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Mirza FN, Haq Z, Abdi P, Diaz MJ, Libby TJ. Artificial Intelligence for Mohs and Dermatologic Surgery: A Systematic Review and Meta-Analysis. Dermatol Surg 2024:00042728-990000000-00881. [PMID: 38991503 DOI: 10.1097/dss.0000000000004297] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 07/13/2024]
Abstract
BACKGROUND Over the past decade, several studies have shown that potential of artificial intelligence (AI) in dermatology. However, there has yet to be a systematic review evaluating the usage of AI specifically within the field of Mohs micrographic surgery (MMS). OBJECTIVE In this review, we aimed to comprehensively evaluate the current state, efficacy, and future implications of AI when applied to MMS for the treatment of nonmelanoma skin cancers (NMSC). MATERIALS AND METHODS A systematic review and meta-analysis was conducted following PRISMA guidelines across several databases, including PubMed/MEDLINE, Embase, and Cochrane libraries. A predefined protocol was registered in PROSPERO, with literature search involving specific keywords related to AI and Mohs surgery for NMSC. RESULTS From 23 studies evaluated, our results find that AI shows promise as a prediction tool for precisely identifying NMSC in tissue sections during MMS. Furthermore, high AUC and concordance values were also found across the various usages of AI in MMS, including margin control, surgical recommendations, similarity metrics, and in the prediction of stage and construction complexity. CONCLUSION The findings of this review suggest promising potential for AI to enhance the accuracy and efficiency of Mohs surgery, particularly for NMSC.
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Affiliation(s)
- Fatima N Mirza
- Department of Dermatology, Warren Alpert Medical School, Brown University, Providence, Rhode Island
| | - Zaim Haq
- Warren Alpert Medical School, Brown University, Providence, Rhode Island
| | - Parsa Abdi
- Memorial University of Newfoundland, Faculty of Medicine, St. Johns, Newfoundland & Labrador, Canada; and
| | - Michael J Diaz
- University of Florida, College of Medicine, Gainesville, Florida
| | - Tiffany J Libby
- Department of Dermatology, Warren Alpert Medical School, Brown University, Providence, Rhode Island
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2
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Trager MH, Gordon ER, Breneman A, Weng C, Samie FH. Artificial intelligence for nonmelanoma skin cancer. Clin Dermatol 2024:S0738-081X(24)00100-7. [PMID: 38925444 DOI: 10.1016/j.clindermatol.2024.06.016] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 06/28/2024]
Abstract
Nonmelanoma skin cancers (NMSCs) are among the top five most common cancers globally. NMSC is an area with great potential for novel application of diagnostic tools including artificial intelligence (AI). In this scoping review, we aimed to describe the applications of AI in the diagnosis and treatment of NMSC. Twenty-nine publications described AI applications to dermatopathology including lesion classification and margin assessment. Twenty-five publications discussed AI use in clinical image analysis, showing that algorithms are not superior to dermatologists and may rely on unbalanced, nonrepresentative, and nontransparent training data sets. Sixteen publications described the use of AI in cutaneous surgery for NMSC including use in margin assessment during excisions and Mohs surgery, as well as predicting procedural complexity. Eleven publications discussed spectroscopy, confocal microscopy, thermography, and the AI algorithms that analyze and interpret their data. Ten publications pertained to AI applications for the discovery and use of NMSC biomarkers. Eight publications discussed the use of smartphones and AI, specifically how they enable clinicians and patients to have increased access to instant dermatologic assessments but with varying accuracies. Five publications discussed large language models and NMSC, including how they may facilitate or hinder patient education and medical decision-making. Three publications pertaining to the skin of color and AI for NMSC discussed concerns regarding limited diverse data sets for the training of convolutional neural networks. AI demonstrates tremendous potential to improve diagnosis, patient and clinician education, and management of NMSC. Despite excitement regarding AI, data sets are often not transparently reported, may include low-quality images, and may not include diverse skin types, limiting generalizability. AI may serve as a tool to increase access to dermatology services for patients in rural areas and save health care dollars. These benefits can only be achieved, however, with consideration of potential ethical costs.
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Affiliation(s)
- Megan H Trager
- Department of Dermatology, Columbia University Irving Medical Center, New York, NY, USA
| | - Emily R Gordon
- Columbia University Vagelos College of Physicians and Surgeons, New York, NY, USA
| | - Alyssa Breneman
- Department of Dermatology, Columbia University Irving Medical Center, New York, NY, USA
| | - Chunhua Weng
- Department of Biomedical Informatics, Columbia University Irving Medical Center, New York, NY, USA
| | - Faramarz H Samie
- Department of Dermatology, Columbia University Irving Medical Center, New York, NY, USA.
