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Related Concept Videos

Hydroboration-Oxidation of Alkenes03:08

Hydroboration-Oxidation of Alkenes

In addition to the oxymercuration–demercuration method, which converts the alkenes to alcohols with Markovnikov orientation, a complementary hydroboration-oxidation method yields the anti-Markovnikov product. The hydroboration reaction, discovered in 1959 by H.C. Brown, involves the addition of a B–H bond of borane to an alkene giving an organoborane intermediate. The oxidation of this intermediate with basic hydrogen peroxide forms an alcohol.
Preparation of Alcohols via Addition Reactions02:15

Preparation of Alcohols via Addition Reactions

Overview
The acid-catalyzed addition of water to the double bond of alkenes is a large-scale industrial method used to synthesize low-molecular-weight alcohols. An acidic atmosphere is required to allow the hydrogen in the water molecule to act as an electrophile and attack the double bond in an alkene. The addition of a proton to the double bond creates a carbocation intermediate. The proton preferentially bonds to the less substituted end of the double bond to create a more stable carbocation...
Acid-Catalyzed Dehydration of Alcohols to Alkenes02:35

Acid-Catalyzed Dehydration of Alcohols to Alkenes

In a dehydration reaction, a hydroxyl group in an alcohol is eliminated along with the hydrogen from an adjacent carbon. Here, the products are an alkene and a molecule of water. Dehydration of alcohols is generally achieved by heating in the presence of an acid catalyst. While the dehydration of primary alcohols requires high temperatures and acid concentrations, secondary and tertiary alcohols can lose a water molecule under relatively mild conditions.
Oxidation of Alcohols02:37

Oxidation of Alcohols

In this lesson, the oxidation of alcohols is discussed in depth. The various reagents used for oxidation of primary and secondary alcohols are detailed, and their mechanism of action is provided.
The process of oxidation in a chemical reaction is observed in any of the three forms:
Preparation of Aldehydes and Ketones from Alcohols, Alkenes, and Alkynes01:33

Preparation of Aldehydes and Ketones from Alcohols, Alkenes, and Alkynes

Aldehydes and ketones are prepared from alcohols, alkenes, and alkynes via different reaction pathways. Alcohols are the most commonly used substrates for synthesizing aldehydes and ketones. The conversion of alcohol to aldehyde, which involves the oxidation process, depends on the class of the alcohol used and the strength of the oxidizing agent. For instance, primary alcohol will form an aldehyde when treated with a weak oxidizing agent; however, it gets over-oxidized to a carboxylic acid in...
Reactions of Aldehydes and Ketones: Baeyer–Villiger Oxidation01:22

Reactions of Aldehydes and Ketones: Baeyer–Villiger Oxidation

Baeyer–Villiger oxidation converts aldehydes to carboxylic acids and ketones to esters. The reaction uses peroxy acids or peracids and is often catalyzed by acid. The reaction is named after its pioneers, Adolf von Baeyer and Victor Villiger. The reaction is achieved by a wide range of peracids such as m-chloroperoxybenzoic acid (mCPBA), perbenzoic acid (C6H5COOOH), peracetic acid (CH3COOOH), hydrogen peroxide (H2O2), and tert-butyl hydroperoxide (t-BuOOH).
The carbonyl center is activated by...

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Excitation-Scanning Hyperspectral Imaging Microscopy to Efficiently Discriminate Fluorescence Signals
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Enhancing Oral Health Diagnostics With Hyperspectral Imaging and Computer Vision: Clinical Dataset Study.

Paul Römer1, Jean-Jacques Ponciano2, Katharina Kloster1

  • 1Department of Oral and Maxillofacial Surgery, University Medical Center of the Johannes Gutenberg University Mainz, Augustusplatz 2, Mainz, 55131, Germany, 49 1747978980.

JMIR Medical Informatics
|September 11, 2025
PubMed
Summary
This summary is machine-generated.

This study introduces a new dataset for endoscopic hyperspectral imaging (HSI) of oral tissues, enabling deep learning models to accurately differentiate tissue types. This advancement supports noninvasive diagnostics and early detection of oral diseases.

Keywords:
artificial intelligencedeep learningdentistryeHSIendoscopic HSIhyperspectral Imagingischemiamedical imaging datasetnon-invasive diagnosticsoral health diagnosticsoral mucosa perfusionoral pathologyoral squamous cell carcinomaspectral analysistissue classification

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Area of Science:

  • Medical Imaging
  • Artificial Intelligence
  • Oral Health

Background:

  • Oral diseases, including cancer, present significant diagnostic challenges due to late detection and complex tissue differentiation.
  • Endoscopic hyperspectral imaging (HSI) combined with deep learning (DL) offers a promising noninvasive approach for modern tissue diagnostics.
  • A large-scale in vivo dataset is crucial for developing and validating DL models for oral tissue analysis.

Purpose of the Study:

  • To create a comprehensive, annotated endoscopic HSI dataset of the oral cavity.
  • To develop and demonstrate automated, reliable differentiation of intraoral tissue structures using HSI and machine learning.
  • To support advancements in noninvasive diagnostics for oral health.

Main Methods:

  • Collected endoscopic HSI data from 226 participants (500-1000 nm range).
  • Annotated oral structures using RectLabel Pro software.
  • Adapted DeepLabv3 with a ResNet-50 backbone for HSI segmentation, training on 70% of the dataset.

Main Results:

  • DeepLabv3 and U-Net models achieved high F1-scores (0.857 and 0.84, respectively).
  • Models demonstrated excellent performance in segmenting mucosa (0.915), retractors (0.94), teeth (0.90), and palate (0.90).
  • Variability analysis confirmed high spectral diversity, validating the dataset's realism.

Conclusions:

  • The developed dataset and DL algorithms address a critical need in oral health imaging.
  • Accurate classification of oral tissues is now achievable, enabling noninvasive pathological analysis.
  • This work paves the way for early cancer detection and improved intraoperative diagnostics.