Evidence map›Paper›PMID 39460161›Full record

ReviewSensors (Basel, Switzerland)2024

Advances in Portable Optical Microscopy Using Cloud Technologies and Artificial Intelligence for Medical Applications.

Alessandro Molani, Francesca Pennati, Samuele Ravazzani, Andrea Scarpellini, Federica Maria Storti, Gabriele Vegetali, Chiara Paganelli, Andrea Aliverti

Abstract readReview
In one paragraph

Review in Sensors (Basel, Switzerland), 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.

0numbers the graph read from it
0cells of the map it votes in
3citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

3 citing papers in PubMed.

  1. Emerging point-of-care technologies for bacterial pathogen detection.Journal of Zhejiang University. Science. B · 2026
    Review
  2. Review
  3. Review
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

8 authors.

Alessandro MolaniDipartimento di Elettronica, Informazione e Bioingegneria, Politecnico di Milano, 20133 Milan, Italy.ORCID 0000-0002-7365-713X
Francesca PennatiDipartimento di Elettronica, Informazione e Bioingegneria, Politecnico di Milano, 20133 Milan, Italy.ORCID 0000-0001-8896-8224
Samuele RavazzaniDipartimento di Elettronica, Informazione e Bioingegneria, Politecnico di Milano, 20133 Milan, Italy.ORCID 0009-0001-2725-9413
Andrea ScarpelliniDipartimento di Elettronica, Informazione e Bioingegneria, Politecnico di Milano, 20133 Milan, Italy.ORCID 0000-0002-1750-4294
Federica Maria StortiDipartimento di Elettronica, Informazione e Bioingegneria, Politecnico di Milano, 20133 Milan, Italy.ORCID 0009-0003-2277-9406
Gabriele VegetaliDipartimento di Elettronica, Informazione e Bioingegneria, Politecnico di Milano, 20133 Milan, Italy.ORCID 0009-0007-9300-5845
Chiara PaganelliDipartimento di Elettronica, Informazione e Bioingegneria, Politecnico di Milano, 20133 Milan, Italy.ORCID 0000-0003-4787-8649
Andrea AlivertiDipartimento di Elettronica, Informazione e Bioingegneria, Politecnico di Milano, 20133 Milan, Italy.ORCID 0000-0002-2950-0231

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

The need for faster and more accessible alternatives to laboratory microscopy is driving many innovations throughout the image and data acquisition chain in the biomedical field. Benchtop microscopes are bulky, lack communications capabilities, and require trained personnel for analysis. New technologies, such as compact 3D-printed devices integrated with the Internet of Things (IoT) for data sharing and cloud computing, as well as automated image processing using deep learning algorithms, can address these limitations and enhance the conventional imaging workflow. This review reports on recent advancements in microscope miniaturization, with a focus on emerging technologies such as photoacoustic microscopy and more established approaches like smartphone-based microscopy. The potential applications of IoT in microscopy are examined in detail. Furthermore, this review discusses the evolution of image processing in microscopy, transitioning from traditional to deep learning methods that facilitate image enhancement and data interpretation. Despite numerous advancements in the field, there is a noticeable lack of studies that holistically address the entire microscopy acquisition chain. This review aims to highlight the potential of IoT and artificial intelligence (AI) in combination with portable microscopy, emphasizing the importance of a comprehensive approach to the microscopy acquisition chain, from portability to image analysis.

Indexed as

Artificial IntelligenceCloud ComputingImage Processing, Computer-AssistedMicroscopyAlgorithmsDeep LearningHumansInternet of ThingsSmartphoneartificial intelligencecloud computingdeep learningInternet of Thingslens-less microscopyphotoacoustic microscopypoint-of-care diagnosticsportable microscopysmartphone microscopy

Identifiers

PMID39460161
PMCPMC11510803

What OpenQuestion holds

Textmetadata
LicenceCC BY
Read underepoch 390

Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.