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3
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Kamm M, Hildebrandt F, Titze B, Höink AJ, Vorwerk H, Sievert KD, Groetzner J, Titze U. Ex Vivo Fluorescence Confocal Microscopy for Intraoperative Examinations of Lung Tumors as Alternative to Frozen Sections-A Proof-of-Concept Study. Cancers (Basel) 2024; 16:2221. [PMID: 38927926 PMCID: PMC11202023 DOI: 10.3390/cancers16122221] [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: 05/06/2024] [Revised: 06/09/2024] [Accepted: 06/12/2024] [Indexed: 06/28/2024] Open
Abstract
BACKGROUND Intraoperative frozen sections (FS) are frequently used to establish the diagnosis of lung cancer when preoperative examinations are not conclusive. The downside of FS is its resource-intensive nature and the risk of tissue depletion when small lesions are assessed. Ex vivo fluorescence confocal microscopy (FCM) is a novel microimaging method for loss-free examinations of native materials. We tested its suitability for the intraoperative diagnosis of lung tumors. METHODS Samples from 59 lung resection specimens containing 45 carcinomas were examined in the FCM. The diagnostic performance in the evaluation of malignancy and histological typing of lung tumors was evaluated in comparison with FS and the final diagnosis. RESULTS A total of 44/45 (98%) carcinomas were correctly identified as malignant in the FCM. A total of 33/44 (75%) carcinomas were correctly subtyped, which was comparable with the results of FS and conventional histology. Our tests documented the excellent visualization of cytological features of normal tissues and tumors. Compared to FS, FCM was technically less demanding and less personnel intensive. CONCLUSIONS The ex vivo FCM is a fast, effective, and safe method for diagnosing and subtyping lung cancer and is, therefore, a promising alternative to FS. The method preserves the tissue without loss for subsequent examinations, which is an advantage in the diagnosis of small tumors and for biobanking.
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Affiliation(s)
- Max Kamm
- Department of Pathology, Medical School and University Medical Center OWL, Klinikum Lippe, Lung Cancer Center Lippe, Bielefeld University, 32756 Detmold, Germany; (M.K.); (F.H.); (B.T.)
| | - Felix Hildebrandt
- Department of Pathology, Medical School and University Medical Center OWL, Klinikum Lippe, Lung Cancer Center Lippe, Bielefeld University, 32756 Detmold, Germany; (M.K.); (F.H.); (B.T.)
| | - Barbara Titze
- Department of Pathology, Medical School and University Medical Center OWL, Klinikum Lippe, Lung Cancer Center Lippe, Bielefeld University, 32756 Detmold, Germany; (M.K.); (F.H.); (B.T.)
| | - Anna Janina Höink
- Department of Diagnostic and Interventional Radiology, Medical School and University Medical Center OWL, Klinikum Lippe, Lung Cancer Center Lippe, Bielefeld University, 32756 Detmold, Germany;
| | - Hagen Vorwerk
- Department of Pneumology, Respiratory and Sleep Medicine, Klinikum Lippe Lemgo, Lung Cancer Center Lippe, 32657 Lemgo, Germany;
| | - Karl-Dietrich Sievert
- Department of Urology, Medical School and University Medical Center OWL, Klinikum Lippe, Bielefeld University, 32756 Detmold, Germany;
| | - Jan Groetzner
- Department of Thoracic Surgery, Klinikum Lippe Lemgo, Lung Cancer Center Lippe, 32657 Lemgo, Germany;
| | - Ulf Titze
- Department of Pathology, Medical School and University Medical Center OWL, Klinikum Lippe, Lung Cancer Center Lippe, Bielefeld University, 32756 Detmold, Germany; (M.K.); (F.H.); (B.T.)
