, Russia
| | - Alla B. Bucharskaya
- Laboratory of Laser Molecular Imaging and Machine Learning, Tomsk State University, 634050 Tomsk, Russia; (A.B.B.); (Y.V.K.)
- Science Medical Center, Saratov State University, 410012 Saratov, Russia
- Department of Pathological Anatomy, Saratov State Medical University, 410012 Saratov, Russia; (D.A.M.); (N.A.N.)
| | - Yury V. Kistenev
- Laboratory of Laser Molecular Imaging and Machine Learning, Tomsk State University, 634050 Tomsk, Russia; (A.B.B.); (Y.V.K.)
| | - Valery V. Tuchin
- Institution of Physics, Saratov State University, 410012 Saratov, Russia; (V.D.G.); (E.A.G.); (V.V.T.)
- Laboratory of Laser Molecular Imaging and Machine Learning, Tomsk State University, 634050 Tomsk, Russia; (A.B.B.); (Y.V.K.)
- Science Medical Center, Saratov State University, 410012 Saratov, Russia
- Institute of Precision Mechanics and Control, FRC “Saratov Scientific Centre of the Russian Academy of Sciences”, 410028 Saratov, Russia
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