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Hermosilla P, Soto R, Vega E, Suazo C, Ponce J. Skin Cancer Detection and Classification Using Neural Network Algorithms: A Systematic Review. Diagnostics (Basel) 2024; 14:454. [PMID: 38396492 PMCID: PMC10888121 DOI: 10.3390/diagnostics14040454] [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: 12/23/2023] [Revised: 02/07/2024] [Accepted: 02/10/2024] [Indexed: 02/25/2024] Open
Abstract
In recent years, there has been growing interest in the use of computer-assisted technology for early detection of skin cancer through the analysis of dermatoscopic images. However, the accuracy illustrated behind the state-of-the-art approaches depends on several factors, such as the quality of the images and the interpretation of the results by medical experts. This systematic review aims to critically assess the efficacy and challenges of this research field in order to explain the usability and limitations and highlight potential future lines of work for the scientific and clinical community. In this study, the analysis was carried out over 45 contemporary studies extracted from databases such as Web of Science and Scopus. Several computer vision techniques related to image and video processing for early skin cancer diagnosis were identified. In this context, the focus behind the process included the algorithms employed, result accuracy, and validation metrics. Thus, the results yielded significant advancements in cancer detection using deep learning and machine learning algorithms. Lastly, this review establishes a foundation for future research, highlighting potential contributions and opportunities to improve the effectiveness of skin cancer detection through machine learning.
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Affiliation(s)
- Pamela Hermosilla
- Escuela de Ingeniería Informática, Pontificia Universidad Católica de Valparaíso, Avenida Brasil 2241, Valparaíso 2362807, Chile (E.V.); (C.S.); (J.P.)
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Orte Cano C, Suppa M, del Marmol V. Where Artificial Intelligence Can Take Us in the Management and Understanding of Cancerization Fields. Cancers (Basel) 2023; 15:5264. [PMID: 37958437 PMCID: PMC10649750 DOI: 10.3390/cancers15215264] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/14/2023] [Revised: 09/22/2023] [Accepted: 09/27/2023] [Indexed: 11/15/2023] Open
Abstract
Squamous cell carcinoma and its precursor lesion actinic keratosis are often found together in areas of skin chronically exposed to sun, otherwise called cancerisation fields. The clinical assessment of cancerisation fields and the correct diagnosis of lesions within these fields is usually challenging for dermatologists. The recent adoption of skin cancer diagnostic imaging techniques, particularly LC-OCT, helps clinicians in guiding treatment decisions of cancerization fields in a non-invasive way. The combination of artificial intelligence and non-invasive skin imaging opens up many possibilities as AI can perform tasks impossible for humans in a reasonable amount of time. In this text we review past examples of the application of AI to dermatological images for actinic keratosis/squamous cell carcinoma diagnosis, and we discuss about the prospects of the application of AI for the characterization and management of cancerization fields.
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Affiliation(s)
- Carmen Orte Cano
- Department of Dermatology, Hôpital Erasme, HUB, Université Libre de Bruxelles, 808 Route de Lennik, 1070 Brussels, Belgium
- Department of Dermato-Oncology, Institut Jules Bordet, HUB, Université Libre de Bruxelles, 1070 Brussels, Belgium
| | - Mariano Suppa
- Department of Dermatology, Hôpital Erasme, HUB, Université Libre de Bruxelles, 808 Route de Lennik, 1070 Brussels, Belgium
- Department of Dermato-Oncology, Institut Jules Bordet, HUB, Université Libre de Bruxelles, 1070 Brussels, Belgium
- Groupe d’Imagerie Cutanée Non Invasive (GICNI), Société Française de Dermatologie (SFD), 75008 Paris, France
| | - Véronique del Marmol
- Department of Dermatology, Hôpital Erasme, HUB, Université Libre de Bruxelles, 808 Route de Lennik, 1070 Brussels, Belgium
- Department of Dermato-Oncology, Institut Jules Bordet, HUB, Université Libre de Bruxelles, 1070 Brussels, Belgium
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Verri M, Scarpino S, Naciu AM, Lopez G, Tabacco G, Taffon C, Pilozzi E, Palermo A, Crescenzi A. Real-Time Evaluation of Thyroid Cytology Using New Digital Microscopy Allows for Sample Adequacy Assessment, Morphological Classification, and Supports Molecular Analysis. Cancers (Basel) 2023; 15:4215. [PMID: 37686491 PMCID: PMC10486817 DOI: 10.3390/cancers15174215] [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: 08/08/2023] [Revised: 08/17/2023] [Accepted: 08/19/2023] [Indexed: 09/10/2023] Open
Abstract
Thyroid cytological examination, a key tool in preoperative thyroid nodule evaluation, is specific and accurate; some drawbacks are due to inadequate or indeterminate cytological reports and there is a need for an innovative approach overcoming the limits of traditional cytological diagnostics. Fluorescence laser confocal microscopes (FCM) is a new optical technique for allowing immediate digital imaging of fresh unfixed tissues and real-time assessment of sample adequacy and diagnostic evaluation for small biopsies and cytological samples. Currently, there are no data about the use of FCMs in the field of thyroid nodular pathology. The aims of this study were to test FCM technology for evaluating the adequacy of FNA samples at the time of the procedure and to assess the level of concordance between FCM cytological evaluations, paired conventional cytology, and final surgical histology. The secondary aim was to define the integrity of nucleic acids after FCM evaluation through NGS molecular analysis. Sample adequacy was correctly stated. Comparing FCM evaluation with the final histology, all cases resulting in malignant or suspicious for malignancy at FCM, were confirmed to be carcinomas (PPV 100%). In conclusion, we describe a successful application of FCM in thyroid preoperative cytological evaluation, with advantages in immediate adequacy assessment and diagnostic information, while preserving cellular specimens for permanent morphology and molecular analysis, thus improving timely and accurate patient management.
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Affiliation(s)
- Martina Verri
- Unit of Endocrine Organs and Neuromuscular Pathology, Fondazione Policlinico Universitario Campus Bio-Medico, 00128 Rome, Italy; (C.T.); (A.C.)
| | - Stefania Scarpino
- Pathology Unit, Department of Clinical and Molecular Medicine, Sapienza University, Sant’Andrea University Hospital, 00189 Rome, Italy; (S.S.); (G.L.); (E.P.)
| | - Anda Mihaela Naciu
- Unit of Metabolic Bone and Thyroid Disorders, Fondazione Policlinico Universitario Campus Bio-Medico, 00128 Rome, Italy; (A.M.N.); (G.T.); (A.P.)
| | - Gianluca Lopez
- Pathology Unit, Department of Clinical and Molecular Medicine, Sapienza University, Sant’Andrea University Hospital, 00189 Rome, Italy; (S.S.); (G.L.); (E.P.)
| | - Gaia Tabacco
- Unit of Metabolic Bone and Thyroid Disorders, Fondazione Policlinico Universitario Campus Bio-Medico, 00128 Rome, Italy; (A.M.N.); (G.T.); (A.P.)
| | - Chiara Taffon
- Unit of Endocrine Organs and Neuromuscular Pathology, Fondazione Policlinico Universitario Campus Bio-Medico, 00128 Rome, Italy; (C.T.); (A.C.)
| | - Emanuela Pilozzi
- Pathology Unit, Department of Clinical and Molecular Medicine, Sapienza University, Sant’Andrea University Hospital, 00189 Rome, Italy; (S.S.); (G.L.); (E.P.)
| | - Andrea Palermo
- Unit of Metabolic Bone and Thyroid Disorders, Fondazione Policlinico Universitario Campus Bio-Medico, 00128 Rome, Italy; (A.M.N.); (G.T.); (A.P.)
| | - Anna Crescenzi
- Unit of Endocrine Organs and Neuromuscular Pathology, Fondazione Policlinico Universitario Campus Bio-Medico, 00128 Rome, Italy; (C.T.); (A.C.)
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Torres VC, Hodge S, Levy JJ, Vaickus LJ, Chen EY, LeBouef M, Samkoe KS. Paired-agent imaging as a rapid en face margin screening method in Mohs micrographic surgery. Front Oncol 2023; 13:1196517. [PMID: 37427140 PMCID: PMC10325620 DOI: 10.3389/fonc.2023.1196517] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/29/2023] [Accepted: 06/05/2023] [Indexed: 07/11/2023] Open
Abstract
Background Mohs micrographic surgery is a procedure used for non-melanoma skin cancers that has 97-99% cure rates largely owing to 100% margin analysis enabled by en face sectioning with real-time, iterative histologic assessment. However, the technique is limited to small and aggressive tumors in high-risk areas because the histopathological preparation and assessment is very time intensive. To address this, paired-agent imaging (PAI) can be used to rapidly screen excised specimens and identify tumor positive margins for guided and more efficient microscopic evaluation. Methods A mouse xenograft model of human squamous cell carcinoma (n = 8 mice, 13 tumors) underwent PAI. Targeted (ABY-029, anti-epidermal growth factor receptor (EGFR) affibody molecule) and untargeted (IRDye 680LT carboxylate) imaging agents were simultaneously injected 3-4 h prior to surgical tumor resection. Fluorescence imaging was performed on main, unprocessed excised specimens and en face margins (tissue sections tangential to the deep margin surface). Binding potential (BP) - a quantity proportional to receptor concentration - and targeted fluorescence signal were measured for each, and respective mean and maximum values were analyzed to compare diagnostic ability and contrast. The BP and targeted fluorescence of the main specimen and margin samples were also correlated with EGFR immunohistochemistry (IHC). Results PAI consistently outperformed targeted fluorescence alone in terms of diagnostic ability and contrast-to-variance ratio (CVR). Mean and maximum measures of BP resulted in 100% accuracy, while mean and maximum targeted fluorescence signal offered 97% and 98% accuracy, respectively. Moreover, maximum BP had the greatest average CVR for both main specimen and margin samples (average 1.7 ± 0.4 times improvement over other measures). Fresh tissue margin imaging improved similarity with EGFR IHC volume estimates compared to main specimen imaging in line profile analysis; and margin BP specifically had the strongest concordance (average 3.6 ± 2.2 times improvement over other measures). Conclusions PAI was able to reliably distinguish tumor from normal tissue in fresh en face margin samples using the single metric of maximum BP. This demonstrated the potential for PAI to act as a highly sensitive screening tool to eliminate the extra time wasted on real-time pathological assessment of low-risk margins.
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Affiliation(s)
- Veronica C. Torres
- Thayer School of Engineering, Dartmouth College, Hanover, NH, United States
| | - Sassan Hodge
- Thayer School of Engineering, Dartmouth College, Hanover, NH, United States
| | - Joshua J. Levy
- Geisel School of Medicine, Dartmouth College, Hanover, NH, United States
- Department of Pathology and Laboratory Medicine, Dartmouth Hitchcock Medical Center, Lebanon, NH, United States
- Department of Dermatology, Dartmouth Hitchcock Medical Center, Lebanon, NH, United States
- Quantitative Biomedical Sciences, Geisel School of Medicine, Dartmouth College, Hanover, NH, United States
- Department of Epidemiology, Geisel School of Medicine, Dartmouth College, Hanover, NH, United States
| | - Louis J. Vaickus
- Department of Pathology and Laboratory Medicine, Dartmouth Hitchcock Medical Center, Lebanon, NH, United States
| | - Eunice Y. Chen
- Geisel School of Medicine, Dartmouth College, Hanover, NH, United States
- Department of Surgery, Dartmouth Hitchcock Medical Center, Lebanon, NH, United States
| | - Matthew LeBouef
- Geisel School of Medicine, Dartmouth College, Hanover, NH, United States
- Department of Dermatology, Dartmouth Hitchcock Medical Center, Lebanon, NH, United States
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González S, Gill M, Juarranz Á. Introduction to the Special Issue on "Keratinocyte Carcinomas: Biology and Evolving Non-Invasive Management Paradigms". Cancers (Basel) 2023; 15:cancers15082325. [PMID: 37190253 DOI: 10.3390/cancers15082325] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/20/2023] [Accepted: 03/22/2023] [Indexed: 05/17/2023] Open
Abstract
Keratinocyte carcinomas (KCs) are the most prevalent form of cancer worldwide, and their incidence is rising dramatically, with an increasing trend in recent years [...].
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Affiliation(s)
- Salvador González
- Department of Medicine and Medical Specialties, Alcalá de Henares University, 28805 Madrid, Spain
| | - Melissa Gill
- Department of Medicine and Medical Specialties, Alcalá de Henares University, 28805 Madrid, Spain
- Department of Clinical Pathology and Cancer Diagnostics, Karolinska University Hospital Solna, 17176 Stockholm, Sweden
- Department of Pathology, SUNY Downstate Health Sciences University, Brooklyn, NY 11203, USA
| | - Ángeles Juarranz
- Department of Biology, Faculty of Sciences, Universidad Autónoma de Madrid, 28049 Madrid, Spain
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Atak MF, Farabi B, Navarrete-Dechent C, Rubinstein G, Rajadhyaksha M, Jain M. Confocal Microscopy for Diagnosis and Management of Cutaneous Malignancies: Clinical Impacts and Innovation. Diagnostics (Basel) 2023; 13:diagnostics13050854. [PMID: 36899999 PMCID: PMC10001140 DOI: 10.3390/diagnostics13050854] [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: 12/28/2022] [Revised: 02/10/2023] [Accepted: 02/20/2023] [Indexed: 02/25/2023] Open
Abstract
Cutaneous malignancies are common malignancies worldwide, with rising incidence. Most skin cancers, including melanoma, can be cured if diagnosed correctly at an early stage. Thus, millions of biopsies are performed annually, posing a major economic burden. Non-invasive skin imaging techniques can aid in early diagnosis and save unnecessary benign biopsies. In this review article, we will discuss in vivo and ex vivo confocal microscopy (CM) techniques that are currently being utilized in dermatology clinics for skin cancer diagnosis. We will discuss their current applications and clinical impact. Additionally, we will provide a comprehensive review of the advances in the field of CM, including multi-modal approaches, the integration of fluorescent targeted dyes, and the role of artificial intelligence for improved diagnosis and management.
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Affiliation(s)
- Mehmet Fatih Atak
- Department of Dermatology, New York Medical College, Metropolitan Hospital, New York, NY 10029, USA
| | - Banu Farabi
- Department of Dermatology, New York Medical College, Metropolitan Hospital, New York, NY 10029, USA
| | - Cristian Navarrete-Dechent
- Department of Dermatology, Escuela de Medicina, Pontificia Universidad Catolica de Chile, Santiago 8331150, Chile
| | | | - Milind Rajadhyaksha
- Dermatology Service, Department of Medicine, Memorial Sloan Kettering Cancer Center, New York, NY 10065, USA
| | - Manu Jain
- Dermatology Service, Department of Medicine, Memorial Sloan Kettering Cancer Center, New York, NY 10065, USA
- Dermatology Service, Department of Medicine, Weill Cornell Medicine, New York, NY 10021, USA
- Correspondence: ; Tel.: +1-(646)-608-3562
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Song W, Liu Y, Qiu L, Qing J, Li A, Zhao Y, Li Y, Li R, Zhou X. Machine learning-based warning model for chronic kidney disease in individuals over 40 years old in underprivileged areas, Shanxi Province. Front Med (Lausanne) 2023; 9:930541. [PMID: 36698845 PMCID: PMC9868668 DOI: 10.3389/fmed.2022.930541] [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: 04/28/2022] [Accepted: 12/19/2022] [Indexed: 01/11/2023] Open
Abstract
Introduction Chronic kidney disease (CKD) is a progressive disease with high incidence but early imperceptible symptoms. Since China's rural areas are subject to inadequate medical check-ups and single disease screening programme, it could easily translate into end-stage renal failure. This study aimed to construct an early warning model for CKD tailored to impoverished areas by employing machine learning (ML) algorithms with easily accessible parameters from ten rural areas in Shanxi Province, thereby, promoting a forward shift of treatment time and improving patients' quality of life. Methods From April to November 2019, CKD opportunistic screening was carried out in 10 rural areas in Shanxi Province. First, general information, physical examination data, blood and urine specimens were collected from 13,550 subjects. Afterward, feature selection of explanatory variables was performed using LASSO regression, and target datasets were balanced using the SMOTE (synthetic minority over-sampling technique) algorithm, i.e., albuminuria-to-creatinine ratio (ACR) and α1-microglobulin-to-creatinine ratio (MCR). Next, Bagging, Random Forest (RF) and eXtreme Gradient Boosting (XGBoost) were employed for classification of ACR outcomes and MCR outcomes, respectively. Results 12,330 rural residents were included in this study, with 20 explanatory variables. The cases with increased ACR and increased MCR represented 1,587 (12.8%) and 1,456 (11.8%), respectively. After conducting LASSO, 14 and 15 explanatory variables remained in these two datasets, respectively. Bagging, RF, and XGBoost performed well in classification, with the AUC reaching 0.74, 0.87, 0.87, 0.89 for ACR outcomes and 0.75, 0.88, 0.89, 0.90 for MCR outcomes. The five variables contributing most to the classification of ACR outcomes and MCR outcomes constituted SBP, TG, TC, and Hcy, DBP and age, TG, SBP, Hcy and FPG, respectively. Overall, the machine learning algorithms could emerge as a warning model for CKD. Conclusion ML algorithms in conjunction with rural accessible indexes boast good performance in classification, which allows for an early warning model for CKD. This model could help achieve large-scale population screening for CKD in poverty-stricken areas and should be promoted to improve the quality of life and reduce the mortality rate.
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Affiliation(s)
- Wenzhu Song
- School of Public Health, Shanxi Medical University, Taiyuan, Shanxi, China
| | - Yanfeng Liu
- Department of Nephrology, Shanxi Provincial People’s Hospital (Fifth Hospital) of Shanxi Medical University, Taiyuan, China
| | - Lixia Qiu
- School of Public Health, Shanxi Medical University, Taiyuan, Shanxi, China
| | - Jianbo Qing
- Department of Nephrology, Shanxi Provincial People’s Hospital (Fifth Hospital) of Shanxi Medical University, Taiyuan, China
| | - Aizhong Li
- Shanxi Provincial Key Laboratory of Kidney Disease, Taiyuan, China
| | - Yan Zhao
- Department of Nephrology, Shanxi Provincial People’s Hospital (Fifth Hospital) of Shanxi Medical University, Taiyuan, China
| | - Yafeng Li
- Department of Nephrology, Shanxi Provincial People’s Hospital (Fifth Hospital) of Shanxi Medical University, Taiyuan, China,Shanxi Provincial Key Laboratory of Kidney Disease, Taiyuan, China,Core Laboratory, Shanxi Provincial People’s Hospital (Fifth Hospital) of Shanxi Medical University, Taiyuan, China,Academy of Microbial Ecology, Shanxi Medical University, Taiyuan, China
| | - Rongshan Li
- Department of Nephrology, Shanxi Provincial People’s Hospital (Fifth Hospital) of Shanxi Medical University, Taiyuan, China,Shanxi Provincial Key Laboratory of Kidney Disease, Taiyuan, China,*Correspondence: Rongshan Li,
| | - Xiaoshuang Zhou
- Department of Nephrology, Shanxi Provincial People’s Hospital (Fifth Hospital) of Shanxi Medical University, Taiyuan, China,Xiaoshuang Zhou,
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Song W, Zhou X, Duan Q, Wang Q, Li Y, Li A, Zhou W, Sun L, Qiu L, Li R, Li Y. Using random forest algorithm for glomerular and tubular injury diagnosis. Front Med (Lausanne) 2022; 9:911737. [PMID: 35966858 PMCID: PMC9366016 DOI: 10.3389/fmed.2022.911737] [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: 04/03/2022] [Accepted: 07/04/2022] [Indexed: 11/16/2022] Open
Abstract
Objectives Chronic kidney disease (CKD) is a common chronic condition with high incidence and insidious onset. Glomerular injury (GI) and tubular injury (TI) represent early manifestations of CKD and could indicate the risk of its development. In this study, we aimed to classify GI and TI using three machine learning algorithms to promote their early diagnosis and slow the progression of CKD. Methods Demographic information, physical examination, blood, and morning urine samples were first collected from 13,550 subjects in 10 counties in Shanxi province for classification of GI and TI. Besides, LASSO regression was employed for feature selection of explanatory variables, and the SMOTE (synthetic minority over-sampling technique) algorithm was used to balance target datasets, i.e., GI and TI. Afterward, Random Forest (RF), Naive Bayes (NB), and logistic regression (LR) were constructed to achieve classification of GI and TI, respectively. Results A total of 12,330 participants enrolled in this study, with 20 explanatory variables. The number of patients with GI, and TI were 1,587 (12.8%) and 1,456 (11.8%), respectively. After feature selection by LASSO, 14 and 15 explanatory variables remained in these two datasets. Besides, after SMOTE, the number of patients and normal ones were 6,165, 6,165 for GI, and 6,165, 6,164 for TI, respectively. RF outperformed NB and LR in terms of accuracy (78.14, 80.49%), sensitivity (82.00, 84.60%), specificity (74.29, 76.09%), and AUC (0.868, 0.885) for both GI and TI; the four variables contributing most to the classification of GI and TI represented SBP, DBP, sex, age and age, SBP, FPG, and GHb, respectively. Conclusion RF boasts good performance in classifying GI and TI, which allows for early auxiliary diagnosis of GI and TI, thus facilitating to help alleviate the progression of CKD, and enjoying great prospects in clinical practice.
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Affiliation(s)
- Wenzhu Song
- School of Public Health, Shanxi Medical University, Taiyuan, China
| | - Xiaoshuang Zhou
- Department of Nephrology, Shanxi Provincial People's Hospital (Fifth Hospital) of Shanxi Medical University, Taiyuan, China
| | - Qi Duan
- Shanxi Provincial Key Laboratory of Kidney Disease, Taiyuan, China
| | - Qian Wang
- Shanxi Provincial Key Laboratory of Kidney Disease, Taiyuan, China
| | - Yaheng Li
- Shanxi Provincial Key Laboratory of Kidney Disease, Taiyuan, China
| | - Aizhong Li
- Shanxi Provincial Key Laboratory of Kidney Disease, Taiyuan, China
| | - Wenjing Zhou
- School of Medical Sciences, Shanxi University of Chinese Medicine, Jinzhong, China
| | - Lin Sun
- College of Traditional Chinese Medicine and Food Engineering, Shanxi University of Chinese Medicine, Jinzhong, China
| | - Lixia Qiu
- School of Public Health, Shanxi Medical University, Taiyuan, China
| | - Rongshan Li
- Department of Nephrology, Shanxi Provincial People's Hospital (Fifth Hospital) of Shanxi Medical University, Taiyuan, China.,Shanxi Provincial Key Laboratory of Kidney Disease, Taiyuan, China
| | - Yafeng Li
- Department of Nephrology, Shanxi Provincial People's Hospital (Fifth Hospital) of Shanxi Medical University, Taiyuan, China.,Shanxi Provincial Key Laboratory of Kidney Disease, Taiyuan, China.,Core Laboratory, Shanxi Provincial People's Hospital (Fifth Hospital) of Shanxi Medical University, Taiyuan, China.,Academy of Microbial Ecology, Shanxi Medical University, Taiyuan, China
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Abstract
Fluorescence microscopy has represented a crucial technique to explore the cellular and molecular mechanisms in the field of biomedicine. However, the conventional one-photon microscopy exhibits many limitations when living samples are imaged. The new technologies, including two-photon microscopy (2PM), have considerably improved the in vivo study of pathophysiological processes, allowing the investigators to overcome the limits displayed by previous techniques. 2PM enables the real-time intravital imaging of the biological functions in different organs at cellular and subcellular resolution thanks to its improved laser penetration and less phototoxicity. The development of more sensitive detectors and long-wavelength fluorescent dyes as well as the implementation of semi-automatic software for data analysis allowed to gain insights in essential physiological functions, expanding the frontiers of cellular and molecular imaging. The future applications of 2PM are promising to push the intravital microscopy beyond the existing limits. In this review, we provide an overview of the current state-of-the-art methods of intravital microscopy, focusing on the most recent applications of 2PM in kidney physiology.
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13
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Bolder MF, Jung K, Stern M. Seasonal variations of serotonin in the visual system of an ant revealed by immunofluorescence and a machine learning approach. ROYAL SOCIETY OPEN SCIENCE 2022; 9:210932. [PMID: 35154789 PMCID: PMC8825996 DOI: 10.1098/rsos.210932] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 06/08/2021] [Accepted: 01/11/2022] [Indexed: 06/14/2023]
Abstract
Hibernation, as an adaptation to seasonal environmental changes in temperate or boreal regions, has profound effects on mammalian brains. Social insects of temperate regions hibernate as well, but despite abundant knowledge on structural and functional plasticity in insect brains, the question of how seasonal activity variations affect insect central nervous systems has not yet been thoroughly addressed. Here, we studied potential variations of serotonin-immunoreactivity in visual information processing centres in the brain of the long-lived ant species Lasius niger. Quantitative immunofluorescence analysis revealed stronger serotonergic signals in the lamina and medulla of the optic lobes of wild or active laboratory workers than in hibernating animals. Instead of statistical inference by testing, differentiability of seasonal serotonin-immunoreactivity was confirmed by a machine learning analysis using convolutional artificial neuronal networks (ANNs) with the digital immunofluorescence images as input information. Machine learning models revealed additional differences in the third visual processing centre, the lobula. We further investigated these results by gradient-weighted class activation mapping. We conclude that seasonal activity variations are represented in the ant brain, and that machine learning by ANNs can contribute to the discovery of such variations.
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Affiliation(s)
- Maximilian F. Bolder
- Institute for Animal Breeding and Genetics, University of Veterinary Medicine Hannover, Hannover, Germany
- Institute of Physiology and Cell Biology, University of Veterinary Medicine Hannover, Hannover, Germany
| | - Klaus Jung
- Institute for Animal Breeding and Genetics, University of Veterinary Medicine Hannover, Hannover, Germany
| | - Michael Stern
- Institute of Physiology and Cell Biology, University of Veterinary Medicine Hannover, Hannover, Germany
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